The National Highway Traffic Safety Administration asserted that distracted driving has become one of the leading causes of motor vehicle accidents，resulting in severe economic losses and casualties worldwide. Distracted driving refers to behaviors in which a driver’s attention shifts from the primary driving task to unrelated activities，impairing visual perception，cognitive awareness，decision-making，and operational control. Common distractions，such as calling，texting，eating，and conversing，are often covert，dynamic，and difficult to detect using a single sensing modality. Therefore，the development of a framework capable of robust perception，adaptive cognition，and real-time reasoning remains a major challenge in intelligent driving research. Embodied intelligence，a core concept of intelligent manufacturing，emphasizes deep interaction and tight coordination between intelligent agents and the physical environment. Moreover，embodied intelligence enables the emergence and evolution of cognition through continuous perception-action coupling，endowing intelligent systems with adaptive behavior generation，self-optimization，and environmental awareness. In recent years，embodied intelligence has been widely applied in bionic robotics，unmanned systems，and intelligent vehicles，driving innovation and theoretical progress in human-machine collaboration and cognitive autonomy. Building upon this theoretical foundation，this study systematically reviews the key technological pathways for distracted driving detection from the perspective of embodied cognition，focusing on frontier research trends，such as large-scale multimodal models，domain adaptation，and continual learning. This study introduces a hierarchical theoretical framework consisting of five interrelated layers that together form a closed-loop structure of perception，cognition，and execution by integrating the cognitive logic of embodied intelligence with the dynamic characteristics of complex traffic environments. The physical layer serves as the foundation，responsible for multimodal data acquisition through cameras，infrared sensors，microphones，and physiological sensors，ensuring a comprehensive coverage of driver behaviors and environmental conditions. The communication and fusion layer handles data transmission，synchronization，and multimodal fusion across heterogeneous devices，enabling reliable multi-source collaboration and adaptive information exchange under varying network and vehicular conditions. The data and knowledge layer manages and semantically interprets the collected information，constructs knowledge graphs of driver states and contextual events，and supports the integration of prior driving knowledge for enhanced reasoning. The cognition and reasoning layer applies advanced learning and inference mechanisms to interpret multimodal inputs，recognize driver states，predict behavioral intentions，and formulate context-aware strategies for distraction mitigation. Finally，the capability and application layer functions as the executive component，connecting cognitive outputs to real-world interaction，and supports human-machine communication through digital human interfaces，semantic prompts，and feedback control mechanisms for adaptive intervention and behavioral regulation. Together，these five layers establish a theoretical structure that transitions from passive perception to active cognition and embodied execution，forming an integrated mechanism for closed-loop understanding and intervention. Unlike existing detection systems that primarily focus on recognition and alerting，the proposed embodied-intelligence-based framework highlights a conceptual transformation toward human-centered adaptability，self-evolving learning，and knowledge-driven decision-making. This study provides a unified and extensible foundation that bridges multimodal perception，cognitive reasoning，and control feedback under the embodied intelligence paradigm as a theoretical contribution. The proposed framework is conceptual rather than engineering-oriented，serving as a blueprint for future empirical validation，experimental simulation，and real-world deployment. Meanwhile，the implementation of the proposed framework still faces considerable challenges. From the technical perspective，the constrained computational resources of in-vehicle terminals limit the deployment of complex deep learning models. Insufficient multimodal sensing and synchronization hinder the scalability of perception systems. Meanwhile，inference hallucination in large-scale reasoning models introduces uncertainty that threatens decision reliability and system safety. From the application perspective，ethical and legal controversies related to driver monitoring，data privacy，and algorithmic transparency remain unresolved，while high economic costs continue to impede large-scale deployment and commercialization. Future research on lightweight optimization，trustworthy and interpretable inference，distributed learning，and regulatory standardization is necessary to address these challenges. Such efforts are expected to ensure that embodied-intelligence-based distracted driving detection can evolve toward safe，efficient，transparent，and socially sustainable intelligent driving ecosystems. Overall，this study presents a systematic，forward-looking，and theoretically grounded exploration of embodied-intelligence-driven distracted driving detection. This study redefines the principles of human-machine collaboration，establishes a pathway for embodied cognitive understanding，and provides theoretical guidance for the evolution of next-generation intelligent and safe driving systems by consolidating fragmented advances in multimodal perception，driver behavior modeling，and adaptive decision-making into a unified theoretical architecture.

Embodied intelligence refers to agents that sense，reason，act，and learn through a tight “body-environment-task” feedback loop. It is widely regarded as the most promising route to general-purpose artificial intelligence （AI）. Paradoxically，although model parameters，training data，and compute budgets have grown by four orders of magnitude in only five years，the evaluation ecosystem remains fragmented and largely irreproducible. The same functional capability （e.g.，“pick-and-place”） is formalized as object-detection mean average precision in one paper，as 3D intersection over union in a second，as task success rate in a third，and as human preference score in a fourth，with absolute gaps that exceed 10%—15% among “state-of-the-art” results. Environments，random seeds，physics parameters，sensor noise models，and success criteria are rarely open-sourced，and thus，the community cannot tell whether an apparent improvement originates from algorithmic innovation，data scale，evaluation cherry picking，or simple benchmark over-fitting. This uncertainty has become a critical bottleneck for scientific progress and industrial adoption. To address the gap，we conduct a systematic survey of 100+ papers published in IEEE/CVF Conference on Computer Vision and Pattern Recognition，IEEE International Conference on Robotics and Automation，Conference on Neural Information Processing Systems，Robotics： Science and Systems，International Conference on Learning Representations，International Journal of Robotic Research，and Journal of Machine Learning Research between 2020 and 2025. Then，we present the first holistic analysis of embodied AI evaluation. We organize the landscape into a three-tier pyramid： static datasets，simulation benchmarks，and real-robot trials. Each tier trades off cost，controllability，and ecological validity. Across all tiers，we decompose evaluation space into four tightly coupled stages： perception，cognition，decision，and execution. Then，we identify fine-grained capability dimensions along with 20+ quantitative metrics. For every dimension，we analyze the following： 1） the underlying methodological assumptions，2） the boundary conditions under which the metric is valid，3） the intrinsic limitations （e.g.，sim-to-real physics mismatch，dataset bias，human-label variance），and 4） the cost-credibility curve that determines when a researcher should ascend from static to simulation，and finally to the real world.All evaluations in this study are based on a theoretical “perception-cognition-decision-execution loop” framework. In embodied AI，perception is the process by which an agent converts raw，asynchronous，and often noisy signals from heterogeneous physical sensors，such as RGB-D cameras，LiDAR，tactile skins，joint encoders，force/torque，and inertial measurement unit units，into a registered，time-aligned digital twin that supplies metric 3D geometry and semantic labels. Cognition is then built on this torrent to distill structured，memory-enabled，and generalizable world models that encode object affordances，physical common sense，and relational and causal links，grounding free-form human language into the resulting latent space. Decision-making combines these world models with an explicit task goal and searches across symbolic，kinematic，and dynamic levels to output a coherent，risk-aware，and resource-efficient plan that is expressed as a hierarchy of subgoals，trajectories，and low-level set points. Finally，execution transforms this plan into real-world mechanical energy through high-rate feedback controllers that track desired motions or forces while compensating for actuator nonlinearities，shock，thermal limits，and human safety constraints，closing the perception-cognition-decision-execution loop that defines embodied AI. In contrast with humans，whose perception-cognition-decision-execution loop is seamlessly integrated，currently available embodied agents suffer structural flaws in every stage. Consequently，evaluation must decouple the four blocks and score accuracy，error，and latency separately to pinpoint bottlenecks. Static tests，i.e.，“written exams”，run on a GPU with a few megabytes of images or point clouds in minutes，but erase dynamics and physics. Simulation，i.e.，the “job interview”，adds interactive physics in tens of seconds but still drifts away from reality. Real robot trials，i.e.，“practical tests”，cost thousands，take days to deploy and minutes per run，but expose every sensing drift，prediction error，or control delay，offering the ultimate ground truth before field deployment. Therefore，by integrating the three-tier pyramid of static test-simulation-real robot evaluation into the closed perception-cognition-decision-execution loop，this work delivers the first systematic quantification of coverage，cost，and accuracy trade-offs across more than 100 embodied AI benchmarks，establishing a reproducible，traceable，and extensible evaluation baseline that not only exposes the exact stage at which state-of-the-art systems fail，but also provides a principled road map for allocating research budgets，standardizing protocols，and certifying safety before real-world deployment，marking a pivotal transition from empirical competition to scientific measurement，and accelerating the field’s development toward trustworthy，deployable，and general-purpose embodied intelligence. The detailed static-dynamic-real embodied intelligence evaluation can be accessed at https://opencompass.org.cn/embodied-intelligence.

Embodied artificial intelligence aims to endow agents with the ability to perceive，reason，and act through continuous interaction with the physical world. In contrast with disembodied intelligence，which mostly operates on static data and symbolic tasks，embodied intelligence must handle sensorimotor coupling，environmental uncertainty，long-horizon decision-making，and real-time control in open-world settings. These requirements expose the limitations of traditional perception-planning-control pipelines. Conventional robotic systems are often built from cascaded modules for visual perception，language understanding，state estimation，task planning，and low-level control. Although such pipelines benefit from modular engineering and interpretability，they frequently suffer from error accumulation across stages，brittle generalization to novel tasks and scenes，and heavy dependence on manually designed interfaces，reward functions，and domain-specific annotations. Recent advances in large-scale foundation models have opened a new path for embodied intelligence by enabling the integration of visual grounding，language reasoning，and action generation within a shared model space. This trend has led to the rapid emergence of vision-language-action （VLA） models，which aim to translate multimodal observations and task instructions directly into executable actions or action-conditioned policies，narrowing the gap between semantic understanding and physical execution.This survey presents a systematic review of VLA models in embodied intelligence from the perspectives of historical development，technical architecture，system design，training strategies，evaluation protocols，and future trends. We first revisit the technological trajectory that made VLA possible. The evolution of large vision models and large language models has substantially improved semantic representation，multimodal alignment，and knowledge transfer，while progress in robot learning has highlighted the importance of scalable policy learning from heterogeneous data sources. Within this context，VLA models can be considered the convergence of three lines of research： multimodal foundation models for perception and reasoning，imitation and reinforcement learning for embodied decision-making，and robot action modeling for continuous control. Their significance lies not merely in adding language prompts to robot policies，but also in establishing a unified interface through which high-level task intent，environmental perception，and low-level action generation can be jointly optimized.A central objective of this survey is to clarify the internal structure of VLA systems. We analyze the key technical modules that constitute current VLA pipelines. On the perception side，VLA models rely on visual encoders to transform raw observations into compact and task-relevant representations. These observations may include RGB images，depth maps，multi-view input，temporal image sequences，or structured spatial representations，such as 3D or bird’s-eye-view features. The choice of visual representation directly influences the model’s capability to capture object semantics，spatial relations，affordances，and scene dynamics. On the language side，textual encoders provide instruction understanding，goal specification，contextual reasoning，and cross-modal alignment. Language not only describes tasks but also offers a flexible interface for compositional generalization，abstract planning，and human-robot interaction. The crucial problem of action representation lies between perception and control. Compared with visual and textual tokens，action signals are continuous，temporally dense，and strongly constrained by embodiment. Therefore，we pay special attention to action tokenization and decoding，reviewing how actions can be represented as discrete tokens，continuous vectors，chunked trajectories，latent action codes，or generative distributions. We further compare the major decoding paradigms，including direct regression，classification after discretization，autoregressive sequence modeling，diffusion-based policy generation，and flow-based generation，and then discuss their respective trade-offs in precision，expressivity，stability，and inference speed.Beyond module-level analysis，this survey proposes a system-level taxonomy that categorizes existing VLA models into three broad classes： single systems，dual-system architectures，and hierarchical systems. Single systems integrate perception，reasoning，and action generation within a unified model，and they are attractive for end-to-end deployment，parameter sharing，and simplified optimization. However，they may experience difficulties in balancing high-level deliberation with fast control. Dual-system architectures address this issue by explicitly separating slow reasoning from fast acting，frequently coupling a deliberative module for semantic planning with a lightweight controller for reactive execution. This design is particularly relevant to robotics because reasoning and motor control operate under different temporal constraints. Hierarchical systems introduce structured intermediate abstractions，such as subgoals，skills，waypoints，object-centric plans，or symbolic task graphs，to improve modularity，interpretability，and reuse across tasks. We argue that this taxonomy provides a useful perspective for understanding the design space of VLA models and analyzing how different systems trade off generalization，efficiency，and robustness.The survey also reviews the training and evaluation ecosystem that supports VLA research. Current VLA models are typically trained through multistage pipelines that involve internet-scale visual-language corpora，simulated interactive data，teleoperated demonstrations，and real robot trajectories. Pretraining equips the model with broad perceptual and linguistic priors，while post-training and task-specific adaptation align those priors with embodiment，action semantics，and control requirements. We summarize commonly used strategies，such as supervised imitation learning，behavior cloning，instruction tuning，parameter-efficient fine-tuning，model distillation，reinforcement learning augmentation，and hybrid training paradigms that combine offline and online data. We further discuss the role of simulator-based benchmarks and real-world evaluations. Simulation enables scalable and reproducible testing under controlled settings，while real robot experiments reveal embodiment gaps，latency constraints，and deployment failures that are not fully captured by simulation alone. In reviewing the literature，we emphasize that cross-paper comparisons must be interpreted with caution because reported results are often influenced by differences in robot morphology，sensor configurations，action horizons，training data quality，evaluation metrics，hardware platforms，and implementation details.Despite their remarkable progress，VLA models still face several fundamental challenges. First，real-time inference remains a major bottleneck. Many powerful multimodal models rely on autoregressive decoding or iterative generative sampling，which may be too slow for high-frequency control. Second，action learning suffers from limited robotic data，inconsistent annotation quality，and poor cross-platform transfer，especially when embodiments differ substantially. Third，environmental generalization is far from solved. Models trained on curated datasets may degrade sharply in cluttered scenes，long-horizon tasks，dynamic environments，or safety-critical settings where perception is ambiguous and recovery from failure is required. Fourth，current VLA systems still exhibit limited causal reasoning and world modeling capabilities. Many methods align observations and instructions effectively but lack deep predictive understanding of object dynamics，interaction consequences，and long-term task structure. Fifth，evaluation remains fragmented. Existing studies often emphasize task success rate while underreporting execution frequency，safety violations，intervention cost，recovery capability，and robustness under distribution shift. Finally，broader issues of trustworthiness，including interpretability，failure diagnosis，ethical risk，and human-centered deployment，have become increasingly important as VLA systems move from laboratory demonstrations toward real applications.In the future，we argue that the next stage of VLA research will be defined by several converging directions. One is the shift from passive perception-conditioned action generation to active reasoning-driven embodied intelligence，in which agents can query，explore，verify，and adapt during execution. Another is the integration of richer world models that support prediction，imagination，and planning across time and modality. Lifelong and continual learning will also be essential for agents that are operating in evolving environments. In addition，future VLA systems are likely to expand beyond fixed single-robot manipulation toward integrated mobile manipulation，long-horizon household assistance，multi-agent collaboration，and broader open-world autonomy. Achieving these objectives will require not only stronger models，but also better data curation，more principled action representations，more realistic evaluation standards，and more reliable safety mechanisms. By synthesizing the development of VLA models from architecture to deployment，this survey aims to provide a structured reference for researchers and practitioners，and to support the advancement of embodied intelligence toward more general，robust，and trustworthy interactions in the physical world. Furthermore，a comprehensively curated list of open-source VLA algorithms，datasets，and simulation benchmarks discussed in this survey is updated at https://github.com/DefaultRui/vision-language-action-models-for-embodied-AI.

With the rapid development and widespread adoption of virtual reality （VR），360° omnidirectional image （ODI） and omnidirectional video （ODV） have become important media for entertainment，education，healthcare，gaming，and immersive training. By capturing a full 360° field of view，they enable users to navigate and interact with content freely，offering a sense of presence that conventional 2D media cannot provide. However，ODI and ODV data also introduce distinctive challenges，including spherical distortion caused by projection methods，uneven viewpoint distributions arising from user-controlled perspectives，and strict real-time processing requirements needed to preserve immersion. These characteristics make saliency detection—the task of identifying regions that attract human attention—particularly important in VR environments，while also rendering traditional 2D saliency models inadequate for full-sphere attention prediction. This survey provides a comprehensive review of recent advances in saliency detection for VR 360° content，spanning traditional feature-based methods and modern deep-learning approaches，including convolutional neural network （CNN），Transformers for long-range dependency modeling，and long short-term memory （LSTM） networks for temporal modeling in video. The survey begins by outlining the fundamental principles of VR 360° content，including imaging mechanisms，geometric properties of spherical representations，and key characteristics such as boundary discontinuity and projection-induced artifacts that distinguish it from conventional 2D imagery and necessitate specialized saliency models. Existing methods are then organized into several major categories： traditional handcrafted approaches based on low-level cues such as color，contrast，and texture； advanced deep architectures designed to learn robust features under spherical constraints； multi-projection modeling strategies for equirectangular，cubemap，and spherical domains； and multimodal fusion methods that integrate visual，auditory，and depth information. Traditional methods are computationally efficient and conceptually simple，but they often struggle with spherical distortion and spatial discontinuity. For example，early superpixel-based segmentation methods are sensitive to boundary effects，and color-dictionary sparse-representation approaches do not adapt well to dynamic VR content. In contrast，deep-learning methods have transformed the field by enabling end-to-end optimization and better handling of dynamic viewpoints and multimodal information. Representative examples include CNN-based models such as SalGCN，which use spherical graph convolution to mitigate projection distortion； Transformer-based models such as SalViT360，which capture global dependencies in 360° video effectively； and LSTM-based models such as HiBayes-LSTM，which improve temporal modeling for scanpath prediction by incorporating user-behavior cues. A major part of this survey is devoted to datasets and evaluation metrics. We review prominent ODI and ODV saliency datasets，including Salient360！，HTRO，and PAVS10K，and summarize their scale，annotation types （such as fixation maps and scanpaths），and applicability to tasks including saliency prediction and salient-object detection. We also analyze commonly used metrics，including Pearson correlation coefficient （CC），normalized scanpath saliency （NSS），Kullback–Leibler divergence （KLD），F-measure，mean absolute error （MAE），S-measure，and E-measure，discussing both their usefulness and their limitations in the presence of 360°-specific distortions，especially near the poles in equirectangular projection. Comparative studies indicate that deep-learning methods，particularly those using multimodal fusion and advanced projection strategies，consistently outperform traditional approaches on key indicators such as AUC and CC. The survey further examines practical applications of saliency detection in VR，including image and video quality assessment，adaptive compression for bandwidth optimization，and virtual cinematography. In quality assessment，saliency-guided models such as SG360BIQA improve prediction accuracy by emphasizing user-attended regions and thereby aligning objective metrics more closely with subjective experience. In compression，approaches such as RoSal360 allocate more bitrate to salient regions to improve transmission efficiency in resource-constrained VR systems. In virtual cinematography，attention-driven deep reinforcement learning models simulate human gaze patterns to support smoother and more intuitive camera control. These examples highlight the broader importance of saliency detection in optimizing VR systems and improving user immersion across applications from immersive storytelling to teleconferencing. Despite this progress，substantial challenges remain，including geometric distortion in spherical representations，the need for real-time processing under strict latency constraints，and the scarcity of unified multimodal datasets that combine visual，auditory，and interaction signals. Future work should therefore explore richer multimodal integration，lightweight and unsupervised learning frameworks，and large-scale standardized benchmarks. Emerging directions such as transfer learning for cross-domain adaptation，contrastive learning for more discriminative representations，and spherical-geometry-aware data augmentation may also help improve generalization and alleviate data scarcity. In summary，this survey offers a detailed analysis of saliency detection for VR 360° content，bridging traditional heuristic methods and modern data-driven approaches. By reviewing algorithms，datasets，evaluation metrics，applications，and open challenges，it aims to support future advances toward more efficient，immersive，and user-centered VR systems. The algorithms and datasets discussed in this article have been summarized at https://github.com/jiacongyu/PVSD.

In the era of big data and artificial intelligence，image processing has emerged as a cornerstone technology that drives advancements across diverse fields，including medical diagnostics，autonomous systems，and remote sensing. The exponential growth of high-resolution image data has created unprecedented computational demands that classical computing architectures increasingly struggle to meet. As the progress under Moore’s law slows，quantum computing has emerged as a transformative paradigm； it leverages fundamental quantum properties，including superposition，entanglement，and quantum parallelism，to overcome classical limitations. This convergence has given rise to the rapidly evolving field of quantum image processing （QIP），which promises to redefine the boundaries of computational imaging capabilities. This comprehensive survey provides a systematic analysis of QIP’s remarkable evolution over the past five years，documenting its transition from a theoretical concept to an emerging practical technology. We introduce an integrated classification framework that mirrors the complete image processing pipeline——from fundamental representation to advanced applications——thus creating a coherent perspective that effectively connects quantum mechanical principles with practical implementation requirements. The foundation of this framework lies in quantum image representation models，where innovative schemes，such as novel enhanced quantum repre-sentation，flexible representation of quantum images，and generalized quantum image representation，have advanced to support arbitrary image dimensions and complex color spaces while simultaneously optimizing critical qubit utilization efficiency. Building upon this foundation，QIP techniques demonstrate increasingly sophisticated capabilities. Advanced filtering algorithms achieve exponential speedups in noise reduction tasks，and quantum Fourier and wavelet transforms enable unprecedented efficiency in image compression. Quantum parallelism facilitates rapid morphological analysis and feature extraction，substantially accelerating fundamental image processing operations. Aside from basic processing，quantum-enhanced methods for edge detection，segmentation，and feature matching also demonstrate remarkable efficiency improvements in extracting meaningful information from complex visual data. The security domain benefits substantially from quantum approaches，with innovative encryption protocols based on quantum walks and chaos theory providing theoretically unbreakable protection for sensitive visual data. Quantum watermarking techniques that utilize entanglement phenomena offer fundamentally undetectable data-hiding capabilities，creating new paradigms for digital rights management and secure communication. In machine learning applications，quantum support vector machines enhance classification accuracy for high-dimensional image data，and quantum convolutional neural networks revolutionize feature extraction through inherent quantum parallelism. Quantum generative adversarial networks enable sophisticated synthetic data generation and have notable applications in data augmentation and privacy preservation. This review emphasizes an integrated perspective that connects theoretical advances with practical implementations across multiple application domains. Our structured analytical framework clearly reveals how abstract quantum principles translate into operational capabilities while simultaneously addressing the critical challenges currently hindering widespread adoption. The noisy intermediate-scale quantum era presents substantial hardware limitations，where qubit decoherence and elevated error rates remain persistent barriers to reliable operation. The considerable theory-practice implementation gap，where algorithm performance on actual quantum devices often falls short of theoretical predictions，requires careful consideration. Moreover，scalability issues in real-world applications，where the substantial overhead of quantum data encoding and measurement can diminish computational advantages，present additional hurdles that must be overcome. Resource management considerations examine how different representation models directly affect quantum circuit complexity and execution time，providing crucial insights for practical algorithm design and implementation. Looking forward，several promising pathways can be taken. Examples include hybrid quantum-classical architectures that leverage the complementary strengths of both computational paradigms，advanced error mitigation techniques that compensate for current hardware imperfections，and quantum-inspired classical algorithms that can deliver enhanced performance on conventional hardware while preserving certain quantum advantages. The discussion extends to current hardware platforms，including superconducting qubits，trapped ions，and photonic quantum systems，to provide practical guidance for experimental research and implementation strategies. Through systematic analysis and critical evaluation，this review demonstrates quantum computing’s massive potential to achieve exponential acceleration in computational tasks，enhance security beyond classical limitations，and enable unprecedented capabilities in image analysis and understanding. Emerging applications in medical imaging，remote sensing，and security systems highlight the transformative potential of these technologies. As quantum hardware continues its steady evolution，the strategic integration of quantum approaches into conventional image processing workflows promises to redefine computational boundaries across numerous disciplines，including healthcare，environmental monitoring，industrial inspection，and artificial intelligence. The field currently stands at the threshold of a new computational paradigm，requiring continued technical innovation and strategic vision to successfully navigate the coming transition. By synthesizing recent advances，clearly identifying current limitations，and outlining promising research directions，this survey aims to accelerate the development and adoption of QIP technologies. The ultimate integration of quantum approaches with classical image processing methodologies promises to create powerful hybrid frameworks that leverage the distinctive strengths of both computational paradigms，fundamentally transforming how we extract knowledge and value from visual data in the emerging quantum era and enabling capabilities beyond the reach of either approach independently. The mentioned algorithms are summarized at https://github.com/cococococo7/QIIP.

With the accelerating transition of the global energy landscape toward renewables，wind power has been widely promoted as a vital component of clean energy. Alongside the rapid expansion of wind turbine installed capacity，the structural health of wind turbine blades （WTBs），which are among the critical power-generating components，directly influences the safety and efficiency of wind power systems. In recent years，unmanned aerial vehicles （UAVs） have been equipped with high-resolution imaging devices for blade image acquisition. By combining advanced image processing and object detection techniques，they have achieved automated，noncontact defect detection，which not only enhances detection efficiency and coverage but also reduces the risks and costs of traditional manual inspections. Therefore，this approach has become a mainstream methodology in the industry and a focal point of research. However，images captured by UAVs often suffer from poor quality （primarily manifested as noise，motion blur，and low resolution），posing critical challenges to defect detection. Moreover，defect features，such as erosion marks，stains，or manufacturing traces，frequently exhibit high visual similarity to blade surface textures and present multiscale and diverse morphological characteristics. This visual ambiguity further complicates accurate localization and classification. In addition，the lack of standardized annotation protocols and the heavy reliance on manual labeling hinder the construction of high-quality datasets. These issues collectively constrain the detection accuracy and generalization performance of deep learning-based object detection models. Existing survey literature on WTB defect detection has primarily emphasized nondestructive testing methods，with a noticeable scarcity of comprehensive reviews dedicated exclusively to image-based detection approaches. Current surveys in this field focus `on architectural advancements within deep learning models，including the integration of lightweight modules，attention mechanisms，and multiscale feature extraction techniques. Although these architectural improvements have enhanced detection accuracy，they often overlook essential aspects，including image preprocessing methods and learning strategies，both of which play a crucial role in determining overall model performance. In this review，we systematically incorporate perspectives on image preprocessing and learning strategies to analyze defect detection methods for WTBs. The main contributions of this review are as follows. 1） From the perspective of defect manifestation in WTBs，defects can be broadly categorized into internal and surface types. We systematically present and analyze the causes，formation mechanisms，and visual characteristics of these defect categories with respect to their structural properties. We also summarize the most frequently observed defect types in datasets. These datasets provide valuable data support for the design and optimization of subsequent defect detection models. 2） This review summarizes the characteristics of defects in detection tasks across various power system components，such as foreign object recognition on transmission lines and insulator defect detection. Through a comparative analysis，this review demonstrates that WTB defect detection uniquely encompasses all the aforementioned challenges and variabilities. Consequently，WTB defect detection necessitates simultaneously addressing a spectrum of challenges，including poor image quality，multiscale defect manifestations，class imbalance，lack of standardized annotation protocols，and the need for lightweight deployable models. Such a problem-driven perspective provides a foundation for inspiring future methodological advancements. 3） We conduct an in-depth investigation into the key components of deep learning-based image-driven WTB defect detection. Three critical aspects，namely，image preprocessing，model architecture，and dominant learning strategies，are presented. With respect to image preprocessing，task-driven methodologies are categorized into image quality enhancement，geometric correction，and structural feature extraction. Each of them is examined for its critical role in improving the saliency of defects and bolstering the robustness of detection models. At the architectural level，the enhancement strategies across mainstream detection frameworks，including two-stage，one-stage，and Transformer-based models，are presented. In terms of learning strategies，structured analyses of the practical effectiveness and potential of approaches，including transfer learning，ensemble learning，cascade strategies，and unsupervised/semisupervised learning，are presented. These strategies are evaluated in terms of their capability to address limited data availability，class imbalance，and label uncertainty in defect detections. 4） This review also provides an overview of publicly available datasets released in recent years，examining their data types，annotation protocols，and defect categories，thereby offering guidance for researchers in selecting appropriate research sources. A summary of commonly used evaluation metrics is provided，underscoring the necessity of image quality assessment in diverse application scenarios. 5） This review outlines critical challenges in image-based WTB defect detection，including imbalanced defect distributions，heavy reliance on manual annotations，limited model generalization under environmental variations，insufficient data modality diversity，and lack of innovation in model architectures. In light of these challenges，several promising research directions are proposed； these include the effective integration of multimodal data，creative design of model structures，and in-depth adoption of weakly or self-supervised learning strategies，with the aim of inspiring and guiding future advancements in WTB defect detection.