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.