Road damage detection is a fundamental component of the intelligent maintenance and management of transportation infrastructure. As global transportation systems continue to modernize and road networks expand rapidly，pavements inevitably deteriorate because of a range of external and internal factors. Traditional inspection methods，including manual surveys and existing automated approaches，still face major challenges in scalability，efficiency，and accuracy，which limits their suitability for large-scale deployment. There is therefore an urgent need for a high-performance，accurate，and robust framework for road damage detection and maintenance. In practical scenarios，road-surface damage often exhibits elongated geometries with high aspect ratios，low texture，low contrast，and strong heterogeneity. These characteristics pose fundamental challenges to existing algorithms，which tend to focus on local texture or semantic cues while overlooking the intrinsic physical properties of road damage. In particular，such models fail to capture the directional feature coupling inherent in the continuous spatial distribution of cracks and the edge attenuation of blurred potholes. The lack of direction-aware features and precise positional cues weakens long-range dependency modeling and reduces responsiveness to targets with indistinct boundaries. In addition，the hierarchical convolution and downsampling operations in convolutional neural networks （CNNs） progressively degrade cross-scale features，causing the loss of critical detail from high-resolution shallow feature maps.

To address these limitations，this study proposes a novel multi-stage spatial-aware detection network （MSANet） built on the YOLOv11 framework. The design follows a progressive feature enhancement-fusion-optimization strategy tailored to the physical characteristics of elongated cracks and blurred-edge potholes. In the direction-sensitive feature enhancement stage，a hybrid Manhattan self-attention and coordinate attention （MCA） mechanism is introduced. Manhattan self-attention models the spatial continuity of crack axes，while coordinate attention improves the localization of blurred-edge defects. Together，these components create a dual-driven enhancement scheme that strengthens both directionality and detail representation，enabling the model to capture long-range spatial dependencies more precisely. To mitigate feature degradation caused by repeated downsampling，a cross-scale feature concat （CFC） module is incorporated into the neck. By cascading feature maps across resolutions，the CFC module balances semantic completeness at larger scales with detail richness at smaller scales，thereby compensating for the loss of fine-grained information. Finally，in the detection head，a coordinate-aware convolution kernel （C3K2_CAC） is embedded within the C3K2 block. This module jointly optimizes spatial position and channel response through direction-aware spatial attention and dynamic channel reweighting，producing anisotropic representation enhancement in high-dimensional feature space. While maintaining computational efficiency，it significantly improves edge clarity，detection robustness，and global discriminative capability. During training，experiments were conducted on a standardized platform using Ubuntu 20.04 and PyTorch. YOLOv11s served as the baseline for comparative and ablation studies. Across all experiments，the same hyperparameters were used： a batch size of 32，a learning rate decayed from 0.01 to 0.000 1，an image resolution of 640 × 640，stochastic gradient descent （SGD） as the optimizer，and a training schedule of 300 epochs. All other settings followed the default network configuration to ensure fairness and reproducibility.

Experiments were conducted on the publicly available RDD2022 Chinese subset，which contains multiple road-damage categories，including longitudinal cracks （D10），transverse cracks （D10），alligator cracks （D20），potholes （D40），and repaired regions. Performance was evaluated using seven standard metrics： precision （P），recall （R），mAP@0.5，mAP@0.5：0.95，F1-score，frames per second （FPS），and GFLOPs. The results show that MSANet significantly outperforms existing state-of-the-art detectors. Compared with mainstream models，MSANet achieves an mAP@0.5 of 0.877 and a comprehensive performance score of 0.845 （Recall： 0.811，Precision： 0.883，F1-score： 0.845），surpassing YOLOv8s （0.824） and RT-DETR （0.801）. Its mAP@0.5：0.95 improves by 4.8% over the baseline，confirming the robustness of the proposed design. In fine-grained damage detection，MSANet further demonstrates strong adaptability，achieving a 4.15% increase in mAP@0.5 （to 0.878），a 3.18% increase in mAP@0.5：0.95 （to 0.649），and a 2.69% gain in F1-score （to 0.840）. Importantly，the model maintains a high inference speed of 142 frame/s，representing only a 6% reduction from the baseline while still substantially outperforming real-time detectors such as RT-DETR. This balance between accuracy and efficiency supports practical deployment in real engineering settings，where rapid decision-making and reliable predictions are both essential. Ablation studies further validate the contribution of each proposed module. The MCA module improves performance without significant computational overhead. The CFC module effectively fuses low-level detail with high-level semantics across network stages，introducing cross-scale contextual information that yields stable gains of 0.5% in mAP@0.5 and 2.3% in mAP@0.5：0.95. The C3K2_CAC module introduces only a modest increase in computational cost while enhancing the spatial awareness of neck features and supplying the detection head with more discriminative representations. Detection heatmaps further confirm that MSANet provides better focus on target regions，stronger suppression of background noise and irrelevant activations，and more complete coverage of damage areas. To assess generalization and deployment potential，experiments were also performed on additional public datasets and field-collected road images，where MSANet achieved consistently promising results，demonstrating strong robustness and scalability across diverse environments.

Efficient and accurate road damage detection is crucial for the intelligent maintenance of transportation infrastructure. By combining spatial-awareness enhancement with detail preservation，the proposed MSANet framework addresses long-standing challenges in directional modeling and fine-grained feature retention. The model achieves substantial improvements in detection accuracy while preserving real-time inference capability，offering both theoretical novelty and engineering practicality. MSANet therefore represents a promising solution for intelligent road infrastructure management and provides useful insights for the design of next-generation detection frameworks that seek to balance efficiency，accuracy，and scalability. With its strong adaptability to both benchmark datasets and real-world imagery，MSANet also lays a foundation for future integration into large-scale intelligent transportation systems and smart-city applications. The code for this paper will be available at https://www.scidb.cn/s/RZBnIz.