## 2 Related Work
### Road Damage Detection  
In recent years, road damage detection methods have experienced rapid development from traditional image processing techniques to deep learning methods. YOLO-based detection methods include YOLO-DC proposed by [PERSON], [PERSON] et al. [15], YOLOv7 BiFPN-G proposed by [PERSON], [PERSON] et al. [16], MED-YOLOv8s proposed by [PERSON], [PERSON] et al. [18], MN-YOLOv5 proposed by [PERSON], [PERSON] et al. [19], the optimized YOLOv8 model proposed by [PERSON], [PERSON] et al. [13], the improved YOLOv8 model proposed by [PERSON], [PERSON] et al. [20], YOLOv5s-DSG proposed by [PERSON] et al. [21], FG_YOLO proposed by [PERSON], [PERSON] et al. [22], SCD proposed by [PERSON], [PERSON] et al. [14], SCD -YOLO, YOLO9 tr proposed by [PERSON], [PERSON] et al. [23], YOLO-LRDD proposed by [PERSON], [PERSON] et al. [24], and YOLOv8-PD proposed by [PERSON], [PERSON] et al. [25]. These methods significantly improve the accuracy and efficiency of road damage detection by improving the YOLO architecture, combining the attention mechanism, the lightweight design, and the feature fusion technique. In terms of accuracy and efficiency of road damage detection, however, many YOLO-based methods [49] improve the detection performance while the model complexity and computation remain high, limiting their application on resource-constrained devices. In addition, significant progress has been made in road damage detection methods based on traditional CNNs. [PERSON], [PERSON] et al. [9] proposed CrdNet, a CNN-based cascade detection network, [PERSON], [PERSON] et al. [7] proposed an automatic image processing algorithm based on the pixel density of cracks, and [PERSON], [PERSON] et al. [10] proposed a detection algorithm based on deformable SSDs [50]. [PERSON], [PERSON] et al. [8] proposed an intelligent integrated detection method combining CNN and Extreme Learning Machine [21], [PERSON], [PERSON] et al. [11] proposed a CNN method without feature extraction, and [PERSON], [PERSON] et al. [12; 17] proposed LSF-RDD and LMFE-RDD, respectively. These methods improve the accuracy and robustness of detection by improving the network structure, introducing multi-scale feature fusion and innovative loss functions. However, some of the methods still have limitations in dealing with complex backgrounds and diverse damage types, and the detection speed and real-time performance need to be further improved. In addition, lightweight and efficient road damage detection models have also become a research hotspot. Many scholars are committed to reducing the model parameters and computation volume while ensuring the detection performance so as to achieve real-time detection on edge devices. For example, [PERSON], [PERSON] et al. [15] and [PERSON], [PERSON] et al. [16] achieved efficient road crack detection by designing a lightweight YOLO model; MED-YOLOv8s proposed by [PERSON], [PERSON] et al. [18] and YOLO-LRDD proposed by [PERSON], [PERSON] et al. [24] achieved efficient road crack detection by introducing a lightweight attention mechanism and optimizing the network structure, which substantially reduces the model complexity; YOLO9 tr proposed by [PERSON], [PERSON] et al. [23] realizes real-time pavement damage detection by using an efficient feature aggregation network [22] and attention mechanism. These lightweight methods significantly improve the inference speed of the model and the ease of deployment while ensuring higher detection accuracy, but the detection stability and generalization ability in extremely complex environments still need to be further optimized. Although existing road damage detection methods have made significant progress in terms of detection accuracy and efficiency, they still have many shortcomings. First, while many YOLO-based models improve the detection performance, the model complexity and computation amount are still high, which limits their wide application on resource-constrained devices.