###### Abstract  
Existing methods for detecting road damage mainly depend on manual inspections or sensor-equipped vehicles, which are inefficient, have limited coverage, and are susceptible to errors and delays. These traditional methods also struggle with detecting minor damage, such as small cracks and initial potholes, making real-time road monitoring challenging. To address these issues and improve the performance for real-time road damage detection using Street View Image Data (SVRDD), this study propose DAPONet, a new deep learning model. DAPONet proposes three main innovations: (1) a dual attention mechanism that combines global context and local attention, (2) a multi-scale partial overparameterization module (CPDA), and (3) an efficient downsampling module (MCD). Experimental results on the SVRDD public dataset show that DAPONet reaches a mAP50 of 70.1%, surpassing YOLOv10n (an optimized version of YOLO) by 10.4%, while reducing the model's size to 1.6 M parameters and cutting FLOPs to 1.7 G, resulting in a 41% and 80% decrease, respectively. Furthermore, the model's mAP50-95 of 33.4% on the MS COCO2017 dataset demonstrates its superior performance, with a 0.8% improvement over EfficientDet-D1, while reducing parameters and FLOPs by 74%. SVRDD; road damage detection; real-time object detection; global and local attention; partially overparameterized 2020  
In this context, road damage detection techniques based on artificial intelligence have significant advantages [4; 5; 6]. These techniques can detect road damage more quickly and accurately, reduce manual intervention, and improve the efficiency of road maintenance, so they have attracted the attention of a large number of researchers. Currently, the existing AI methods for road damage detection can be roughly divided into four categories: methods based on traditional image processing [7] utilize traditional image processing techniques such as image preprocessing, edge detection, feature extraction, etc., to analyze the road image and damage recognition. This type of method relies on hand-designed features, and is more sensitive to image quality and environmental conditions. Techniques driven by machine learning [8] perform classification and detection by extracting manual features and using machine learning algorithms (e.g., support vector machines, random forests, etc.). Such methods offer greater flexibility in feature selection and algorithm optimization, but usually require large amounts of labeled data for training. Deep learning-based single-stage and two-stage detection models [9; 10; 11; 12; 13; 14] include single-stage detection models, such as the You Only Look Once (YOLO) series, are classic examples of single-stage object detection. By integrating object localization and classification into a single network, they complete detection in just one forward propagation, making them highly efficient for real-time tasks. And two-stage detection models (e.g., Faster R-CNN), which are able to automatically learn features and perform end-to-end damage detection and classification. Deep learning methods perform well in complex scenarios with high detection accuracy and robustness, as well as hybrid models that incorporate lightweighting and robustness enhancement [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25], which aim to reduce model complexity and computational resource requirements while maintaining detection accuracy. Such models are suitable for resource-constrained devices and real-time detection applications, enhancing the feasibility and efficiency of practical deployment. The application of these methods greatly improves the road damage detection and classification capabilities in diverse scenarios. Although these methods have made significant progress in detection accuracy, model robustness, and computational efficiency, several challenges remain. For instance, processing complex contextual information and maintaining real-time detection speeds while ensuring high accuracy continue to be significant obstacles.