Object detection model design for tiny road surface damage

Road surface damage detection is crucial in highway maintenance and traffic safety maintenance. However, existing detection methods generally suffer from insufficient generalization capability, poor detection of tiny damage, and difficulty balancing detection accuracy and computational cost. This study proposes a novel road surface damage object detection model (RSDD) to address these challenges. Firstly, a backbone applied to road surface damage feature extraction is designed to solve the problems of feature loss and insufficient extraction of tiny damage during feature extraction. Second, to achieve efficient feature fusion, multiple attention is introduced to optimize features at different stages. Then, a bi-directional feature fusion path is proposed to realize the information exchange between features of different stages, and an enhanced feature pyramid is constructed. Finally, a multi-scale decoupled detection head is adopted to realize the accurate detection of different sizes of damage. Additionally, this study built a road dataset containing rich samples of tiny damage. Extensive comparative experiments are conducted on the collected dataset and a public dataset to validate the generalization performance of RSDD. The experimental results show that RSDD has significant advantages in tiny damage detection while having excellent trade-offs in terms of accuracy, scale, and speed. Specifically, the model achieves 70.8% and 61.2% mAP₅₀ on the two datasets with an inference latency of only 4.5 ms under the condition that the number of parameters is 16.5 M. Compared with YOLOv8s, which has a similar number of parameters, RSDD achieves 5.5% and 3.3% improvement in the detection accuracy, respectively, and speeds up the inference by 0.6 ms.