LMFE-RDD: a road damage detector with a lightweight multi-feature extraction network

Abstract

Road damage detection using computer vision and deep learning to automatically identify all kinds of road damage is an efficient application in object detection, which can significantly improve the efficiency of road maintenance planning and repair work and ensure road safety. However, due to the complexity of target recognition, the existing road damage detection models usually carry a large number of parameters and a large amount of computation, resulting in a slow inference speed, which limits the actual deployment of the model on the equipment with limited computing resources to a certain extent. In this study, we propose a road damage detector named LMFE-RDD for balancing speed and accuracy, which constructs a Lightweight Multi-Feature Extraction Network (LMFE-Net) as the backbone network and an Efficient Semantic Fusion Network (ESF-Net) for multi-scale feature fusion. First, as the backbone feature extraction network, LMFE-Net inputs road damage images to obtain three different scale feature maps. Second, ESF-Net fuses these three feature graphs and outputs three fusion features. Finally, the detection head is sent for target identification and positioning, and the final result is obtained. In addition, we use WDB loss, a multi-task loss function with a non-monotonic dynamic focusing mechanism, to pay more attention to bounding box regression losses. The experimental results show that the proposed LMFE-RDD model has competitive accuracy while ensuring speed. In the Multi-Perspective Road Damage Dataset, combining the data from all perspectives, LMFE-RDD achieves the detection speed of 51.0 FPS and 64.2% mAP@0.5, but the parameters are only 13.5 M.