YOLOX-RDD: A Method of Anchor-Free Road Damage Detection for Front-View Images

Abstract

Road damage detection (RDD) based on front-view images of roads is more in line with practical application scenarios and is suitable for automatic road damage detection systems. The road damage objects in the front-view images have the characteristics of complex background, multi-scale and large aspect ratio, which greatly increase the difficulty of detection. We propose an anchor-free road damage detection model YOLOX-RDD for front-view images. YOLOX is used as the basic network and three optimization strategies are implemented according to the characteristics of road damage objects. The refined switchable atrous convolution (RSAC) is used to adaptively adjust the receptive field according to the size of the object, which can satisfy the requirements of the detection of the damages of multi-scale and large aspect ratio. For unobvious road damage detection in complex background, four feature enhancement attention (FEA) modules are added to the network to extract more salient information and enhance the fusion effect. Two-level adaptive spatial feature fusion (ASFF) is performed by fusing dark2 with the three output feature maps of neck respectively, and the optimal fusion weights are learned through training to further improve the detection capability of multi-scale objects. The experiments on CNRDD, RDD2020 and USRDD datasets demonstrate the effectiveness and high generalization of our method. Compared with the baseline model, the mAP@0.5 can be improved by up to 2.78%, and F1-Score can be improved by up to 2.55%. The FPS can reach up to 90, achieving a balance between detection accuracy and speed.