Multi-Scale Semantic Map Distillation for Lightweight Pavement Crack Detection

Timely and accurate pavement crack detection plays a crucial role in urban transportation and pavement management. Recently, deep learning-based pavement crack detection approaches have witnessed significant advancements. However, high-performance networks heavily rely on complex network structures and massive model parameters, making their application in practical scenarios challenging. To achieve efficient yet accurate pavement crack detection, this paper presents a novel Lightweight Pavement Crack Detection based on Multi-Scale Semantic Map Distillation (LPCD-MSMD). Specifically, we improve the U-Net by designing a novel Cascaded U-Net (C-UNet) structure and incorporating additional branches to capture more subtle features. The proposed C-UNet is optimized by reducing the number of convolutional layers and compressing the channel dimensions, benefiting to a more lightweight model while maintaining comparable performance. Additionally, we utilize the original C-UNet as the teacher network and the simplified network as the student network for a lightweight pavement crack detection. Through the proposed multi-scale semantic map distillation strategy, the student network can acquire multi-scale output knowledge from the cascaded output of the teacher network, benefiting significantly improved performance of the CU-Net(s). Thorough experimental results demonstrate that the elaborately designed student network possesses a remarkably small parameter size of only 0.54 MB. Moreover, the performance evaluated on the Crack500 dataset and the GAPS384 dataset indicate the superiority of proposed method over several typical deep learning-based approaches including SegNet, FCN, BiSeNet, and U-Net. In particular, our method outperforms the second-best method by 0.09 and 0.124 in terms of F1 score evaluated on the Crack500 dataset and the GAPS384 dataset, respectively.