Multiscale Attention Networks for Pavement Defect Detection

Abstract

Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning (DL)-based convolution neural networks (CNNs) has shown competitive performance in image detection and classification. To detect pavement defects automatically and improve effects, a multiscale mobile attention-based network, which we termed MANet, is proposed to perform the detection of pavement defects. The architecture of the encoder–decoder is used in MANet, where the encoder adopts the MobileNet as the backbone network to extract pavement defect features. Instead of the original $3\,\, \times 3$ convolution, the multiscale convolution kernels are used in depthwise separable convolution (DSConv) layers of the network. Furthermore, the hybrid attention mechanism is separately incorporated into the encoder and decoder modules to infer the significance of spatial points and interchannel relationship features for the input intermediate feature maps. The proposed approach achieves state-of-the-art performance on two publicly available benchmark datasets, i.e., the Crack500 (500 crack images with $2000\,\, \times 1500$ pixels) and CFD (118 crack images with $480\,\, \times 320$ pixels) datasets. The mean intersection over union (MIoU) of the proposed approach on these two datasets reaches 0.7219 and 0.7788, respectively. Ablation experiments show that the multiscale convolution and hybrid attention modules can effectively help the model extract high-level feature representations and generate more accurate pavement crack segmentation results. We further test the model on locally collected pavement crack images (131 images with $1024\,\, \times 768$ pixels) and it achieves a satisfactory result. The proposed approach realizes the MIoU of 0.6514 on the local dataset and outperforms other compared baseline methods. Experimental findings demonstrate the validity and feasibility of the proposed approach and it provides a viable solution for pavement crack detection in practical application scenarios. Our code is available at https://github.com/xtu502/pavement-defects .