Automated Pixel-Level Detection of Expansion Joints on Asphalt Pavement Using a Deep-Learning-Based Approach

Abstract

Pixel-level detection of expansion joints on complex pavements is significant for traffic safety and the structural integrity of highway bridges. This paper proposed an improved HRNet-OCR, named as expansion joints segmentation network (EJSNet), for automated pixel-level detection of the expansion joints on asphalt pavement. Different from the high-resolution network (HRNet), the proposed EJSNet modifies the residual structure of the first stage by conducting a Conv. + BN + ReLU (convolution + batch normalization + rectified linear unit) operation for each shortcut connection, which can avoid the network degradation. The feature selection module (FSM) and receptive field block (RFB) module are incorporated into the proposed EJSNet model to learn and extract the contexts at different resolution levels for enhanced latent representations. The convolutional block attention module (CBAM) is introduced to enhance the adaptive feature refinement of the network. Moreover, the shared multilayer perceptron (MLP) architecture of the channel attention module (CAM) is also modified in this paper. Experimental results demonstrate that the F-measure and intersection-over-union (IOU) attained by the proposed EJSNet model on 500 testing image sets are 95.14% and 0.9036, respectively. Compared with four state-of-the-art models for semantic segmentation (i.e., SegNet, DeepLabv3+, dual attention network (DANet), and HRNet-OCR), the proposed EJSNet model can yield higher detection accuracy on both private and public datasets.