A lightweight network for crack detection with lightweight feature extraction and multi-scale feature fusion

Abstract

Automated pavement crack detection is of great significance to the efficiency of road maintenance. Benefit from the development of convolutional neural networks (CNNs), automatic crack detection has been gradually developed. While the convolutional neural network improves the accuracy of crack detection, the calculation amount and parameter amount of the model are greatly enhanced. This limits the application of crack detection methods on some mobile devices. In order to solve this problem, We propose a lightweight network which uses a lightweight feature extraction module combined with an attention mechanism to extract features from crack images. We use the multi-scale feature fusion module to achieve the fusion of different scale crack features. Through these modules, We built a network with nearly 0.94M parameters and only 6G FLOPs while achieving comparable crack detection performance. Extensive experiments on DeepCrack dataset show that the proposed network is superior to other comparison networks.