SC-CrackSeg: A Real-time Shared Feature Pyramid Network for Crack Detection and Segmentation

Detecting cracks is important in a number of civil engineering applications. Recent advances in computer vision have enabled automatic crack detection and fine-grained segmentation using deep learning. However, the models used in previous work are often large and are therefore mainly suitable for offline structure monitoring where images taken from a site are analysed later by a powerful computer. In this work, we address the segmentation problem in an online setting, which permits the use of mobile inspection devices such as drones with limited computing power to monitor structures independently in real-time. We propose SC-CrackSeg, which has a very small number of parameters and can provide very high segmentation accuracy. Our main contribution is a multi-branch information-sharing architecture that efficiently manages global perspective while maintaining the fine and high-resolution details key in crack detection. SC-CrackSeg extends a previously proposed model but optimized specifically for this application: reduction to a single-input, a more efficient context mining module, and a simpler feature fusion module. We evaluate SC-CrackSeg on large crack detection data sets and the results show that our proposed model is competitive against the existing methods.