Autonomous surface crack identification for concrete structures based on the you only look once version 5 algorithm

Abstract

Failure to repair roads in a timely manner may shorten their life and even cause traffic accidents. Thus, accurate crack detection and reasonable classification are crucial for road safety evaluation. In this study, an improved network model based on the You Only Look Once version 5 algorithm is presented, with three additional modules: The first module improves the data processing speed by replacing the C3 module in the original network with a lightweight network model. The second module was used to lighten network weight by reusing a simple convolution structure to equivalently represent the calculation of a convolution layer as a weighted sum of several small convolution blocks. And the third module is used to improve the detection accuracy by removing the upsampling and performing three-way splicing. The proposed model can detect different types of cracks, and an extensive ablation study is reported based on various combinations of the proposed modules. Based on training on a database of 5484 images, the results show that the improved network proposed in this study can effectively identify pavement cracks. Compared with the original network, mean Average Precision is increased by 5.98%, the inference time is reduced by 4.82%, and the model weight is decreased by 17.36%. Additionally, to comply with engineering practice, comparative experiments were conducted on the pre-rotated dataset. The results showed that compared with You Only Look Once version 8, the improved algorithm improved accuracy, recall, average accuracy, and F1 score by 3.28%, 8.46%, 3.79% and 5.89%, respectively. This study can serve as an important reference for the development of crack detection methods.