Autonomous surface crack identification of concrete structures based on an improved one-stage object detection algorithm

oncrete is a widely used material in the infrastructure system. However, this material is susceptible to several factors that eventually create concrete cracks. Thus, accurately identifying the cracks' size and location in concrete structures is crucial for structural safety evaluation. In this research, an improved YOLOv4 network adopting the pruning technique and the EvoNorm-S0 structure was put forward to better identify concrete cracks from many misleading targets. The pruning technique is used to light-weight the network structure, and the EvoNorm-S0 could improve the detection accuracy. The results indicate that compared with the original YOLOv4, the mAP50 of the improved network is increased from 91.69% to 92.54% when both models are trained for 100 epochs, and the 1-Batch inference time is reduced by 15.9%. Moreover, the weight of the proposed network is just 54.9% of the original ones. The proposed network was also compared with three other leading algorithms in this field (i.e., SSD300, YOLOv3, and YOLO X-L) using the same dataset. The results show that the proposed network can not only correctly classify the largest number of objects with a fast calculation speed, but also has the highest mAP50. Thus, this proposed network exhibits several advantages for detecting concrete cracks and is a desirable tool for practical engineering.