Improved YOLOv5-based multi-crack detection in concrete wall surfaces

Abstract

In engineering and infrastructure management, it is essential to ensure the safety and longevity of structures. This can be carried out by properly detecting and monitoring surface cracks. From this lens, this paper presents a novel method for identifying surface cracks using the YOLOv5 (You Only Look Once) deep learning architecture to provide real-time object detection capabilities. First, the YOLOv5 was used with predefined hyperparameters to evaluate its pre-trained knowledge in concrete cracking. Then, the YOLOv5 architecture was fine-tuned to accommodate the specific characteristics of surface cracks within concrete structural components. Finally, the model's backbone was replaced with ResNet-50, and its performance was examined. The experiments in this study involved a diverse dataset of surface crack images and aimed to compare the performance of the three approaches in terms of Precision, Recall, and mean Average Precision metrics. Our findings indicate that YOLOv5-based approaches possess good surface crack identification, with the backbone replacement approach demonstrating the potential for improved adaptability to various structural environments. By combining the capabilities of YOLOv5 and the training strategies, the approach enhances the accuracy and reliability of the surface crack detection systems, resulting in the overall safety and durability of critical infrastructure.