YOLOv8-Based Object Detection Model for Automated Bridge Crack Detection

Abstract

Bridge infrastructure maintenance is crucial for public safety and efficient transportation networks. Deep-learning-based object detection models offer promising solutions for automated bridge crack detection, reducing the reliance on traditional visual inspection methods which are often time-consuming, subjective, and potentially hazardous. This study conducts a comprehensive evaluation of two state-of-the-art object detection models, You Only Look Once (YOLO) v8 and YOLOv5, in the context of bridge crack detection. Our experimental evaluation involves training and testing both models on a dataset comprising images of bridge surfaces with varying degrees of crack severity. We compare their performance in detection accuracy, inference speed, and model size. The results demonstrate that, while both YOLOv8 and YOLOv5 exhibit strong performance in detecting bridge cracks, there are discernible differences in their detection accuracy and computational efficiency. YOLOv8, particularly the nano model, demonstrates superior accuracy in identifying crack patterns. It achieved an impressive mean average precision of 0.602 at 0.5 intersection over union while maintaining a remarkably low processing time of just 1.1 ms per image and a compact model size of 5.96 MB. These achievements make it better suited to meet the lightweight and accuracy requirements of object detection using smart device applications.