Benchmarking YOLOv8 for Optimal Crack Detection in Civil Infrastructure

TL;DR

This paper evaluates YOLOv8's effectiveness for real-time crack detection in civil infrastructure, demonstrating its superior speed and accuracy through extensive hyperparameter tuning and model scaling.

Contribution

It provides a comprehensive benchmarking of YOLOv8 across multiple scales and optimizers, highlighting its potential for infrastructure monitoring applications.

Findings

YOLOv8 with SGD outperforms other models in accuracy and speed.

Hyperparameter optimization significantly improves detection performance.

YOLOv8 sets a new benchmark for real-time crack detection in civil infrastructure.

Abstract

Ensuring the structural integrity and safety of bridges is crucial for the reliability of transportation networks and public safety. Traditional crack detection methods are increasingly being supplemented or replaced by advanced artificial intelligence (AI) techniques. However, most of the models rely on two-stage target detection algorithms, which pose concerns for real-time applications due to their lower speed. While models such as YOLO (You Only Look Once) have emerged as transformative tools due to their remarkable speed and accuracy. However, the potential of the latest YOLOv8 framework in this domain remains underexplored. This study bridges that gap by rigorously evaluating YOLOv8's performance across five model scales (nano, small, medium, large, and extra-large) using a high-quality Roboflow dataset. A comprehensive hyperparameter optimization was performed, testing six state-of-the-art optimizers-Stochastic Gradient Descent, Adaptive Moment Estimation, Adam with Decoupled Weight Decay, Root Mean Square Propagation, Rectified Adam, and Nesterov-accelerated Adam. Results revealed that YOLOv8, optimized with Stochastic Gradient Descent, delivered exceptional accuracy and speed, setting a new benchmark for real-time crack detection. Beyond its immediate application, this research positions YOLOv8 as a foundational approach for integrating advanced computer vision techniques into infrastructure monitoring. By enabling more reliable and proactive maintenance of aging bridge networks, this work paves the way for safer, more efficient transportation systems worldwide.