Comparative Analysis of Object Detection Algorithms for Surface Defect Detection

TL;DR

This paper compares six object detection algorithms for surface defect detection, finding YOLOv11 to outperform others in accuracy and speed on the NEU-DET dataset, highlighting its suitability for industrial quality control.

Contribution

It provides a comprehensive performance comparison of six prominent object detection algorithms specifically for surface defect detection, emphasizing YOLOv11's superior capabilities.

Findings

YOLOv11 achieved 70% higher accuracy than other models.

YOLOv11 was faster and more efficient in defect detection.

YOLOv11's architecture improvements enhanced localization precision.

Abstract

This article compares the performance of six prominent object detection algorithms, YOLOv11, RetinaNet, Fast R-CNN, YOLOv8, RT-DETR, and DETR, on the NEU-DET surface defect detection dataset, comprising images representing various metal surface defects, a crucial application in industrial quality control. Each model's performance was assessed regarding detection accuracy, speed, and robustness across different defect types such as scratches, inclusions, and rolled-in scales. YOLOv11, a state-of-the-art real-time object detection algorithm, demonstrated superior performance compared to the other methods, achieving a remarkable 70% higher accuracy on average. This improvement can be attributed to YOLOv11s enhanced feature extraction capabilities and ability to process the entire image in a single forward pass, making it faster and more efficient in detecting minor surface defects. Additionally, YOLOv11's architecture optimizations, such as improved anchor box generation and deeper convolutional layers, contributed to more precise localization of defects. In conclusion, YOLOv11's outstanding performance in accuracy and speed solidifies its position as the most effective model for surface defect detection on the NEU dataset, surpassing competing algorithms by a substantial margin.