Comparative Analysis of Object Detection Algorithms for Surface Defect Detection

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 regar ding 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 YOLOv11's enhanced feature extraction capabilities and ability to process the entire image in a single forward pass, making it faster and more efficient in detecting smaller surface defects. Additionally, YOLOv11's architecture optimizations, such as improved anchor box generation and deeper convolutional layers, contributed to more precise localization of defects.