YOLOv11在金属表面缺陷检测中精度和速度均显著优于其他模型。
Comparative Analysis of Object Detection Algorithms for Surface Defect Detection
- 采用单次前向传播和优化特征提取，实现高效检测。
- 平均精度比其他模型高70%，对细微缺陷识别更准。
- 适合工业质检场景，尤其需实时高精度的缺陷识别。
本文对比了六种主流目标检测算法——YOLOv11、RetinaNet、Fast R-CNN、YOLOv8、RT-DETR 和 DETR——在 NEU-DET 表面缺陷检测数据集上的表现。该数据集包含多种金属表面缺陷图像，是工业质量控制中的关键应用。评估指标包括检测准确率、速度与对不同缺陷类型（如划痕、夹杂物、轧入氧化皮）的鲁棒性。结果显示，YOLOv11 作为前沿实时检测算法，在平均准确率上相比其他方法提升70%。这一优势源于其更强的特征提取能力、单次前向传播处理全图的架构设计，以及改进的锚框生成与更深的卷积层结构，从而实现更精确的缺陷定位。结论表明，YOLOv11 在 NEU-DET 数据集上表现出色，显著超越其他算法。
原文摘要 · Abstract (English)
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.
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