Steel Surface Defect Detection Using Improved Deep Learning Algorithm: ECA-SimSPPF-SIoU-Yolov5

Abstract

Steel surface defect detection is an indispensable part of industrial production and processing processes. It helps to reduce production costs, ensure product quality, improve production safety and compliance, and maintain sustainability and competitiveness. To address the low detection accuracy of traditional methods, this paper developed and investigated an improved algorithm based on YOLO for steel surface defect detection: ECA(Efficient Channel Attention) -SimSPPF (Simplified Spatial Pooling - Fast) -SIoU (Scylla Intersection over Union) -Yolov5. First, deformable convolutions were used to replace some conventional convolutions in the model, which expanded the receptive field and improved detection accuracy. Additionally, Efficient Channel Attention was integrated into the model to improve the weight of important information. Then, the SimSPPF was employed in place of the SPP module in the model, reducing computational complexity. Finally, the SIoU loss function was utilized to handle bounding box regression more effectively. The paper conducted different ablation experiments, and the improved ECA-SimSPPF-SIoU-Yolov5 algorithm demonstrated superior detection performance. Using the NEU-DET dataset, the mAP reached 78.8%, which was a 7.1% improvement higher than the original model, while the Recall reached 76.4% and improved by 3.7% compared to the original model. The improved model showed significant improvements in terms of mAP and Recall. Furthermore, the paper conducted multiple comparative experiments, comparing the model with other attention mechanisms and loss functions. The results demonstrated that the improved ECA-SimSPPF-SIoU-Yolov5 algorithm achieved good detection results in terms of mAP and Recall. In the third comparative experiment, the model was compared with YOLOv5 model with different network depths and the latest Yolov8 model, and the improved model also achieved good detection accuracy.

Keywords

• Defect detection
• deep learning
• object detection
• convolutional neural network
• defect classification