Strip steel surface defect detection based on lightweight YOLOv5 - Zhang, Yongping; Shen, Sijie; Xu, Sen (Frontiers in Neurorobotics, 2023)
We propose a light-weight strip steel surface defect identification algorithm based on YOLOv5 to address the issues of low accuracy and sluggish speed in strip steel surface defect detection. Firstly, we introduce the efficient lightweight convolutional layer called GSConv. The Slim Neck, designed based on GSConv, replaces the original algorithm's neck, reducing the number of network parameters and improving detection speed. Secondly, we incorporate SimAM, a non-parametric attention mechanism, into the improved neck to enhance detection accuracy. Finally, we utilize the SIoU function as the regression prediction loss instead of the original CIoU to address the issue of slow convergence and improve efficiency. According to experimental findings, the YOLOv5-GSS algorithm outperforms the YOLOv5 method by 2.9% on the NEU-DET dataset and achieves an average accuracy (mAP) of 83.8% with a detection speed (FPS) of 100Hz, which is 3.8Hz quicker than the YOLOv5 algorithm.