Surface Defect Detection of Steel Plate Based on SKS-YOLO - Shiyang Zhou; Siming Ao; Zhiying Yang; Huaiguang Liu (IEEE Access, 2024)
During the production process of steel plate, surface defect detection is crucial for high-quality products. For the existing defect detection method based on machine vision, there are various types of problems, such as large model calculations, low detection accuracy and difficulties of recognizing small defect targets. To reduce and solve these issues, the paper proposes a new defect detection model, simplified kernel and squeeze on a you only look once network (SKS-YOLO), which can achieve rapid and effective defect detection on steel plate. Firstly, it adopts EfficientNetv2 as the backbone, significantly reducing model calculations and accelerating training speed while maintaining accuracy. Subsequently, the atrous spatial pyramid pooling (ASPP) module is utilized to obtain a larger receptive field, extracting more feature information from surface defects. The integration of the squeeze excitation network (SE-Net) attention mechanism enhances capabilities of feature extraction furtherly. Then, the K-means algorithm is applied to cluster and obtain more suitable anchor frames for defect targets. It not only increases the number of positive samples, but also expedites model convergence. Finally, the loss function of simplified intersection over union (SIoU) is used to enhance the ability of model to locate and detect surface defect targets. The experimental results show that the mean average precision (mAP) is 89.40% at a detection speed of 55 frames per second (FPS), which is better than the state-of-the-art (SOTA) detection models.