Surface defect detection of steel based on improved YOLOv5 algorithm

To address the challenge of achieving a balance between efficiency and performance in steel surface defect detection, this paper presents a novel algorithm that enhances the YOLOv5 defect detection model. The enhancement process begins by employing the K-means++ algorithm to fine-tune the location of the prior anchor boxes, improving the matching process. Subsequently, the loss function is transitioned from generalized intersection over union (GIOU) to efficient intersection over union (EIOU) to mitigate the former's degeneration issues. To minimize information loss, Carafe upsampling replaces traditional upsampling techniques. Lastly, the squeeze and excitation networks (SE-Net) module is incorporated to augment the model's sensitivity to channel features. Experimental evaluations conducted on a public defect dataset reveal that the proposed method elevates the mean average precision (mAP) by seven percentage points compared to the original YOLOv5 model, achieving an mAP of 83.3%. Furthermore, our model's size is significantly reduced compared to other advanced algorithms, while maintaining a processing speed of 47 frames per second. This performance demonstrates the effectiveness of the proposed enhancements in improving both accuracy and efficiency in defect detection.