Optimization Algorithm of Steel Surface Defect Detection Based on YOLOv8n-SDEC

Considering steel as one of the most widely utilized materials, the detection of defects on its surface has always been a paramount area of research. Traditional target detection algorithms often face challenges such as low detection accuracy, missed and false detections, insufficient feature extraction capabilities, and inadequate feature fusion in tasks related to steel surface defect detection. To address these issues, this study proposes an enhanced algorithm, YOLOv8n-SDEC, utilizing the open-source dataset NEU-DET from Northeastern University as the sample dataset. Initially, the study improves the original SPPF module to the SPPCSPC module, enabling the network to better emphasize the features of the target. Furthermore, to augment the network's feature extraction capability, a fusion with deformable convolution is introduced, enhancing the extraction of features from defective targets. The traditional CIoU loss function is substituted with the EIoU loss function in YOLOv8n aiming to minimize the discrepancies in height and width between predicted boxes and ground truth boxes. This substitution is intended to hasten model convergence and improve localization performance. Lastly, CARAFE is employed to replace the nearest neighbor algorithm, reducing the loss of feature information due to upsampling operations. Experimental outcomes reveal that the accuracy of the enhanced model reaches 76.7%, marking a 3.3% increase over the traditional model. Compared to conventional steel surface defect detection algorithms, the algorithm introduced in this study achieves more precise detection of steel surface defects.