Development of a YOLO-V3-based model for detecting defects on steel strip surface

Abstract

During steel strip production, mechanical forces and environmental factors cause surface defects of the steel strip. Therefore, detection of such defects is key to the production of high quality products. Moreover, surface defects of the steel strip cause great economic losses to the high-tech industry. So far, few studies have explored methods of identifying the defects, and most of the currently available algorithms are not sufficiently effective. Therefore, we developed an end-to-end defect detection model based on YOLO-V3. Briefly, the anchor-free feature selection mechanism was utilized to select an ideal feature scale for model training, replace the anchor-based structure, and shorten the computing time. Next, specially designed dense convolution blocks were introduced into the model to extract rich feature information, which effectively improves feature reuse, feature propagation, and enhances the characterization ability of the network. The experimental results show that, compared with other comparison models, the improved model proposed in this study has higher performance. For instance, the proposed model yielded 71.3% mAP on the GC10-DET dataset, and 72.2% mAP on the NEU-DET dataset.