基于深度学习的钢卷端面缺陷检测系统设计

Because of the complexity of image background, different defect characteristics and lack of standard defect data set, the hot rolled steel end defects are difficult to automatically identify with traditional visual methods. The deep learning-based technical architecture of defect detection for hot-rolled steel was presented, and the key techniques such as image acquisition, preprocessing and segmentation, deep neural network algorithm design and defect feature database construction were given. A deep learning-based steel roll end defect detection system was designed. The test showed that the defect detection rate of this system was more than 90%, which solved the industrial problem of long-term dependence on manual naked eye recognition of end surface defects of hot rolling steel, and realizes the online, automatic and accurate detection of end surface defects. A complete set of end defect detection technology and equipment were formed, which were used in many hot rolling lines in the steel industry. The application results showed that the detection system was stable and reliable.