Research on Small Object Defect DetectionAlgorithms Based on Deep Learning

Abstract In order to improve the accuracy of industrial defect detection based on deep learning, the special defect data set IDEF (Industrial defect data set, IDEF) is constructed, and the defect data is enhanced by digital image processing and deep learning. After data enhancement, the accuracy of defect detection is improved by 0.55 percentage points. Aiming at the phenomenon that small target defects are easy to be missed by the model, a method of adding grid intersection points to the regression strategy of target detection network is proposed to increase the predicted defect benchmark. The experiment shows that this method improves the defect detection accuracy map (mean average precision) by 1.96%. Aiming at the problem of poor representation ability of small target defects, a scale weight loss function is proposed, which makes the model more sensitive to defects with smaller size and more thorough learning of small target defects. It is found that this method improves the accuracy of defect detection by 2.23%. Finally, based on the fcosv2 basic model, two small target defect detection strategies are used at the same time to improve the defect detection accuracy by 3.35%.