Robustness defect detection: Improving the performance of surface defect detection in interference environment

Abstract

In industrial production environments, the performance of surface defect detection methods for products is affected by disturbances in the equipment, manufacturing processes, and the environment, resulting in poor robustness of the training model. In this paper, an improved defect detection method based on feature extraction fusion and dynamic label assignment is proposed. Three improved network structures, i.e., the adaptive feature recognition convolution (AFRC), path aggregation balanced feature pyramid, and dynamic label assignment, are proposed for the feature extraction, feature fusion, and detection phases of the existing Faster RCNN model, respectively. The AFRC is employed to improve the feature extraction ability of the model for irregular defects, the path balance feature pyramid is utilized to enhance the recognition ability of the model for different levels of feature maps, and the dynamic label assignment is employed to improve the quality of the candidate frames. Under disturbed environments, the results of the proposed improvements outperform the benchmark faster RCNN model in terms of average precision (AP) by 4.1%, 3.7%, and 4.2% on an aluminum surface defect dataset and by 1.8%, 2.2%, and 3.7% AP on a magnetic tile surface defect dataset. The experimental results demonstrate that the proposed scheme effectively improves the robustness of the baseline model in environments with various disturbances.