Efficient online surface defect detection using multiple instance learning

Abstract

Artificial intelligence (AI)-empowered defect detection has emerged as a promising solution for enhancing quality control in manufacturing. While prevalent object detection-based methods have achieved competitive performance, they do carry inherent limitations that necessitate further refinement prior to their practical application in online surface defect detection. This study introduces an efficient online surface defect detection method that makes predictions on the presence of defects based on image-level labels. The method leverages the multiple instance learning (MIL) framework, and utilizes convolutional neural network (CNN) as feature extractor. Extensive experiments are conducted on two real-world datasets to evaluate the method with a custom CNN and Resnet50 as feature extractors (referred to as MIL-CNN and MIL-Resnet50). The results demonstrate the superiority of the proposed method compared with the well-established benchmark methods, especially highlighting the advantage of MIL-Resnet50. Without requiring fine-grained labeling, MIL-Resnet50 enhances F1-macro by 2.5% and 1.5% within the two datasets compared to the second-ranking. Notably, it excels in detecting small-object defects. It also exhibits advantages in terms of detection speed, and are lightweight, making it easy to deploy even in resource-limited scenarios. Additionally, MIL-Resnet50 exhibits the capability to provide approximate defect localization through feature maps. These findings highlight the significant potential of the proposed method within industrial applications.