A Holistic Approach to IGBT Board Surface Fractal Object Detection Based on the Multi-Head Model

Abstract

In industrial visual inspection, foreign matters are mostly fractal objects. Detailed detection of fractal objects is difficult but necessary because better decision-making relies on more detailed and more comprehensive detection information. This presents a challenge for industrial applications. To solve this problem, we proposed a holistic approach to fractal object detection based on a multi-head model. We proposed the IWS (Information Watch and Study) module to provide enhancement learning capabilities for object information. It increases the detection dimension of the object and can perform more detailed detection. In order to realize the portability of the IWS module, it can be easily and quickly deployed to the existing advanced object detection model to achieve end-to-end detection. We proposed the FGI (Fine-Grained Information) Head, which is used to extract more comprehensive feature vectors from the original base model. We proposed the WST (Watch and Study Tactic) Learner for object information processing and adaptive learning of class cluster centers. Using the MRD (Multi-task Result Determination) strategy to combine the classification results and IWS results, the final detection results are derived. In the experiment, the IWS and MRD were mounted on three different models of the YOLO series. The experimental results show that YOLO+IWS has good foreign object detection capabilities to meet the needs of industrial visual inspection. Moreover, for the detailed detection ability of fractal objects, YOLO+IWS is better than the other 11 competing methods. We designed a new evaluation index and an adjustment mechanism of class learning weights to make better judgments and more balanced learning. Not only that, we applied YOLO+IWS to form a brand new object detection system.