Proposal of a Framework for Screw Appearance Anomaly Detection Using a Rotational Imaging System

In screw inspection processes, quality control is predominantly performed through manual visual inspection, where inspectors examine several thousand screws per day. Consequently, inspection accuracy is highly influenced by individual skill levels and fatigue, leading to variability in results and an increased risk of missed defects. These challenges highlight the need for a robust and efficient automated inspection system that operates independently of human subjectivity. To address this issue, we propose an anomaly detection framework for screw inspection based on machine learning and image processing techniques. The proposed method integrates defect regions extraction from single frames with a decision algorithm that leverages the tracking results of abnormal regions across consecutive frames. Furthermore, to enable efficient implementation of the proposed framework, an inspection apparatus capable of evaluating multiple samples simultaneously is developed. Experimental results obtained from 105 samples demonstrate a recall of 100% and a precision of 46%, indicating effective localization of defect regions and a reduction in false detections through the incorporation of temporal tracking. These results confirm that the proposed approach is effective as a visual inspection method that explicitly accounts for the rotational inspection process encountered in practical manufacturing environments.