Structural Knowledge-Based Anomaly Detection to Inspect Ball-Based Lens Actuators

Abstract

In the manufacturing supply chain of smart devices, defect inspection for core components, such as lens actuator that constitute the final product, is essential for ensuring productivity and reliability. Especially, the ball-based lens actuator—which controls the movement of the lens through balls mounted in the camera module—is one of the essential devices that require inspection of structural features such as the position, number, and arrangement state of the balls. In the industrial component inspection domain, general-purpose anomaly detection model based on deep learning methods have primarily been used as a means of detecting visual defects; however, they are not suitable for inspecting anomalies in structural features, such as those found in lens actuator. For this reason, this study proposes an anomaly detection process based on structural features. In this study, the proposed method is designed to reduce false positives in defect inspection by explicitly detecting structural anomalies through pattern recognition and computer vision techniques. In this paper, the proposed method was applied to various types of experiments using a dataset of images collected from an actual actuator assembly process. The proposed method showed that it demonstrated superior performance, as well as robustness in invariant-feature recognition compared to general-purpose anomaly detection models. (13.75% higher precision than the conventional inspection system, and 1.48% to 35.84% higher recall than the conventional general-purpose anomaly detection models.)