Intelligent Thresholding Method for Surface Mount Devices Based on Q-Learning

Threshold selection for thresholding segmentation methods is a critical issue in vision-based detection of surface mount devices (SMDs). The thresholds in actual industrial equipments rely on manual adjustment in SMDs applications, which depends on the experience and ability of an operator. We propose a novel method based Q-Learning to automatically learn the threshold using a segmentation metric instead of manual adjustment, making the threshold adjustment process more intelligent. This framework regards 256 thresholds as state sets. A threshold list with a reasonable step is selected as actions set simulating an operator's threshold adjustment process. The metric difference of SMDs before and after threshold adjustment forms the reward/punishment of Q-Learning framework. Finally, a new strategy that determines the search direction according to previous reward is proposed to improve search efficiency. We conducted experiments on different chips and lights, and the results show that the proposed Q-Learning method performed better on search efficiency and segmentation than the traditional methods.