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Enhanced detection of diverse defects by developing lighting strategies using multiple light sources based on reinforcement learning

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Abstract

Traditional inspection systems with a single light source are not efficient at detecting a few particular defects with a single inspection. Unlike before, the multi-light source inspection environment allows us to extract more different defects in a piece of work, depending on what we are working on with varying sources of light. We proposed the formulation of the multi-lights source lighting strategy to improve the inspection capability of Automated Optical Inspection (AOI). The process of developing this study not only utilizes the ubiquitous image processing to extract defects but also imports the design of generalized defect sample and reinforcement learning, dealing with diverse defects under in-depth inspection by cascading both light and camera parameters. As a result, the AOI system emphasized that the inspection parameters can be intelligently adjusted to appropriate values based on various defects, maximizing the detection of diverse defects. From the perspective of intelligent AOI results, there are two outstanding outcomes for a multi-light source lighting strategy. One is an efficient learning process, which facilitates us to obtain the strategy needed in 40 to 50 min, depending on the reward function designed. The other is an advanced inspection function that can extract 37% more defects than conventional methods.

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Data availability

The datasets and materials generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Code availability

The Code during the present study is not publicly available but is available from the corresponding author on request.

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Funding

The authors would like to acknowledge the support of this work from Ministry of Science and Technology, Taiwan R.O.C. (MOST 108-2221-E-007-082-MY3).

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  1. Department of Power Mechanical Engineering, National Tsing Hua University, 30013, Hsinchu, Taiwan ROC

    Chih-Kai Cheng & Hung-Yin Tsai

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  1. Chih-Kai Cheng
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  2. Hung-Yin Tsai
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Correspondence to Hung-Yin Tsai.

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Cheng, CK., Tsai, HY. Enhanced detection of diverse defects by developing lighting strategies using multiple light sources based on reinforcement learning. J Intell Manuf 33, 2357–2369 (2022). https://doi.org/10.1007/s10845-021-01800-4

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  • DOI: https://doi.org/10.1007/s10845-021-01800-4

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