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.

具有单个光源的传统检测系统在通过一次检测检测一些特定缺陷方面效率不高。与以前不同的是，多光源检测环境使我们能够提取一件作品中更多不同的缺陷，具体取决于我们使用不同光源进行的工作。我们提出了多光源照明策略的制定，以提高自动光学检测（AOI）的检测能力。开发这项研究的过程不仅利用无处不在的图像处理来提取缺陷，还引入了广义缺陷样本和强化学习的设计，通过级联光和相机参数来处理深度检查下的各种缺陷。因此，AOI系统强调检测参数可以根据各种缺陷智能调整到合适的值，最大限度地检测各种缺陷。从智能AOI结果来看，多光源照明策略有两个突出的结果。一个是有效的学习过程，这有助于我们在 40 到 50 分钟内获得所需的策略，具体取决于设计的奖励函数。另一个是先进的检查功能，可以比传统方法多提取 37% 的缺陷。这有助于我们在 40 到 50 分钟内获得所需的策略，具体取决于设计的奖励函数。另一个是先进的检查功能，可以比传统方法多提取 37% 的缺陷。这有助于我们在 40 到 50 分钟内获得所需的策略，具体取决于设计的奖励函数。另一个是先进的检查功能，可以比传统方法多提取 37% 的缺陷。