在精密电子制造领域中，极其微弱的缺陷也会对产品的功能带来损害，需要及时识别并剔除。基于 AI 算法的视觉缺陷检测方法以其非接触、高精度、高效稳定的优点，正在工业质检环节中得到广泛应用。然而，如何在流水线上快速精准地实现被测产品在被检区域的高精度成像，以及如何解决复杂纹理背景对微弱缺陷检测造成的干扰等问题，成为实现微弱缺陷检测亟待研究解决的系列问题。 因此，本文针对智能手机摄像头规则纹理背景下的微弱缺陷检测任务，搭建了一套缺陷检测系统，围绕高精度快速视觉对位方法、工件表面被检测区域定位方法和规则纹理背景下微弱缺陷检测方法三项关键技术展开研究。本文主要的工作和贡献有：
In the field of precision electronics manufacturing, even extremely weak defects can damage the function of products and therefore need to be detected in a timely manner. The AI algorithm-based visual defect detection method has been widely used in industrial quality inspection with the advantage of non-contact, high accuracy, efficiency and stability. However, achieving high precision imaging of the inspected area imaging of the product on the assembly line quickly and accurately, as well as mitigating the interference problem caused by complex texture backgrounds for faint defect detection are the series of problems that need to be solved urgently to realize faint defect detection. Therefore, in this paper, a mobile phone screen camera defect detection system is established for the task of detecting faint defects with regular texture background, focusing on three key technologies: a high-precision fast alignment method based on binocular vision, a method for extracting the inspected area on the workpiece surface and a method for detecting faint defects with regular texture background. The main work and contributions of this paper are as follows.
(1) A high-precision fast alignment method based on binocular vision is proposed, which can accurately adjust the mobile phone screen to the target position and pose after one-shot alignment operation without servo control. Firstly, a calibration method of the telecentric vision system based on an improved nonlinear damped least-squares method is proposed to establish the relationship between the image coordinate system and the local world coordinate system in the binocular vision system. Secondly, in order to transform the coordinates from the local world coordinate system to a unified coordinate system with the platform’s rotation center as the origin, an angle constraint-based rotation center calibration method is proposed. Thirdly, a two-stage feature point detection method based on shape matching is proposed to detect the feature points of the workpiece. Based on these, the position and pose of the workpiece are obtained. Then the alignment commands are calculated based on the current and the target position and pose of the workpiece, enabling the accurate alignment to be accomplished in one operation. Finally, a series of calibration and alignment experiments were conducted. the experiments and results show that the alignment errors are within ±0.020mm and the time taken to calculate alignment commands is less than 20ms, which demonstrates the effectiveness of the method.
(2) To solve the problem that the detected area is difficult to be extracted effectively under the regular texture background of mobile phone screen camera, a self-supervised image registration method is proposed to align the image to be detected to the reference image position and pose by directly estimating their affine transformation parameters. The method simulates the corresponding affine transformation of the object in the field of view by randomly generating the affine transformation, which alleviates the difficult problem of obtaining real data labels in the image alignment field. Experimental results show that the method performs high accuracy, fast inference speed on the image alignment task with regular texture backgrounds, and good adaptability to different data sets.
(3) Aiming at the difficulty of weakness of defects in regular texture background of mobile phone screen camera with regular texture background, a defect segmentation model is implemented based on multi-scale biased feature fusion, which effectively segments weak defects in images by implementing a feature fusion strategy that is more biased to visual low-level features containing precise spatial location information of defects. In addition, in order to speed up defect detection in industrial sites, a practical and efficient center-of-mass-guided image patch slicing strategy is proposed in this paper for obtaining more compact image blocks. Finally, model compression experiments are carried out through a knowledge distillation strategy to obtain a lightweight defect segmentation model to adapt to the requirements of industrial sites. The experimental results demonstrate that the method can accurately detect two kinds of weak defects in mobile phone screen camera in real time, which are microcracks and rainbow lines.
|Keyword||工业外观检测 规则纹理 视觉对位方法 被检区域定位 微弱缺陷检测|
|Subject Area||自动化技术应用 ; 自动控制技术其他学科|
|MOST Discipline Catalogue||工学::控制科学与工程|
|高晗. 针对规则纹理器件的微弱缺陷检测系统研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.|
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