|Place of Conferral||中国科学院自动化研究所|
|Keyword||深孔零件 缺陷检测 视觉检测 深度卷积神经网络 目标检测 轮廓提取 形态分析|
（3）提出了一种无先验知识的多目标形态信息提取算法。深孔零件具有多种尺寸和结构，利用先验知识虽然可以准确提取某种类型零件内部结构形态信息，但是对于不同种类的零件泛化性较差。为了解决不同类型零件中目标形态的提取，提出了一种基于Faster R-CNN的最小外接矩形提取方法。在Faster R-CNN的基础上进行改进，使其检测结果输出从常规的目标包络框变为目标最小外接矩形。随后，将之与基于卡尔曼滤波的骨架提取算法和基于布朗运动的轮廓提取算法相结合，基于所提取的目标最小外接矩形的特征，在无先验知识情况下的对多目标骨架与轮廓进行提取，从而实现了不同类型零件中关键结构形态信息的提取。
With the continuous development of aerospace, optoelectronic, new energy and biomedical engineering, the micro-mechanical systems such as micro-electro-mechanical system (MEMS), micro-targets and micro-optics have been highly developed and found an increasingly wide application. As their basic element, the components with deep hole are especially important. Therefore, the quality inspection of such component is highly demanded. In practical production, due to small diameter, large depth and complex internal structure of the component, its quality inspection is always a difficult problem. The conventional inspection relies on manual operation. Although the operators can implement the inspection task, it is easily affected by their subjective consciousness during the inspection process, and the consistency cannot be guaranteed. At the same time, mass production requires more inspectors, which also leads to high production costs. It has become an important research to achieve automatic quality inspection and defect identification of the component with high precision and efficiency. This paper focuses on the key issues in the quality inspection of the component with deep hole. The main work and contributions are as follows:
(1) An automatic pick-and-place system for the component with deep hole is constructed. Quality inspection of the component is always a technical difficulty. In most cases, the quality is inspected by non-visual methods based on the physical characteristics of the components, and these methods cannot guarantee the accuracy and robustness of the detection. In contrast, the vision-based defect inspection method can effectively inspect the quality of components. However, due to the characteristics of small aperture and deep hole, the acquisition of clear images inside the component has always been a problem. In response to this problem, a visual imaging system based on industrial endoscope is designed, and a complete automatic quality inspection pick-and-place system is developed. The system can overcome the difficulty that the conventional visual imaging system cannot collect clear images inside the component and achieve the acquisition of the component’s internal image stably and clearly. The construction of the system lays a foundation for realizing the automatic quality inspection of components with deep hole.
(2) A multi-objective morphological feature extraction algorithm based on prior knowledge is proposed. The quality of the component is directly related to its internal structure. However, it is difficult for the conventional feature extraction algorithm to accurately extract the internal structure shape information due to shadows, noises and low contrast of the image and the complex internal structure. In order to solve this problem, a skeleton detection algorithm based on Kalman filter is proposed. The algorithm uses prior knowledge to detect key points in the component. Then the pixels belonging to the component structure in the image are extracted through the extreme point searching method. Finally, the point’s position is amended by Kalman filter algorithm to weaken the impact of noise and shadow. Subsequently, a contour extraction algorithm based on Brownian motion model is proposed. The curvature distribution of key structural contours in the component is utilized as a priori knowledge to establish a Brownian motion model. The probability that each edge belongs to the object’s contour is calculated in the edge image of the component. The candidate edges of the contour are filtered out, and finally the fracture contour of the object is extracted by edge grouping method. The Brownian motion model proposed in this paper can remove the redundant edges arising from the noises and shadows in the image and effectively improve the real-time performance and accuracy of contour extraction.
(3) A multi-objective morphological information extraction algorithm without prior knowledge is proposed. Deep hole components have a variety of sizes and internal structures. Although the prior knowledge can be utilized to accurately extract the internal structural shape information of a certain type of parts, the generalization for different types of component is poor. In order to achieve the extraction of shape information in different types of components, a minimum bounding rectangle extraction method based on Faster R-CNN is proposed. Based on Faster R-CNN, the detection result is changed from the regular bounding box into the minimum bounding rectangle. Subsequently, combined with the Kalman filter-based skeleton detection algorithm and the Brownian motion-based contour extraction algorithm, utilizing the extracted minimum bounding rectangle, the objects’ skeleton and contour are extracted without prior knowledge. Sequentially, the shape information extraction of key structural in different types of components is realized.
(4) A quality inspection method of deep hole component is designed. Firstly, the minimum bounding rectangle extraction algorithm based on Faster R-CNN, the skeleton extraction algorithm based on Kalman filter and the contour extraction algorithm based on Brownian motion are adopted to extract different morphological features of the internal structure in the component. Subsequently, different morphological characteristics are analyzed to determine the key factors that can directly reflect the defect characteristics of the component. Finally, the quality inspection of component and defect classification are realized by formulating relevant regulation. The method is validated on the images acquitted in the practical industrial field. The experimental results show that the proposed method can effectively detect the quality of component and the types of defects. Compared with the existing methods, the proposed method achieves significant improvement in accuracy, which can meet the requirements of deep hole components’ quality inspection.
|宫新一. 深孔类零部件内部缺陷的视觉检测方法研究[D]. 中国科学院自动化研究所. 中国科学院大学,中国科学院自动化研究所,2019.|
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