|Place of Conferral||北京|
|Keyword||摄像机标定 表面瑕疵检测 自动检测系统 卷积神经网络 零件测量|
|Other Abstract||Precision workpieces are widely used in many industrial areas, such as consumer electronic products, medical instruments, aeronautic vehicles, etc. The quality of work-piece surface directly affects the functionality and reliability of the products. With the need of large scale equipment manufacturing and strategic scientific research, quality demands for precision work-piece are improving. Especially for some work-pieces, 100% of products are required to be checked. Meanwhile, the requirement for efficient detection and measurement for such a large quantity of items is increasing constantly.|
At present, the method of work-piece detection based on vision is widely used in the field of industry. Many researchers have proposed a variety of algorithms and the corresponding systems for different surface defect detections. However, there are still some problems to be solved in practical application, such as low detection precision, lack of sample capacity, low generalization ability. This dissertation focuses on the key technologies on the surface detection of precision work-piece, such as, calibration of vision system, work-piece recognition, defect detection and defect classification, and so on. The main work and contributions are as follow:
（1）To solve the problem of camera and hand-in-eye system calibration in precision work-piece detection system, a novel and effective self-calibration approach for robot vision is presented, which can effectively estimate both the camera intrinsic parameters and the hand-eye transformation at the same time. The proposed calibration procedure is based on two arbitrary feature points of the environment, and three pure translational motions and two rotational motions of robot end-effector are needed. From the results of experiment, when the distortion of image noise is σ=5 pixels, the reletive errors of intrinsic parameters are less than 0.06%, and the rotational and translational motion parameters of hand-eye relation are less than 2%. When the distortion of motion noise is 0.1, the reletive errors of intrinsic parameters are less than 0.14%, and the rotational and translational motion parameters of hand-eye relation are less than 3%. The proposed algorithm has been verified by simulated data with different noise and disturbance. Because of fewer feature points and robot motions needed, the proposed method greatly improves the efficiency and practicality of the calibration procedure.
（2）To improve the reality and accuracy of locating and capturing precision work-piece in automatic detection system, a fast and accurate work-piece detection algorithm is proposed based on top-down feature extraction and bottom-up saliency estimation. Firstly, a top-down feature extraction method based on the prior knowledge of work-piece is presented, in which the contour of a work-piece is chosen as the major feature and the corresponding template of the edges is created. Secondly, a bottom-up salient region estimation algorithm is proposed, where the image boundaries are labelled as background queries, and the salient region can be detected by computing contrast against image boundary. In addition, strategies such as image pyramids and a stopping criterion are adopted to speed-up the algorithm. From the results of experiment, the runtime of proposed algorithm is about 4ms, and precision work-pieces can be 100% detected successfully when a appropriate threshold is set. Experiments and results demonstrate the effectiveness of the proposed method.
（3）To solve the problem of surface defect segmentation and extraction, a new detection method based on multi-scale and with contour reconstruction for precision work-pieces is proposed. In this method, the training images are reconstructed by automatic multi-scale extraction and with contour local extraction. A new data set based on reconstruction is established. Then, the convolutional neural network trained by the new data set can realize many kind of surface defects detection at the same time. Experiments on the above methods have been carried out. The results show that the success rate of defect detection by proposed algorithm is above 97%. Meanwhile, in order to solve the problem of lack of precision work-piece data set, over ten thousand precision work-pieces images are collected and labeled, which will be a strong support for the following research of precision work-piece surface defect detection.
（4）To solve the problem of lack of sample set, a surface defect detection algorithm based on synthetic sample for precision work-piece is proposed. A lot of synthetic samples are created from a small number of real precision work-pieces, and a convolutional neural network based on the created synthetic samples are proposed. From the results of experiment, when 30 real precision work-pieces is adopted, the detection results by the proposed algorithm can reach 98.2%, 99.1% and 100% for indentation defect, scratch defect and spot defect, respectively. Combining the above proposed technologies, a real-time detection and measurement system based on man-made samples is proposed for small size precision work-pieces. This automatic system embedded with the proposed detection algorithm based on small samples is designed to pick out defective work-pieces without any manual auxiliary. In addition, the calibrate method for vision system with telecentric lens is discussed, and the dimension of the workpieces is measured.
Finally, the results of the research a re summarized, and the future work is analyzed and prospected.
|孙佳. 精密零件视觉外观检测方法研究[D]. 北京. 中国科学院研究生院,2018.|
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