The Experts below are selected from a list of 78027 Experts worldwide ranked by ideXlab platform
Xuguo Yan - One of the best experts on this subject based on the ideXlab platform.
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a semi supervised convolutional neural network based method for steel Surface Defect recognition
Robotics and Computer-integrated Manufacturing, 2020Co-Authors: Yiping Gao, Liang Gao, Xuguo YanAbstract:Abstract Automatic Defect recognition is one of the research hotspots in steel production, but most of the current methods focus on supervised learning, which relies on large-scale labeled samples. In some real-world cases, it is difficult to collect and label enough samples for model training, and this might impede the application of most current works. The semi-supervised learning, using both labeled and unlabeled samples for model training, can overcome this problem well. In this paper, a semi-supervised learning method using the convolutional neural network (CNN) is proposed for steel Surface Defect recognition. The proposed method requires fewer labeled samples, and the unlabeled data can be used to help training. And, the CNN is improved by Pseudo-Label. The experimental results on a benchmark dataset of steel Surface Defect recognition indicate that the proposed method can achieve good performances with limited labeled data, which achieves an accuracy of 90.7% with 17.53% improvement. Furthermore, the proposed method has been applied to a real-world case from a Chinese steel company, and obtains an accuracy of 86.72% which significantly better than the original method in this workshop.
Ming Lei - One of the best experts on this subject based on the ideXlab platform.
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a steel Surface Defect recognition algorithm based on improved deep learning network model using feature visualization and quality evaluation
IEEE Access, 2020Co-Authors: Shengqi Guan, Ming LeiAbstract:Steel Defect detection is used to detect Defects on the Surface of the steel and to improve the quality of the steel Surface. However, traditional image detection algorithms cannot meet the detection requirements because of small Defect features and low contrast between background and features about steel Surface Defect datasets. A novel recognition algorithm for steel Surface Defects based on improved deep learning network models using feature visualization and quality evaluation is proposed in this paper. Firstly, the VGG19 is used to pre-train the steel Surface Defect classification task and the corresponding DVGG19 is established to extract the feature images in different layers from Defects weight model. Secondly, the SSIM and decision tree are used to evaluate the feature image quality and adjust the parameters and structure of VGG19. On this basis, a new VSD network is obtained and used for the classification of steel Surface Defects. Comparing with ResNet and VGG19 methods, experiment results show that the proposed method markedly can improve the average accuracy of classification, and the model is able to converge quickly, which can be good for steel Surface Defect recognition using VSD network model of feature visualization and quality evaluation.
Jian Gao - One of the best experts on this subject based on the ideXlab platform.
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automatic Surface Defect detection for mobile phone screen glass based on machine vision
Applied Soft Computing, 2017Co-Authors: Chuanxia Jian, Jian GaoAbstract:Display Omitted A Surface Defect detection system is proposed for mobile phone screen glass. This system achieves 94% in sensitivity and 97.33% in specificity.The proposed system takes approximate 1.6601s to detect a MPSG. The detection accuracy and speed can meet the needs of online detection for MPSG.Compared with other methods used in the experiment, the proposed improved fuzzy c-means can segment the Surface Defects in MPSG more accurately. Defect detection using machine vision technology plays an important role in the manufacturing process of mobile phone screen glass (MPSG). This study proposes an improved detection algorithm for MPSG Defect recognition and segmentation. Considering the problem of MPSG image misalignment caused by vibrations in the mobile stages, a contour-based registration (CR) method is used to generate the template image used to align the MPSG images. Based on this registration result, the combination of subtraction and projection (CSP) is used to identify Defects on the MPSG image, which can eliminate the influence of fluctuation in ambient illumination. To segment the Defects with a fuzzy grey boundary from a noisy MPSG image, an improved fuzzy c-means cluster (IFCM) algorithm is developed in this study. A Defect detection system is developed, and the proposed algorithms are validated using a number of experimental tests on MPSG images. The testing results demonstrate that the approach proposed in this study can effectively detect various Defects on MPSG and that it has better performance than other methods.
Yiping Gao - One of the best experts on this subject based on the ideXlab platform.
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a semi supervised convolutional neural network based method for steel Surface Defect recognition
Robotics and Computer-integrated Manufacturing, 2020Co-Authors: Yiping Gao, Liang Gao, Xuguo YanAbstract:Abstract Automatic Defect recognition is one of the research hotspots in steel production, but most of the current methods focus on supervised learning, which relies on large-scale labeled samples. In some real-world cases, it is difficult to collect and label enough samples for model training, and this might impede the application of most current works. The semi-supervised learning, using both labeled and unlabeled samples for model training, can overcome this problem well. In this paper, a semi-supervised learning method using the convolutional neural network (CNN) is proposed for steel Surface Defect recognition. The proposed method requires fewer labeled samples, and the unlabeled data can be used to help training. And, the CNN is improved by Pseudo-Label. The experimental results on a benchmark dataset of steel Surface Defect recognition indicate that the proposed method can achieve good performances with limited labeled data, which achieves an accuracy of 90.7% with 17.53% improvement. Furthermore, the proposed method has been applied to a real-world case from a Chinese steel company, and obtains an accuracy of 86.72% which significantly better than the original method in this workshop.
Mingkai Zhang - One of the best experts on this subject based on the ideXlab platform.
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an intelligent and automated 3d Surface Defect detection system for quantitative 3d estimation and feature classification of material Surface Defects
Optics and Lasers in Engineering, 2021Co-Authors: Yulong Zong, Jin Liang, Huan Wang, Maodong Ren, Mingkai ZhangAbstract:Abstract To evaluate Defects on the Surface of the materials at the 3D level accurately and quantitatively, a 3D Surface Defect detection system based on stereo vision is presented, which can extract the precise 3D Defect features of the detected object. The proposed detection system consists of two image capture modules and a turntable to capture the complete 3D information and color texture information from the object Surface. More precisely, each image capture module is a binocular stereo vision system containing two monochrome cameras, a color camera, and a speckle projector which is used to reconstruct the 3D point clouds of the object Surface based on stereo digital image correlation (stereo-DIC). Furthermore, a point-image mapping relationship between the reconstructed 3D object points and the color images is established. Eventually, the 3D characteristic parameters of Defects are calculated by the corresponding 3D point cloud of the Defect area obtained by segmenting the Defect area using the image segmentation and point cloud segmentation algorithms according to this point-image mapping relationship. A convolutional neural network named DenseNets is employed to identify Defect types intelligently. A high-precision multi-camera calibration method based on close-range photogrammetry is applied to ensure system detection accuracy in the proposed system. The experimental results demonstrate that the system has higher accuracy and better performance in system calibration, 3D reconstruction, and Defect feature calculation.
