In various injection molding manufacturing plants, there are many difficulties in detecting defective products during production. Since there are limitations in detecting product defects with the human eye, this paper proposes a framework for detecting product defects in a human-free manufacturing environment. We detect product defects using Canny Edge Detection, a powerful edge detector, and provide reliability of products detected using Mask R-CNN, a neural network with excellent speed and accuracy. As the network, the ResNet101 network with the highest accuracy was selected, and the network was used as the backbone network of Mask R-CNN, and the image was resized and sized using LEDs when shooting to detect even small scratches.
{"title":"Fault Detection Using Canny Edge Detection and Mask R-CNN in Injection Molding of Manufacturing Processes","authors":"Jaeen Lee, Jaehyung Lee, Chaegyu Lee, J. Jeong","doi":"10.1145/3484274.3484286","DOIUrl":"https://doi.org/10.1145/3484274.3484286","url":null,"abstract":"In various injection molding manufacturing plants, there are many difficulties in detecting defective products during production. Since there are limitations in detecting product defects with the human eye, this paper proposes a framework for detecting product defects in a human-free manufacturing environment. We detect product defects using Canny Edge Detection, a powerful edge detector, and provide reliability of products detected using Mask R-CNN, a neural network with excellent speed and accuracy. As the network, the ResNet101 network with the highest accuracy was selected, and the network was used as the backbone network of Mask R-CNN, and the image was resized and sized using LEDs when shooting to detect even small scratches.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129417677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Object detection and pose estimation are fundamental modules in robotic applications, many of these objects are man-made like mechanical parts. Although have been researched widely in recent designs, detecting objects from a cluttered pile is still challenging. In this paper, a robust method for the detection and pose estimation of randomly piled man-made objects in blocks of point cloud is presented which increases the detection efficacy significantly. The approach begins with building blocks from the point cloud, each of which contains one object. Then object detection and pose estimation are performed in the blocks using 3D primitive shapes. We evaluate the performance of our approach in comparison with state-of-the-art methods. Experiments show that the proposed system detects objects efficiently and accurately in the presence of noise and occlusion.
{"title":"DIB: Piled Man-made Object Detection and Pose Estimation in Point Cloud Blocks","authors":"Weiqian Guo, R. Ying, Peilin Liu, Weihang Wang","doi":"10.1145/3484274.3484281","DOIUrl":"https://doi.org/10.1145/3484274.3484281","url":null,"abstract":"Object detection and pose estimation are fundamental modules in robotic applications, many of these objects are man-made like mechanical parts. Although have been researched widely in recent designs, detecting objects from a cluttered pile is still challenging. In this paper, a robust method for the detection and pose estimation of randomly piled man-made objects in blocks of point cloud is presented which increases the detection efficacy significantly. The approach begins with building blocks from the point cloud, each of which contains one object. Then object detection and pose estimation are performed in the blocks using 3D primitive shapes. We evaluate the performance of our approach in comparison with state-of-the-art methods. Experiments show that the proposed system detects objects efficiently and accurately in the presence of noise and occlusion.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114892986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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