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Fault Detection Using Canny Edge Detection and Mask R-CNN in Injection Molding of Manufacturing Processes

Published: 23 November 2021 Publication History

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.

References

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Image retrieval based on improved Canny edge detection algorithm Published in: Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC) Date of Conference: 20-22 Dec. 2013 Date Added to IEEE Xplore: 28 August 2014 ISBN Information: INSPEC Accession Number: 14547565 Publisher: IEEE Conference Location: Sheng yang, China
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An Improved Canny Edge Detection Algorithm Li Xuan, Zhang Hong School of Electronic Information Engineering Shenyang Aerospace University Shenyang, China [email protected]
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A Modified Canny Edge DetectionAlgorithm for Fruit Detection & Classification
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M. K. Monir Rabby, B. Chowdhury and J. H. Kim2018 10th International Conference on Electrical and Computer Engineering (ICECE), pp. 237-240, 2018
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Classification of Stenography using Convolutional Neural Networks and Canny Edge Detection Algorithm Francis Jesmar P. Montalbo, Davood Pour Yousefian Barfeh
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Face Detection with the Faster R-CNN Huaizu Jiang ; Erik Learned-Miller 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) FG Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on. :650-657 May, 2017
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Towards Real-Time Smile Detection Based on Faster Region Convolutional Neural Network Nguyen, Chi Cuong Tran, Giang Son Nghiem, Thi Phuong Doan, Nhat Quang Gratadour, Damien Burie, Jean Christophe Luong, Chi Mai
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Comparison of gradient operator based pseudocolored enhanced medical images Raghuvanshi, R.S. | Datar, A. 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference on. :1-5 Jul, 2013
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Mask R-CNN IEEE Transactions on Pattern Analysis and Machine Intelligence December 2018. Kaiming He, Georgia Gkioxari, Piotr Dollar,Ross Girshick

Cited By

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  • (2022)An Adaptive Approach for Fault Localization using R-CNN2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)10.1109/ASSIC55218.2022.10088417(1-6)Online publication date: 19-Nov-2022

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cover image ACM Other conferences
ICCCV '21: Proceedings of the 4th International Conference on Control and Computer Vision
August 2021
207 pages
ISBN:9781450390477
DOI:10.1145/3484274
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 November 2021

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Author Tags

  1. Canny
  2. Edge Detection
  3. Injection Plant
  4. Mask R-CNN

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  • (2022)An Adaptive Approach for Fault Localization using R-CNN2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)10.1109/ASSIC55218.2022.10088417(1-6)Online publication date: 19-Nov-2022

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