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PartsNet: A Unified Deep Network for Automotive Engine Precision Parts Defect Detection

Published: 08 December 2018 Publication History

Abstract

Defect detection is a basic and essential task in automatic parts production, especially for automotive engine precision parts. In this paper, we propose a new idea to construct a deep convolutional network combining related knowledge of feature processing and the representation ability of deep learning. Our algorithm consists of a pixel-wise segmentation Deep Neural Network (DNN) and a feature refining network. The fully convolutional DNN is presented to learn basic features of parts defects. After that, several typical traditional methods which are used to refine the segmentation results are transformed into convolutional manners and integrated. We assemble these methods as a shallow network with fixed weights and empirical thresholds. These thresholds are then released to enhance its adaptation ability and realize end-to-end training. Testing results on different datasets show that the proposed method has good portability and outperforms the state-of-the-art algorithms.

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  • (2024)AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data AugmentationAutomation10.3390/automation50400335:4(578-596)Online publication date: 20-Nov-2024
  • (2024)A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial ApplicationsIEEE Access10.1109/ACCESS.2024.342516612(94250-94295)Online publication date: 2024
  • (2024)Comparative Analysis of Deep Learning Models for Car Part Image SegmentationData Management, Analytics and Innovation10.1007/978-981-97-3245-6_19(267-279)Online publication date: 28-Jul-2024
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  1. PartsNet: A Unified Deep Network for Automotive Engine Precision Parts Defect Detection

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    CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence
    December 2018
    641 pages
    ISBN:9781450366069
    DOI:10.1145/3297156
    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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    • Shenzhen University: Shenzhen University

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    New York, NY, United States

    Publication History

    Published: 08 December 2018

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    Author Tags

    1. Defect detection
    2. PartsNet
    3. fully convolutional DNN
    4. result refinement

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    Cited By

    View all
    • (2024)AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data AugmentationAutomation10.3390/automation50400335:4(578-596)Online publication date: 20-Nov-2024
    • (2024)A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial ApplicationsIEEE Access10.1109/ACCESS.2024.342516612(94250-94295)Online publication date: 2024
    • (2024)Comparative Analysis of Deep Learning Models for Car Part Image SegmentationData Management, Analytics and Innovation10.1007/978-981-97-3245-6_19(267-279)Online publication date: 28-Jul-2024
    • (2023)Automatic Inspection System for Segregation of Defective Parts of Heavy VehiclesElectrotehnica, Electronica, Automatica10.46904/eea.23.71.4.110800471:3(33-40)Online publication date: 15-Aug-2023
    • (2023)Deep learning model for defect analysis in industry using casting imagesExpert Systems with Applications10.1016/j.eswa.2023.120758232(120758)Online publication date: Dec-2023
    • (2022)A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and ClassificationSensors10.3390/s2220784622:20(7846)Online publication date: 16-Oct-2022
    • (2022)FAECCD-CNet: Fast Automotive Engine Components Crack Detection and Classification Using ConvNet on ImagesApplied Sciences10.3390/app1219971312:19(9713)Online publication date: 27-Sep-2022
    • (2021)Intelligent Machine Vision Model for Defective Product Inspection Based on Machine LearningJournal of Sensor and Actuator Networks10.3390/jsan1001000710:1(7)Online publication date: 28-Jan-2021
    • (2020)Pointer Defect Detection Based on Transfer Learning and Improved Cascade-RCNNSensors10.3390/s2017493920:17(4939)Online publication date: 1-Sep-2020
    • (2020)A Review on Industrial Surface Defect Detection Based on Deep Learning TechnologyProceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence10.1145/3426826.3426832(24-30)Online publication date: 18-Sep-2020
    • Show More Cited By

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