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Deep Learning in Semantic Segmentation of Rust in Images

Published: 17 April 2020 Publication History

Abstract

Rust detection is an essential topic in many areas, especially in telecommunication, which needs effective systems to segment and recognize rust on power electric towers, antenna. Our exclusive architecture use is based on a fully convolutional neural network for semantic segmentation and composed of Densenet encoder PSP intermediate layers and two skip connections upsample layers. The code written in Python used Pytorch libraries to compute and categorize the images. Comparing between models such as E-Net, U-Net, FCN, we have received our highest FCN (Fully Convolutional Neural) model for the most stable ratio of IoU (Intersection over Union) in 3 models stated with mean scores are 58.1 for origin images and 61.8 for background removal. With the results, we will contribute to detect rust on electric poles in time to avoid rust-causing serious consequences.

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  • (2025)CorFormer: a hybrid transformer-CNN architecture for corrosion segmentation on metallic surfacesMachine Vision and Applications10.1007/s00138-025-01663-236:2Online publication date: 29-Jan-2025
  • (2024)Autonomous Image-Based Corrosion Detection in Steel Structures Using Deep LearningSensors10.3390/s2411363024:11(3630)Online publication date: 4-Jun-2024
  • (2024)BibliographyCorrosion and Corrosion Protection of Wind Power Structures in Marine Environments10.1016/B978-0-323-85744-4.00015-5(687-727)Online publication date: 2024
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    cover image ACM Other conferences
    ICSCA '20: Proceedings of the 2020 9th International Conference on Software and Computer Applications
    February 2020
    382 pages
    ISBN:9781450376655
    DOI:10.1145/3384544
    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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    Publication History

    Published: 17 April 2020

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    Author Tags

    1. CNN
    2. Corrosion detection
    3. Densenet
    4. Encoder
    5. FCN
    6. PSP
    7. Rust detection

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    Cited By

    View all
    • (2025)CorFormer: a hybrid transformer-CNN architecture for corrosion segmentation on metallic surfacesMachine Vision and Applications10.1007/s00138-025-01663-236:2Online publication date: 29-Jan-2025
    • (2024)Autonomous Image-Based Corrosion Detection in Steel Structures Using Deep LearningSensors10.3390/s2411363024:11(3630)Online publication date: 4-Jun-2024
    • (2024)BibliographyCorrosion and Corrosion Protection of Wind Power Structures in Marine Environments10.1016/B978-0-323-85744-4.00015-5(687-727)Online publication date: 2024
    • (2023)A comparison of learning-based approaches for the corrosion detection on barrels in industrial applicationstm - Technisches Messen10.1515/teme-2023-000990:7-8(522-532)Online publication date: 12-Jun-2023
    • (2023)Video Analytics using Deep Learning in Cloud Services to Detect Corrosion - A Comprehensive Survey2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)10.1109/ICIMIA60377.2023.10426026(949-955)Online publication date: 21-Dec-2023
    • (2022)Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal StructuresSensors10.3390/s2219760022:19(7600)Online publication date: 7-Oct-2022
    • (2022)Image-Based Detection of Structural Defects Using Hierarchical Multi-scale AttentionPattern Recognition10.1007/978-3-031-16788-1_21(337-353)Online publication date: 27-Sep-2022
    • (2021)Semantic Segmentation for Corrosion Detection in Archaeological Artefacts before Restoration2021 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)10.1109/SYNASC54541.2021.00049(246-251)Online publication date: Dec-2021

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