ATTENTION U-NET–BASED APPROACH FOR AUTOMATED LEATHER DEFECT DETECTION

This study was entirely conducted by the author, including data collection, preprocessing, model implementation, and analysis. The author designed and implemented the Attention U-Net–based method, performed all experiments, and prepared the full manuscript. The work focuses on automating leather defect detection through deep learning and computer vision techniques.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-21
DOI
https://doi.org/10.5281/zenodo.17556641
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

ATTENTION U-NET–BASED APPROACH FOR AUTOMATED LEATHER DEFECT DETECTION

Zenodo (CERN European Organization for Nuclear Research)
Industrial Vision Systems and Defect Detection
article

ATTENTION U-NET–BASED APPROACH FOR AUTOMATED LEATHER DEFECT DETECTION

article en

Abstract

This study was entirely conducted by the author, including data collection, preprocessing, model implementation, and analysis. The author designed and implemented the Attention U-Net–based method, performed all experiments, and prepared the full manuscript. The work focuses on automating leather defect detection through deep learning and computer vision techniques.

Zenodo (CERN European Organization for Nuclear Research)
Niğde Ömer Halisdemir Üniversitesi (TR)
Openalex Percentile: Top 39%
Industrial Vision Systems and Defect Detection
DOIOpen Access PDFOpenAlex
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ATTENTION U-NET–BASED APPROACH FOR AUTOMATED LEATHER DEFECT DETECTION · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS