Automatic Textile Stain Detection Using Yolo Algorithm
Automatic textile stain detection is essential for optimizing the quality control process within the textile industry. Traditional hands-on inspection methods are time-consuming, not immune to errors, and expensive. This research paper proposes a novel approach for automatic textile stain detecti...
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| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | English English |
| 出版事項: |
INTI International University
2024
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| 主題: | |
| オンライン･アクセス: | http://eprints.intimal.edu.my/2105/1/joit2024_46.pdf http://eprints.intimal.edu.my/2105/2/643 http://eprints.intimal.edu.my/2105/ http://ipublishing.intimal.edu.my/joint.html |
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| 要約: | Automatic textile stain detection is essential for optimizing the quality control process within the
textile industry. Traditional hands-on inspection methods are time-consuming, not immune to
errors, and expensive. This research paper proposes a novel approach for automatic textile stain
detection using the YOLO (You Only Look Once) algorithm, a state-of-the-art object detection
model. The proposed system utilizes a YOLOv5 model trained on a diverse dataset of stained
textile images to accurately identify and localize stains in real-time. The model's performance is
evaluated based on standard metrics such as precision, recall, and mean average precision (mAP).
Experimental results Showcase the impact of the YOLO-based approach in achieving high
accuracy and efficiency in stain detection, significantly outperforming traditional methods. This
research contributes to the advancement of automation in the textile industry, ultimately leading
to improved quality control, reduced costs, and enhanced productivity. |
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