Enhancing surface detection: A comprehensive analysis of various YOLO models
Material defects can significantly affect strength, durability and overall quality. Complex backgrounds and variations in steel surface images often hinder productivity and quality in industrial environments. Accurate defect detection becomes difficult due to small target size and unclear features....
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| Language: | English |
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Elsevier
2025-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025008138 |
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| author | G. Deepti Raj B. Prabadevi |
| author_facet | G. Deepti Raj B. Prabadevi |
| author_sort | G. Deepti Raj |
| collection | DOAJ |
| description | Material defects can significantly affect strength, durability and overall quality. Complex backgrounds and variations in steel surface images often hinder productivity and quality in industrial environments. Accurate defect detection becomes difficult due to small target size and unclear features. However, implementing accurate and automated object detection algorithms mitigates these challenges, allowing errors or defects to be detected before processing. Version 5 of You Only Look Once (YOLO), a precisely optimized learning model, has undergone extensive testing on steel strip datasets, providing effective solutions for recognition and detection in industry environments. This study presents an improved YOLOv5 detection model, exploiting the efficient channel attention (ECA) and coordinated attention (CoordAtt) mechanisms. Our results show notable improvements, with the ECA hybrid attention mechanism achieving 2–4 times faster inference times while maintaining high accuracy. Additionally, CoordAtt integration minimizes the parameter count by 25 % and gives higher accuracy on one of the datasets. Comparative analysis with YOLOv6, YOLOv7, and YOLOv8 demonstrates the superior accuracy of the enhanced YOLOv5 model on the NEU-DET and GC10-DET steel strip benchmark datasets, highlighting its effectiveness in detecting and timely recognition of actual defects. |
| format | Article |
| id | doaj-art-43edc2173fce487b8972ac8c457ee38d |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-43edc2173fce487b8972ac8c457ee38d2025-02-08T05:00:44ZengElsevierHeliyon2405-84402025-02-01113e42433Enhancing surface detection: A comprehensive analysis of various YOLO modelsG. Deepti Raj0B. Prabadevi1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology University, Vellore, 632014, Tamilnadu, IndiaCorresponding author.; School of Computer Science Engineering and Information Systems, Vellore Institute of Technology University, Vellore, 632014, Tamilnadu, IndiaMaterial defects can significantly affect strength, durability and overall quality. Complex backgrounds and variations in steel surface images often hinder productivity and quality in industrial environments. Accurate defect detection becomes difficult due to small target size and unclear features. However, implementing accurate and automated object detection algorithms mitigates these challenges, allowing errors or defects to be detected before processing. Version 5 of You Only Look Once (YOLO), a precisely optimized learning model, has undergone extensive testing on steel strip datasets, providing effective solutions for recognition and detection in industry environments. This study presents an improved YOLOv5 detection model, exploiting the efficient channel attention (ECA) and coordinated attention (CoordAtt) mechanisms. Our results show notable improvements, with the ECA hybrid attention mechanism achieving 2–4 times faster inference times while maintaining high accuracy. Additionally, CoordAtt integration minimizes the parameter count by 25 % and gives higher accuracy on one of the datasets. Comparative analysis with YOLOv6, YOLOv7, and YOLOv8 demonstrates the superior accuracy of the enhanced YOLOv5 model on the NEU-DET and GC10-DET steel strip benchmark datasets, highlighting its effectiveness in detecting and timely recognition of actual defects.http://www.sciencedirect.com/science/article/pii/S2405844025008138Convolution neural networksYOLOv5Attention mechanismsObject detectionDefect detectionLeakyReLU |
| spellingShingle | G. Deepti Raj B. Prabadevi Enhancing surface detection: A comprehensive analysis of various YOLO models Heliyon Convolution neural networks YOLOv5 Attention mechanisms Object detection Defect detection LeakyReLU |
| title | Enhancing surface detection: A comprehensive analysis of various YOLO models |
| title_full | Enhancing surface detection: A comprehensive analysis of various YOLO models |
| title_fullStr | Enhancing surface detection: A comprehensive analysis of various YOLO models |
| title_full_unstemmed | Enhancing surface detection: A comprehensive analysis of various YOLO models |
| title_short | Enhancing surface detection: A comprehensive analysis of various YOLO models |
| title_sort | enhancing surface detection a comprehensive analysis of various yolo models |
| topic | Convolution neural networks YOLOv5 Attention mechanisms Object detection Defect detection LeakyReLU |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025008138 |
| work_keys_str_mv | AT gdeeptiraj enhancingsurfacedetectionacomprehensiveanalysisofvariousyolomodels AT bprabadevi enhancingsurfacedetectionacomprehensiveanalysisofvariousyolomodels |