LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement
In the realm of construction site monitoring, ensuring the proper use of safety helmets is crucial. Addressing the issues of high parameter values and sluggish detection speed in current safety helmet detection algorithms, a feature-enhanced lightweight algorithm, LG-YOLOv8, was introduced. Firstly,...
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10141 |
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| author | Zhipeng Fan Yayun Wu Wei Liu Ming Chen Zeguo Qiu |
| author_facet | Zhipeng Fan Yayun Wu Wei Liu Ming Chen Zeguo Qiu |
| author_sort | Zhipeng Fan |
| collection | DOAJ |
| description | In the realm of construction site monitoring, ensuring the proper use of safety helmets is crucial. Addressing the issues of high parameter values and sluggish detection speed in current safety helmet detection algorithms, a feature-enhanced lightweight algorithm, LG-YOLOv8, was introduced. Firstly, we introduce C2f-GhostDynamicConv as a powerful tool. This module enhances feature extraction to represent safety helmet wearing features, aiming to improve the efficiency of computing resource utilization. Secondly, the Bi-directional Feature Pyramid (BiFPN) was employed to further enrich the feature information, integrating feature maps from various levels to achieve more comprehensive semantic information. Finally, to enhance the training speed of the model and achieve a more lightweight outcome, we introduce a novel lightweight asymmetric detection head (LADH-Head) to optimize the original YOLOv8-n’s detection head. Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. Therefore, LG-YOLOv8 has high accuracy and fast detection speed for safety helmet detection, which realizes real-time accurate detection of safety helmets and an ideal lightweight effect. |
| format | Article |
| id | doaj-art-1a9e19e1fd8a4b6dbba543df8d6a267c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-1a9e19e1fd8a4b6dbba543df8d6a267c2024-11-26T17:47:38ZengMDPI AGApplied Sciences2076-34172024-11-0114221014110.3390/app142210141LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature EnhancementZhipeng Fan0Yayun Wu1Wei Liu2Ming Chen3Zeguo Qiu4School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaIn the realm of construction site monitoring, ensuring the proper use of safety helmets is crucial. Addressing the issues of high parameter values and sluggish detection speed in current safety helmet detection algorithms, a feature-enhanced lightweight algorithm, LG-YOLOv8, was introduced. Firstly, we introduce C2f-GhostDynamicConv as a powerful tool. This module enhances feature extraction to represent safety helmet wearing features, aiming to improve the efficiency of computing resource utilization. Secondly, the Bi-directional Feature Pyramid (BiFPN) was employed to further enrich the feature information, integrating feature maps from various levels to achieve more comprehensive semantic information. Finally, to enhance the training speed of the model and achieve a more lightweight outcome, we introduce a novel lightweight asymmetric detection head (LADH-Head) to optimize the original YOLOv8-n’s detection head. Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. Therefore, LG-YOLOv8 has high accuracy and fast detection speed for safety helmet detection, which realizes real-time accurate detection of safety helmets and an ideal lightweight effect.https://www.mdpi.com/2076-3417/14/22/10141safety helmet testinglightweight networkC2f-GhostDynamicConvBiFPNLADH-Head |
| spellingShingle | Zhipeng Fan Yayun Wu Wei Liu Ming Chen Zeguo Qiu LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement Applied Sciences safety helmet testing lightweight network C2f-GhostDynamicConv BiFPN LADH-Head |
| title | LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement |
| title_full | LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement |
| title_fullStr | LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement |
| title_full_unstemmed | LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement |
| title_short | LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement |
| title_sort | lg yolov8 a lightweight safety helmet detection algorithm combined with feature enhancement |
| topic | safety helmet testing lightweight network C2f-GhostDynamicConv BiFPN LADH-Head |
| url | https://www.mdpi.com/2076-3417/14/22/10141 |
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