LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection
Real-time detection of rockfall on slopes is an essential part of a smart worksite. As a result, target detection techniques for rockfall detection have been rapidly developed. However, the complex geologic environment of slopes, special climatic conditions, and human factors pose significant challe...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/737 |
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| author | Anjun Yu Hongrui Fan Yonghua Xiong Longsheng Wei Jinhua She |
| author_facet | Anjun Yu Hongrui Fan Yonghua Xiong Longsheng Wei Jinhua She |
| author_sort | Anjun Yu |
| collection | DOAJ |
| description | Real-time detection of rockfall on slopes is an essential part of a smart worksite. As a result, target detection techniques for rockfall detection have been rapidly developed. However, the complex geologic environment of slopes, special climatic conditions, and human factors pose significant challenges to this research. In this paper, we propose an enhanced high-speed slope rockfall detection method based on YOLOv8n. First, the LSKAttention mechanism is added to the backbone part to improve the model’s ability to balance the processing of global and local information, which enhances the model’s accuracy and generalization ability. Second, in order to ensuredetection accuracy for smaller targets, an enhanced detection head is added, and other detection heads of different sizes are combined to form a multi-scale feature fusion to improve the overall detection performance. Finally, a bidirectional feature pyramid network (BiFPN) is introduced in the neck to effectively reduce the parameters and computational complexity and improve the overall performance of rockfall detection. In addition we compare the LSKAttention mechanism with other attention mechanisms to verify the effectiveness of the improvements. Compared with the baseline model, our method improves the average accuracy mAP@0.5 by 4.8%. Moreover, the amount of parameters is reduced by 20.2%. Among the different evaluation criteria, the LHB-YOLOv8 method shows obvious advantages, making it suitable for engineering applications and the practical deployment of slope rockfall detection systems. |
| format | Article |
| id | doaj-art-676fdde9a73142388fb48bf6b2f93842 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-676fdde9a73142388fb48bf6b2f938422025-01-24T13:20:38ZengMDPI AGApplied Sciences2076-34172025-01-0115273710.3390/app15020737LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone DetectionAnjun Yu0Hongrui Fan1Yonghua Xiong2Longsheng Wei3Jinhua She4Jiangxi Ganyue Expressway Co., Ltd., Nanchang 330000, ChinaSchool of Automation, China University of Geosciences, Wuhan 430074, ChinaSchool of Automation, China University of Geosciences, Wuhan 430074, ChinaSchool of Automation, China University of Geosciences, Wuhan 430074, ChinaSchool of Automation, China University of Geosciences, Wuhan 430074, ChinaReal-time detection of rockfall on slopes is an essential part of a smart worksite. As a result, target detection techniques for rockfall detection have been rapidly developed. However, the complex geologic environment of slopes, special climatic conditions, and human factors pose significant challenges to this research. In this paper, we propose an enhanced high-speed slope rockfall detection method based on YOLOv8n. First, the LSKAttention mechanism is added to the backbone part to improve the model’s ability to balance the processing of global and local information, which enhances the model’s accuracy and generalization ability. Second, in order to ensuredetection accuracy for smaller targets, an enhanced detection head is added, and other detection heads of different sizes are combined to form a multi-scale feature fusion to improve the overall detection performance. Finally, a bidirectional feature pyramid network (BiFPN) is introduced in the neck to effectively reduce the parameters and computational complexity and improve the overall performance of rockfall detection. In addition we compare the LSKAttention mechanism with other attention mechanisms to verify the effectiveness of the improvements. Compared with the baseline model, our method improves the average accuracy mAP@0.5 by 4.8%. Moreover, the amount of parameters is reduced by 20.2%. Among the different evaluation criteria, the LHB-YOLOv8 method shows obvious advantages, making it suitable for engineering applications and the practical deployment of slope rockfall detection systems.https://www.mdpi.com/2076-3417/15/2/737slope rockfallintelligent siteBIFPNLSKAttention |
| spellingShingle | Anjun Yu Hongrui Fan Yonghua Xiong Longsheng Wei Jinhua She LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection Applied Sciences slope rockfall intelligent site BIFPN LSKAttention |
| title | LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection |
| title_full | LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection |
| title_fullStr | LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection |
| title_full_unstemmed | LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection |
| title_short | LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection |
| title_sort | lhb yolov8 an optimized yolov8 network for complex background drop stone detection |
| topic | slope rockfall intelligent site BIFPN LSKAttention |
| url | https://www.mdpi.com/2076-3417/15/2/737 |
| work_keys_str_mv | AT anjunyu lhbyolov8anoptimizedyolov8networkforcomplexbackgrounddropstonedetection AT hongruifan lhbyolov8anoptimizedyolov8networkforcomplexbackgrounddropstonedetection AT yonghuaxiong lhbyolov8anoptimizedyolov8networkforcomplexbackgrounddropstonedetection AT longshengwei lhbyolov8anoptimizedyolov8networkforcomplexbackgrounddropstonedetection AT jinhuashe lhbyolov8anoptimizedyolov8networkforcomplexbackgrounddropstonedetection |