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DOI	10.1109/ACCESS.2024.3408269
	BSM-YOLO: A Dynamic Sparse Attention-Based Approach for Mousehole Detection
	Xie, Tianshuo; Luo, Xiaoling; Pan, Xin
通讯作者	Luo, XL
来源期刊	IEEE ACCESS
ISSN	2169-3536
出版年	2024
卷号	12 页码:78787-78798
英文摘要	In recent years, the proliferation of mousehole in grasslands has exacerbated desertification and compromised grassland productivity, posing potential threats to human safety. Consequently, the identification and forecasting of mouse-hole dynamics for effective infestation control have emerged as pressing concerns. Manual mousehole detection is labor-intensive and time-consuming, hindering comprehensive spatial understanding. Moreover, prevailing detection models lack robust feature extraction for small targets like mousehole, resulting in suboptimal recognition capabilities and diminished accuracy. Addressing these challenges, we propose an enhanced one-stage detection model BSM-YOLO based on YOLOv5 architecture. Firstly, the model integrates a BiFormer module leveraging Bi-Level Routing Attention to capture both global and local features within mousehole images. Subsequently, the incorporation of Shuffle Attention mechanisms enhances the learning of feature dependencies and intricate relationships. Lastly, the adoption of the MPDIoU loss function accurately delineates bounding box characteristics, mitigating redundant box generation and expediting model convergence. In our experimental framework, we curated a dataset comprising 2397 mousehole images to train the BSM-YOLO model. Results indicate that the BSM-YOLO model achieves an average detection accuracy of 94.5%, representing a 5.4% enhancement over the baseline YOLOv5s model. Additionally, the model demonstrates an 8.7 f/s improvement in detection speed. Furthermore, ablation experiments confirm the efficacy of each refinement incorporated into the BSM-YOLO model.
英文关键词	Feature extraction YOLO Classification algorithms Biological system modeling Adaptation models Prediction algorithms Computational modeling YOLOv5 object detection deep learning mousehole
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001242938200001
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404134
推荐引用方式 GB/T 7714	Xie, Tianshuo,Luo, Xiaoling,Pan, Xin. BSM-YOLO: A Dynamic Sparse Attention-Based Approach for Mousehole Detection[J],2024,12:78787-78798.
APA	Xie, Tianshuo,Luo, Xiaoling,&Pan, Xin.(2024).BSM-YOLO: A Dynamic Sparse Attention-Based Approach for Mousehole Detection.IEEE ACCESS,12,78787-78798.
MLA	Xie, Tianshuo,et al."BSM-YOLO: A Dynamic Sparse Attention-Based Approach for Mousehole Detection".IEEE ACCESS 12(2024):78787-78798.