Detection result comparison: YOLOv8nvsBMR-YOLO.

<p>Qualitative results demonstrate BMR-YOLO’s superiority: (a) it reduces missed detections (false negatives); (b) it minimizes false alarms (false positives); (c) it provides more precise bounding boxes, confirming enhanced reliability in complex environments.</p>

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Bibliographic Details
Main Author:	Hang Ren (1312905) (author)
Other Authors:	Ping Lan (652758) (author)
Published:	2025
Subjects:	Neuroscience Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified traditional ciou loss time monitoring systems poor lighting conditions multiseam attention mechanism model &# 8217 lighting variation conditions layer routing attention feature extraction performance experimental results demonstrate existing methods shows enabling dynamic allocation deep learning approach biformer vision transformer aware siou metric 000 annotated images reducing computational requirements low computational cost bounding box regression yolo framework based regression process computational resources computational efficiency yolov8n architecture yolo outperforms yolo achieves validation using training stability study proposes scale features proprietary dataset practical applicability performed cross occluded objects notable improvement localization accuracy handle multi detection head detection accuracy comprehensive dataset complex environments comparative analysis c2f module
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Detection result comparison: YOLOv8nvsBMR-YOLO.

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