Algorithm comparison test results.

<div><p>Traditional fall detection methods face significant limitations in complex environments, particularly under occlusion and poor lighting conditions. To address these challenges and enhance the detection accuracy in intelligent real-time monitoring systems, this study proposes an o...

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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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Algorithm comparison test results.

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