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16 January 2025 Fall detection algorithm based on improved Yolov7
Hu Cao, Jie Xu
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Proceedings Volume 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024); 1344735 (2025) https://doi.org/10.1117/12.3045319
Event: International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 2024, Wuhan, China
Abstract
Aiming at the problems of slow detection speed and low accuracy in current human fall detection tasks, a fall detection algorithm based on Yolov7 was proposed. ODConv-ELAN module was constructed in Yolov7 backbone network to replace the original ELAN structure and enhance the ability of extracting target features. Secondly, the more advanced EIoU function is used as the new boundary frame loss function, which improves the convergence speed and efficiency of the prediction frame in the process of model training. Finally, CA attention mechanism is introduced into the output terminal of the network to improve the detection performance of human fall behavior. In addition, a fall detection data set in the campus environment was created. The accuracy P of the improved algorithm in this data set reached 94.34%, the recall rate R reached 92.34%, and the average accuracy mAP reached 94.65%, which realized the demand for more accurate human fall detection.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Hu Cao and Jie Xu "Fall detection algorithm based on improved Yolov7", Proc. SPIE 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 1344735 (16 January 2025); https://doi.org/10.1117/12.3045319
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KEYWORDS
Detection and tracking algorithms
Convolution
Feature extraction
RGB color model
Education and training
Ablation
Head
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