Improvement of the key point detection algorithm based on yolov8

Xueqin Li; Liansun Zeng; Lixin Zheng

doi:10.1117/12.3011492

7 December 2023 Improvement of the key point detection algorithm based on yolov8

Xueqin Li, Liansun Zeng, Lixin Zheng

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129410U (2023) https://doi.org/10.1117/12.3011492
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Human key point detection has essential application prospects in human and computer interaction and disease prediction. However, in the current scenario, there is still some room for improvement in the accuracy of key point detection when facing complex scenes and some key points are missing. To address the above situation this paper proposes an improved YOLOv8-SW model based on the YOLOV8 model, adding the SA attention mechanism to improve the model's ability to extract channel and spatial features, replacing IOU with WIOU, and increasing the proportion of the model's weight on the general quality box. The validation results on the COCO2017 dataset show that the improved model can achieve better detection performance for complex scenes. Compared with the baseline method, the accuracy is improved by 1.0%, which is significant in promoting the practical application of human key point detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xueqin Li, Liansun Zeng, and Lixin Zheng "Improvement of the key point detection algorithm based on yolov8", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129410U (7 December 2023); https://doi.org/10.1117/12.3011492

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