Research on a multi-person pose estimation model to balance accuracy and speed

This article presents BASP_YOLO, an enhanced multi-person pose estimation model designed to balance accuracy and speed for real-world applications. To address the computational complexity and limited robustness of existing methods, the proposed model integrates lightweight DSConv layers, a multi-scale fusion module combining BiFPN and efficient attention mechanisms, an optimized spatial pyramid pooling module with CSPC connections, and an SPD-DS module to mitigate channel information loss. Evaluated on the MS COCO dataset, BASP_YOLO achieves a [email protected] of 84.6 % at 54 FPS, outperforming mainstream models like YOLO-Pose and OpenPose. The improvements reduce computational load by 52.2 % while enhancing occlusion handling, small-object detection, and robustness to environmental interference. The effectiveness of the model improvements was further validated using the MPII dataset. This work improves the accuracy of pose estimation while compromising real-time performance as little as possible, advancing deployment feasibility in resource-constrained scenarios.