Huang Xiangqian, Li Xiaoming, Yuan Limengzi, Jiang Zhao, Jin Hongwei, Wu Wanghao, Cai Ru, Zheng Meilian, Bai Hongpeng
International Business School, Zhejiang Yuexiu University, Shaoxing, Zhejiang, 312000, China.
College of Information Science and Technology, Shihezi University, Shihezi, Xinjiang, 832000, China.
Sci Rep. 2025 Jan 15;15(1):2026. doi: 10.1038/s41598-025-86593-9.
Falling is an emergency situation that can result in serious injury or even death, especially in the absence of immediate assistance. Therefore, developing a model that can accurately and promptly detect falls is crucial for enhancing quality of life and safety. In the field of object detection, while YOLOv8 has recently made notable strides in detection accuracy and speed, it still faces challenges in detecting falls due to variations in lighting, occlusions, and complex human postures. To address these issues, this study proposes the SDES-YOLO model, an improvement based on YOLOv8. By incorporating a multi-scale feature extraction pyramid (SDFP), occlusion-aware attention mechanism (SEAM), an edge and spatial information fusion module (ES3), and a WIoU-Shape loss function, the SDES-YOLO model significantly enhances fall detection performance in complex scenarios. With only 2.9M parameters and 7.2 GFLOPs of computation, SDES-YOLO achieves an mAP@0.5 of 85.1%, representing a 3.41% improvement over YOLOv8n, while reducing parameter count and computation by 1.33% and 11.11%, respectively. These results indicate that SDES-YOLO successfully combines efficiency and precision in fall detection. Through these innovations, SDES-YOLO not only improves detection accuracy but also optimizes computational efficiency, making it effective even in resource-constrained environments.
跌倒属于紧急情况，可能导致严重受伤甚至死亡，在没有即时救助的情况下尤其如此。因此，开发一个能够准确、迅速检测跌倒的模型对于提高生活质量和安全性至关重要。在目标检测领域，虽然YOLOv8最近在检测精度和速度方面取得了显著进展，但由于光照变化、遮挡和复杂的人体姿势，它在检测跌倒时仍面临挑战。为了解决这些问题，本研究提出了SDES-YOLO模型，这是一种基于YOLOv8的改进模型。通过结合多尺度特征提取金字塔（SDFP）、遮挡感知注意力机制（SEAM）、边缘与空间信息融合模块（ES3）以及WIoU-Shape损失函数，SDES-YOLO模型显著提高了复杂场景下的跌倒检测性能。SDES-YOLO仅具有290万个参数和72亿次浮点运算，mAP@0.5达到85.1%，比YOLOv8n提高了3.41%，同时参数数量和计算量分别减少了1.33%和11.11%。这些结果表明，SDES-YOLO在跌倒检测中成功地结合了效率和精度。通过这些创新，SDES-YOLO不仅提高了检测精度，还优化了计算效率，使其即使在资源受限的环境中也能有效运行。
