基于YOLOv5s和改进质心跟踪的人员跌倒检测

针对目标检测器检测跌倒时过于依赖卷积网络分类效果、无法利用运动信息的问题，本文设计了一种基于YOLOv5s和改进质心跟踪的跌倒检测模型。为解决耗费资源问题，用MobileNetV3网络和Slim Neck模块对YOLOv5s进行轻量化，同时将MobileNetV3网络中的SE模块替换为更高效的ECA模块，降低网络复杂度的同时保持较高的精度。引入哈希感知算法改进质心跟踪，增加目标关联的依据，提高跌倒检测的准确性。实验结果显示改进YOLOv5s模型大小下降52.2%,计算量下降51.8%,精度高达90.3%。改进质心跟踪的跌倒检测模型准确率提高了4.3%。结果表明了本文提出模型的有效性和优越性。

Personnel fall detection based on YOLOv5s and improved centroid tracking

Aiming at the problem that the object detector relies too much on the classification effect of convolutional network and cannot use motion information when detecting falls, this paper designs a fall detection model based on YOLOv5s and improved centroid tracking. To solve the problem of resource consumption, the MobileNetV3 network and Slim Neck module are used to lightweight YOLOv5s, and the SE module in the MobileNetV3 network is replaced with the more efficient ECA module, which reduces the network complexity while maintaining high accuracy. Hash sensing algorithm is introduced to improve centroid tracking, increase the basis of target association, and improve the accuracy of fall detection. The experimental results show that the size of the improved YOLOv5s model is reduced by 52.2%, the computational capacity is reduced by 51.8%, and the accuracy is as high as 90.3%. The accuracy of fall detection model with improved centroid tracking was increased by 4.3%. The results show the effectiveness and superiority of the proposed model.