改进YOLOV7的跌倒人员检测

Improved YOLOV7 for Fall Detection

随着人口老龄化的到来,老年人在跌倒后能否及时被发现并得到救治显得越来越重要。采用YOLOV7可以通过图像识别老年人跌倒,为提高原始YOLOV7的检测精度和速度,本研究对YOLOV7进行了一系列改进,并提出了一种新型YOLOV7结构——YOLOV7-CMJ。首先对收集到的图片进行处理,对部分图片进行了旋转、亮度等预处理,并对其进行标定以获取样本数据集;其次引入CBAM注意力机制,增强了通道注意力和空间注意力,从而提升模型的准确性;最后,将YOLOV7中原有的PANet特征融合改为MJPANet,即多跳动征融合结构,并将之前的Concat采用加权的方式进行替换,从而得到改进YOLOV7-CMJ结构。通过与原始YOLOV7进行实验对比可知,改进后的算法精度提高了7.4%、召回率提高了7.1%、平均精度提高了7.1%,证明了改进算法的有效性,更好地满足了摔倒检测要求。

With the advent of the aging population,it is increasingly important for the elderly to be detected and treated in time after falling.In order to improve the detection accuracy and speed of the original YOLOV7,a series of improvements are made to YOLOV7,and a new YOLOV7 structure,namely YOLOV7-CMJ structure,is proposed.Firstly,the collected pictures are processed,and some pictures are preprocessed with rotation,brightness and other preprocessing,and calibrated to obtain sample datasets.Secondly,the CBAM attention mechanism is introduced to enhance channel attention and spatial attention,thereby improving the accuracy of the model.Finally,the original PANet feature fusion in YOLOV7 is changed to MJPANet,that is,multi-beating sign fusion structure,and the previous Concat is replaced by weighting,so as to improve the YOLOV7-CMJ structure.By comparing with the original YOLOV7,it can be seen that the accuracy of the improved algorithm is increased by 7.4%,the recall rate is increased by 7.1%,and the average accuracy is increased by 7.1%,which proves the effectiveness of the improved algorithm and better meets the requirements of fall detection.