Human Fall Detection Based on Re-Parameterization and Feature Enhancement

It is easy to fall when the stairs, subway stations, bus stations, and factories are crowded. Real-time detection of human falls is helpful for timely assistance. In this paper, we propose an efficient and real-time detection network ED-YOLO for human fall detection. Firstly, a re-parameterization backbone is proposed. The shallow convolution (conv) modules in the backbone are replaced by DBBConv and DBBC3 modules, which 1*3 and 3*1 convs are used to replace pooling in DBBConv, and DBBConv is used to replace normal conv in DBBC3. The deep conv module in the backbone are replaced by E-DBBConv and E-DBBC3 modules, which can improve the ability to extract detailed features. Then, a novel feature enhancement module (FEM) is proposed to enhance the features representation of the region of interest and the fusion of features. FEM is added to the feature pyramid network (FPN) to improve detection accuracy. Finally, the CIoU Loss is replaced by Gradient Smoothing-SIoU loss (GS-SIoU Loss), and gradient smoothing is introduced to improve the regression speed and accuracy of the prediction box. In order to further reduce the inference over-head of the model, the network proposed in this paper is pruned. The mAP of the proposed network achieves 96.25%, while the parameters of the model are only 6.34M, and the detection FPS reaches 31 in RTX2080ti. The proposed network and other mainstream lightweight networks are tested on the test set. The experimental results show that the performance of human fall detection in the proposed network is superior to other networks. Especially, the mAP is 2.42% higher than YOLOv5s, and the detection speed is 14.8% faster than it.