Lightweight deep neural network method for personnel detection and people counting in elevator

The invention belongs to the technical field of computer vision and target detection, and relates to a lightweight deep neural network method for personnel detection and people counting in an elevatorcar. According to the method, a convolutional neural network model is lightened, a Raspberry Pi 4B is taken as a development system, and embedded resources are fully utilized to locally realize personnel detection and people counting of an elevator car. According to the invention, a convolutional neural network (CNN) model structure comprises 11 blocks and totally comprises 23 convolution layers,region recommendation is carried out on an advanced semantic feature map, and the recommended region carries out dichotomy on a human head part and a background through a full connection layer; and on the basis of a multi-scale diversity target detection algorithm SSD, depth separable convolution is added to the convolution layer of each block of a network structure, the detection speed is obviously increased, video detec