Abstract
Bus passenger flow information can facilitate scientific dispatching plans, which is essential to decision making and operation performance evaluation. Real-time acquisition of bus passenger flow information is an indispensable part for bus intellectualization. The method of passenger flow statistics in bus video monitoring scene based on deep convolution neural network can provide rich information for passenger flow statistics. In order to adapt to the real scenario of mobile and embedded devices on buses, and to consider the bandwidth limitation, this paper uses a lightweight network model M7, which is suitable for the vehicle system. Based on the classic network model tiny YOLO, the model is optimized by a depthwise separable convolution method. The optimized network model M7 reduces the number of parameters and improves the detection speed, while maintaining a low loss in detection accuracy. As such, the network model M7 is compressed and further optimized by removing redundant channels. The experimental results show that the detection speed of the network model target recognition after channel compression is 40%, which is faster than the precious channel compression on the premise of ensuring detection.
Highlights
With the development of economy and society, urban traffic is increasingly subject to overcrowdedness
With the continuous upgrading of processor performance, real-time statistical analysis of bus passenger flow based on images has been realized. e technology of feature extraction and matching recognition based on the two-dimensional image has been widely used in passenger flow statistics at home and abroad. e development of the deep convolutional neural network in feature extraction has brought amazing convenience to traffic control under video monitoring
Aiming at the passenger flow statistics of bus mobile embedded system, this paper proposes a novel CNN based on compression optimization of the M7 network model, Mathematical Problems in Engineering which is based on the lightweight network model tiny YOLO optimized by a depthwise separable convolution method
Summary
With the development of economy and society, urban traffic is increasingly subject to overcrowdedness. E development of the deep convolutional neural network in feature extraction has brought amazing convenience to traffic control under video monitoring. With the continuous upgrading of processor performance, real-time statistical analysis of bus passenger flow based on images has been realized. It can train the crowd counting algorithm model under video monitoring scene of bus end-to-end, eliminating the steps of foreground segmentation, artificial design, and feature extraction. Aiming at the passenger flow statistics of bus mobile embedded system, this paper proposes a novel CNN based on compression optimization of the M7 network model, Mathematical Problems in Engineering which is based on the lightweight network model tiny YOLO optimized by a depthwise separable convolution method. The compression algorithm is used to compress the convolutional layer
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