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.
Network traffic is believed to have a significant impact on network performance and is the result of the application operation on networks. Majority of current network performance analysis are based on the premise that the traffic transmission is through the shortest path, which is too simple to reflect a real traffic process. The real traffic process is related to the network application process characteristics, involving the realistic user behavior. In this paper, first, an application can be divided into the following three categories according to realistic application process characteristics: random application, customized application and routine application. Then, numerical simulations are carried out to analyze the effect of different applications on the network performance. The main results show that (i) network efficiency for the BA scale-free network is less than the ER random network when similar single application is loaded on the network; (ii) customized application has the greatest effect on the network efficiency when mixed multiple applications are loaded on BA network.
In recent years, with the development of computer science, deep learning is held as competent enough to solve the problem of inference and learning in high dimensional space. Therefore, it has received unprecedented attention from both the academia and the business community. Compared with CPU/GPU, FPGA has attracted much attention for its high-energy efficiency, short development cycle and reconfigurability in the aspect of deep learning algorithm. However, because of the limited research on OpenCL optimization on FPGA of deep learning algorithms, OpenCL tools and models applied to CPU/GPU cannot be directly used on FPGA. This makes it difficult for software programmers to use FPGA when implementing deep learning algorithms for a rewarding performance. To solve this problem, this paper proposed an OpenCL computational model based on FPGA template architecture to optimize the time-consuming convolution layer in deep learning. The comparison between the program applying the computational model and the corresponding optimization program provided by Xilinx indicates that the former is 8-40 times higher than the latter in terms of performance.
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