In order to improve the operation efficiency of fire image recognition neural network, the FPGA hardware acceleration of fire image recognition neural network is studied and implemented. Firstly, with the help of fire image database and TensonFlow machine learning platform, a fire image recognition neural network is trained with VGG19 as the neural network model. Then the FPGA hardware design of convolution layer, pooling layer, full connection layer and activation function is carried out for the trained neural network through Vivado. Secondly, the designed VGG19 fire image recognition convolution neural network accelerator is debugged on the ZYNQ7020 development board. Finally, the acceleration performance of fire identification convolutional neural network accelerator system is tested in three aspects: acceleration efficiency, resource utilization and power consumption. The experimental results show that the accelerator can reduce the clock cycle required by each convolution layer of fire image recognition neural network from one million to ten thousand, the resource utilization meets the chip requirements, and the chip power consumption is reduced to 2.067w. While improving the operation efficiency of neural network, it realizes low power consumption.
In recent years, along with the computer operation speed unending enhancement, the computer is employed to carry on the dangerous cargos the examination and the recognition to obtain the more and more widespread applications. Aiming at the disadvantage of high false detection rate in target classification detection using existing feature training classifiers, the work proposes a detection algorithm for hazardous articles with convolutional neural network on the basis of deep learning. For the image to be checked, sliding windows of different scales are used to determine whether there is an object window. For object detection, a convolutional neural network is trained with a large number of positive and negative samples. In order to better adapt to object detection, the topology of the convolutional neural network is improved. The window of suspected hazardous article is input into the improved convolutional neural network for dangerous object detection, and the false detection rate is reduced while maintaining the original detection rate.
In multi-agent systems, agents coordinate their behavior and work together to achieve a shared goal through collaboration. However, in multi-agent systems, selecting qualified participants to form effective collaboration communities is challenging. In this paper, we propose a minimum circle covering algorithm, as a solution for on-demand participant selection for collaboration in multi-agent systems. Furthermore, a twenty-one point FBG sensors are divided into four sensing function agent in Structural Health Monitoring (SHM) system is experimented in an aircraft wing box. Correspondingly, there are four intelligent evaluation agents and one system collaborative agent in the multi-agent intelligent health monitoring system. For the damage loading position prediction on the aircraft wing box, the collaborative participation selection strategy based on the minimum circle coverage is verified experimentally. The research result indicates that the minimum circle covering algorithm can be used to select the participation in multi-agent intelligent health monitoring system, of all the participations in the collaboration, it enables them to identify and select a qualified participants.
This paper is based on the deep learning license plate recognition system, which is a method of deep learning in the recognition of license plates.In the recognition of license plates, improved Convolutional-Neural-Network (CNN) is used to identify the accuracy and speed of recognition. The experimental results show that the application of convolutional neural network in license plate recognition can effectively improve the recognition rate of the license plate in various environments such as pollution, insufficient illumination, etc. This recognition rate is improved by means of a large training character set. The more character forms included in the character set, the higher the recognition rate, the more the license plate character recognition rate can reach 98% or more. In addition, for the trained convolutional neural network, including the license plate extraction and pre-processing recognition speed can also reach less than 30 ms.
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