Research on Weather Recognition Based on a Field Programmable Gate Array and Lightweight Convolutional Neural Network

With the rapid development of deep learning, weather recognition has become a research hotspot in the field of computer vision, and the research on field programmable gate array (FPGA) acceleration based on deep learning algorithms has received more and more attention, based on which, we propose a method to implement deep neural networks for weather recognition in a small-scale FPGA. First, we train a deep separable convolutional neural network model for weather recognition to reduce the parameters and speed up the performance of hardware implementation. However, large-scale computation also brings the problem of excessive power consumption, which greatly limits the deployment of high-performance network models on mobile platforms. Therefore, we use a lightweight convolutional neural network approach to reduce the scale of computation, and the main idea of lightweight is to use fewer bits to store the weights. In addition, a hardware implementation of this model is proposed to speed up the operation and save on-chip resource consumption. Finally, the network model is deployed on a Xilinx ZYNQ xc7z020 FPGA to verify the accuracy of the recognition results, and the accelerated solution succeeds in achieving excellent performance with a speed of 108 FPS and 3.256 W of power consumption. The purpose of this design is to be able to accurately recognize the weather and deliver current environmental weather information to UAV (unmanned aerial vehicle) pilots and other staff who need to consider the weather, so that they can accurately grasp the current environmental weather conditions at any time. When the weather conditions change, the information can be obtained in a timely and effective manner to make the correct judgment, to ensure the flight of the UAV, and to avoid the equipment being affected by the weather leading to equipment damage and failure of the flight mission. With the help of this design, the UAV flight mission can be better completed.