In order to meet the requirements of real-time detection of road signs,for the current mainstream target detection algorithms on the image processor there are a large number of model parameters,poor real-time performance,high power consumption and high cost,a real-time detection of road signs based on FPGA is proposed.In order to reduce the number of parameters and improve the detection speed,YOLOv3-tiny is used as the feature extraction network for the training and optimization of the weight parameters;the model floating-point parameters are quantized into 8-bit fixed-point numbers,and the quantized network model is used to complete the deployment experiments on the FPGA.The experimental results show that at the Yolov3-tiny network detection rate,the test frame rate of this system for the experimental dataset can reach 153 fps,the power consumption is 4.92 W,and the peak GOP/s is 115GOP/s.This system can satisfy the requirement of real-time target detection,and it can realize the deployment of the system under low power consumption.
维普
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