Design of Road Traffic Target Detection System Based on FPGA+ARM Heterogeneous Platform
In response to the requirement of small size,high energy efficiency,fast detection speed,and high accuracy for current object detection technology in intelligent vehicles,a novel embedded road traffic object detection design is proposed.This design uses YOLOv3 as the base network model and enhances detection accuracy and speed by adding new network detection layers and employing network pruning techniques.On the hardware side,a hardware platform is built around the Deep Learning Processing Unit(DPU)to parallelize the network's convolutional computations.The improved model,after quantization and compilation,can be deployed on an FPGA+ARM heterogeneous platform.Testing on the KITTI dataset shows a detection accuracy of 85.32%,power consumption of 8.2 W,detection speed of 31.2 Hz,and a computational power efficiency of 58.3 GOPs/W,which is 3.9 times that of the RTX 2060 Super GPU and 9.3 times that of the Intel i7-12400 CPU.Experimental results indicate that this design meets the requirement for road traffic object detection.Compared to commonly used object detection platforms like GPUs,the proposed solution is more suitable for flexible deployment in intelligent vehicles with limited space and energy supply due to its smaller deployment footprint and lower power con-sumption.
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