Neural Network for Real-Time Object Detection on FPGA

Object detection is one of the most active research and application areas of neural networks. In this article we combine FPGA and neural networks technologies to solve the real-time object recognition problem. The article discusses the integration of the YOLOv3 neural network on the DE10-Nano FPGA. Slightly worse indicators of the main metrics (mAP, FPS, inference time) when operating a neural network on a De10-Nano board in comparison with more expensive solutions based on GPUs, are offset by differences in the cost and dimensions of the FPGA board used. Based on the results of the study of various methods for converting neural networks to FPGA, it was concluded that this architecture is applicable for solving problems of detecting objects on a video stream in real time.