Abstract
The traditional image feature point extraction relies on the characteristics of manual design. This paper proposes a neural network image feature point extraction method based on FPGA, which detects image feature points, direction estimation and descriptor extraction. Each part is Based on the convolutional neural network (CNN) implementation, using pipeline optimization, loop unrolling, storage optimization, fixed-point quantization, etc., using Xilinx's high-level synthesis tool Vivado HLS, the algorithm programs of the neural network layers written by C++ and OpenCV are converted into At the RTL level, the image feature points are extracted using the Python+ARM+FPGA method. Experiments show that FPGA can extract a large number of image feature points, and FPGA is 8.2 times more efficient than CPU, 1.7 times that of GPU, and power consumption is much lower than GPU.
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