A dataset generation for object recognition and a tool for generating ROS2 FPGA node
Hayato Amano, Hayato Mori, Akinobu Mizutani, Tomohiro Ono, Yuma Yoshimoto, Takeshi Ohkawa, Hakaru Tamukoh · 2021
This paper introduces our autonomous driving system equipped with recognition processing units from a camera image for hazard object / human-doll detection and drive lane detection. In particular, this paper focuses on a dataset generation method for neural networks and a generation tool “FPGA Oriented Easy Synthesizer Tool (FOrEST)” for ROS2-FPGA nodes. The results show that mAP of a neural network trained by the generated dataset is 94%, and a overhead of ROS2-FPGA communication by the FOrEST is 2–3 ms.