Abstract
A case study in applying modern FPGAs as a platform to accelerate intelligent vision-guided crop detection in agricultural field robots is presented. A state-of-the-art YOLOv3 object detection neural network was adapted to detect broccoli and cauliflower in image dataset obtained from autonomous agricultural robots. A baseline floating point implementation achieved 96 % mAP, and an efficient, quantized implementation suitable for FPGA implementation achieved 92 % mAP. The proposed FPGA solution achieved 136.86 ms inference latency while consuming 12.43 W in a low latency setup, and 28.48 frames per second while consuming 17.78 W in a high throughput setup. Compared to an embedded GPU implementation of the same task, the FPGA solution is 4.12 times more power-efficient and offers 6.85 times higher throughput, translating to faster and longer operation of a battery-powered field robot.
