Deep Learning-based Real-time Object Detection for Empty-Dish Recycling Robot

The world is facing a shrinking workforce by the sagging birth rate and an aging population. Robot techniques are one of the best solutions for taking place of humans and overcoming this emergency issue. This paper introduces a deep learning-based empty-dish recycling robot for realizing the automatic empty-dish recycling after breakfast, dinner, or lunch in a restaurant, canteen, or cafeteria. A deep learning model You Only Look Once (YOLO) - is equipped for dish detection such as cups, bowls, chopsticks, towels et al., and catch points are calculated for controlling the robot arm to recycle the target dishes. Finally, the YOLOv4 model is quantized by TensorRT and deployed on Jetson Nano. The real-time dish detection YOLO is focused on this paper, the experimental results show that after the YOLO model quantization, the detection time of a single image is increased from 3.93s to 0.44s, with more than 96.00% high accuracy on Precision, Recall, and F1 values. The functions of empty-dish recycling are realized, which will lead to further development toward practical use.