An interactive ball training partner robot based on YOLOv5 - Zeng Yangji; Liu Zihong; Cai Yong; Guo Xingchen; Mo Jinlong (Dianzi Jishu Yingyong, 2022)
In order to solve the problem of insufficient human-computer interaction ability of ball training partner robots, a new human-computer interaction mode based on Raspberry Pi and YOLOv5 algorithm was proposed, which enabled the robot to realize six different actions: forward, backward, left, right, throwing the ball, and kicking the ball. After calibrating and training the data sets collected in three different environments(indoor, outdoor sunny day and outdoor cloudy day), the recognition accuracy of the six poses in the test set under three different environments is 96.33% indoor，95% outdoor sunny day，and 94.3% outdoor cloudy day, respectively. Compared with other algorithms based on feature matching and small target detection using gestures, the robot has higher detection speed and accuracy, which makes the robot more intelligent.