Lightweight pear detection algorithm based on improved YOLOv5

Abstract

Pear recognition is one of the key technologies of pear picking robot, and the pear recognition algorithm based on convolutional neural network has high computing cost and large parameters, which is difficult to be deployed on pear picking robot with low computer resources. This paper presents a lightweight pear real-time detection method based on YOLOv5. This method designs a lightweight feature extraction network based on Ghost bottom-leneck, and embeds the SE module into the designed network, which improves the ability of feature extraction while reducing the amount of network parameters. The experimental results show that compared with YOLOv5l, the parameters of the improved lightweight model are reduced by 48.17 %, mAP is increased by 0.9 %, and the recognition speed is increased by 36 %. The improved model is more suitable to be deployed on the picking robot with limited computing power and provides a solution for the vision system of pear picking robot.