Study on Feedback and Correction of Tomato Picking Localization Information

Abstract

In the process of picking tomatoes, due to the mechanical error caused by the mechanical arm, the tomato positions cannot be detected accurately, and the information feedback of the positioning is not available, affecting the picking efficiency.Therefore, this article proposed visual feedback information and correction, and designed an improved yolov5s model lightweight detection method, whose backbone network was replaced with lightweight ShuffleNetV2.In addition, the Bidirectional Feature Pyramid Network (BiFPN) was added to obtain richer feature information.Experimental results showed that the improved model achieved 97.4 percent mAP, 97.5 percent accuracy and 1.89 MB model size, with inference time of 4.8 ms per image.This detection method quickly calculated the Euclidean distance between the reference point and the target tomato.The target tomato, with the Euclidean distance less than 58.12 mm, was picked successfully, while the one, with the Euclidean distance greater than 58.12 mm, was not picked.Then the error needs to be calculated and fed back to the robot for picking again.The whole process realized information feedback and correction and improved the picking efficiency with less feedback time.