A Yolov7 cherry tomato identification method that integrates depth information

Bo Cui; Zhi Zeng; Yibin Tian

doi:10.1117/12.2689199

31 July 2023 A Yolov7 cherry tomato identification method that integrates depth information

Bo Cui, Zhi Zeng, Yibin Tian

Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 127471E (2023) https://doi.org/10.1117/12.2689199
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China

Abstract

Research and development of automatic fruit picking robot is an effective way to solve the low efficiency of manual picking and reduce the cost of picking. Picking robots usually need to use the vision system to obtain scene information, and use image processing methods to detect and locate fruit. In this paper, the RGBD camera based on TOF technology is used to collect data. At first, a method to segment the cherry tomato region on the RGBD image by combining color and depth information is proposed, and then according to the segmented cherry tomato region, the point cloud normal vector is incorporated into the RGB image for data preprocessing. Label the preprocessed data set to make data set, train and test on multiple deep learning algorithms, and the experiment shows that the accuracy has been improved to a certain extent. And improve the input and output information post-training based on the yolov7 algorithm. The experimental results show that the improved algorithm map (0.5-0.95) improves by 5.1%, which can meet the detection requirements based on the picking robot

Citation Download Citation

Bo Cui, Zhi Zeng, and Yibin Tian "A Yolov7 cherry tomato identification method that integrates depth information", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 127471E (31 July 2023); https://doi.org/10.1117/12.2689199

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KEYWORDS

RGB color model

Color

Image segmentation

Data modeling

Detection and tracking algorithms

Cameras

Education and training

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