Enhanced Real-Time Target Detection for Picking Robots Using Lightweight CenterNet in Complex Orchard Environments

Pan Fan, Chusan Zheng, Jin Sun, Dong Chen, Guodong Lang and Yafeng Li ()
Additional contact information
Pan Fan: School of Computer, Baoji University of Arts and Science, Baoji 721016, China
Chusan Zheng: School of Computer, Baoji University of Arts and Science, Baoji 721016, China
Jin Sun: School of Computer, Baoji University of Arts and Science, Baoji 721016, China
Dong Chen: School of Computer, Baoji University of Arts and Science, Baoji 721016, China
Guodong Lang: Apple Full Mechanized Scientific Research Base of Ministry of Agriculture and Rural Affairs, Yangling 712100, China
Yafeng Li: School of Computer, Baoji University of Arts and Science, Baoji 721016, China

Agriculture, 2024, vol. 14, issue 7, 1-23

Abstract: The rapid development of artificial intelligence and remote sensing technologies is indispensable for modern agriculture. In orchard environments, challenges such as varying light conditions and shading complicate the tasks of intelligent picking robots. To enhance the recognition accuracy and efficiency of apple-picking robots, this study aimed to achieve high detection accuracy in complex orchard environments while reducing model computation and time consumption. This study utilized the CenterNet neural network as the detection framework, introducing gray-centered RGB color space vertical decomposition maps and employing grouped convolutions and depth-separable convolutions to design a lightweight feature extraction network, Light-Weight Net, comprising eight bottleneck structures. Based on the recognition results, the 3D coordinates of the picking point were determined within the camera coordinate system by using the transformation relationship between the image’s physical coordinate system and the camera coordinate system, along with depth map distance information of the depth map. Experimental results obtained using a testbed with an orchard-picking robot indicated that the proposed model achieved an average precision (AP) of 96.80% on the test set, with real-time performance of 18.91 frames per second (FPS) and a model size of only 17.56 MB. In addition, the root-mean-square error of positioning accuracy in the orchard test was 4.405 mm, satisfying the high-precision positioning requirements of the picking robot vision system in complex orchard environments.

Keywords: fruit detection; color space; localization; apple-picking robot (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/7/1059/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/7/1059/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:7:p:1059-:d:1426444

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().