Design and Experiment of a Visual Detection System for Zanthoxylum-Harvesting Robot Based on Improved YOLOv5 Model

Jinkai Guo, Xiao Xiao, Jianchi Miao, Bingquan Tian, Jing Zhao () and Yubin Lan ()
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Jinkai Guo: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Xiao Xiao: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Jianchi Miao: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Bingquan Tian: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Jing Zhao: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Yubin Lan: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2023, vol. 13, issue 4, 1-18

Abstract: In order to achieve accurate detection of mature Zanthoxylum in their natural environment, a Zanthoxylum detection network based on the YOLOv5 object detection model was proposed. It addresses the issues of irregular shape and occlusion caused by the growth of Zanthoxylum on trees and the overlapping of Zanthoxylum branches and leaves with the fruits, which affect the accuracy of Zanthoxylum detection. To improve the model’s generalization ability, data augmentation was performed using different methods. To enhance the directionality of feature extraction and enable the convolution kernel to be adjusted according to the actual shape of each Zanthoxylum cluster, the coordinate attention module and the deformable convolution module were integrated into the YOLOv5 network. Through ablation experiments, the impacts of the attention mechanism and deformable convolution on the performance of YOLOv5 were compared. Comparisons were made using the Faster R-CNN, SSD, and CenterNet algorithms. A Zanthoxylum harvesting robot vision detection platform was built, and the visual detection system was tested. The experimental results showed that using the improved YOLOv5 model, as compared to the original YOLOv5 network, the average detection accuracy for Zanthoxylum in its natural environment was increased by 4.6% and 6.9% in terms of mAP@0.5 and mAP@0.5:0.95, respectively, showing a significant advantage over other network models. At the same time, on the test set of Zanthoxylum with occlusions, the improved model showed increased mAP@0.5 and mAP@0.5:0.95 by 5.4% and 4.7%, respectively, compared to the original model. The improved model was tested on a mobile picking platform, and the results showed that the model was able to accurately identify mature Zanthoxylum in its natural environment at a detection speed of about 89.3 frames per second. This research provides technical support for the visual detection system of intelligent Zanthoxylum-harvesting robots.

Keywords: YOLOv5; deformable convolution; attention mechanism; visual detection system; Zanthoxylum-harvesting 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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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