A Hierarchical Visual Grasping Architecture Based on YOLOv5 With Hyperparameter Optimization
Year: 2025
Type: article
Abstract: Fruit picking in complex orchard environments is limited by the low detection and recognition accuracy due to clustered background, illumination variation, and partial occlusion. To improve the picking performance, a hierarchical visual grasping architecture based on You Look Only Once (YOLO) algorithm and adaptive error compensation is proposed. The upper layer uses YOLOv5 and inverse kinematics to recognize and localize the target. Ant colony optimization is specifically used for hyperparameter tuning of YOLOv5 to improve the detection accuracy. The middle layer dynamically compensates for the output torque of the joint actuator through the feedback linearization method. The lower layer finishes precise grasping through nonlinear mapping model between the pulse width modulation signal and the servo angle. Experimental results validate that the proposed architecture outperforms traditional methods by reaching 98.2% and 97.7% recognition accuracy in obstacle-free scenarios and complex environments, a higher grasping success rate, and a lower positioning deviation.
DOI: 10.4018/ijsir.389878
Authors: Caihong Zhao, Yuhang Ye, Xu Zhou, Yanyu Chen, Yuanhao Yang
Institution: Suzhou University of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4414955572