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Object Detection Method for Grasping Robot Based on Improved YOLOv5.

Qisong Song1, Shaobo Li1,2, Qiang Bai1

  • 1College of Mechanical Engineering, Guizhou University, Guiyang 550025, China.

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|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved YOLOv5 object detection method for grasping robots, enhancing positioning accuracy and recognition efficiency. The optimized model achieves high precision and recall, making robot grasping more reliable.

Keywords:
convolutional neural networkgrasping robothand-eye calibration methodimproved YOLOv5 networkobject detection

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Current grasping robots face challenges with inaccurate positioning and low recognition efficiency due to basic vision technology.
  • The industrial trend towards anthropomorphic grasping robots necessitates advancements in object detection.

Purpose of the Study:

  • To develop an improved object detection method for grasping robots to achieve more accurate positioning and recognition.
  • To enhance the performance of the YOLOv5 model for robot grasping applications.

Main Methods:

  • Designed a robot object detection platform and a wooden block image dataset.
  • Employed Eye-In-Hand calibration for 3D object pose estimation.
  • Optimized the YOLOv5 model using network pruning (depth and width) and hyperparameter tuning.

Main Results:

  • The improved YOLOv5 model achieved high performance metrics: 99.35% precision, 99.38% recall, 99.43% mAP, and 99.41% F1 score.
  • The YOLOv5_ours model demonstrated increased mAP (1.12-1.27%) and reduced model size (10.71-86.84%) compared to original YOLOv5 variants.
  • Experimental validation confirmed the feasibility and effectiveness of the proposed object detection method.

Conclusions:

  • The proposed improved YOLOv5 method significantly enhances object detection performance for grasping robots.
  • The optimization techniques lead to a more efficient and accurate system for industrial robotic applications.