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Lightweight tea bud recognition network integrating GhostNet and YOLOv5.

Miaolong Cao¹, Hao Fu¹, Jiayi Zhu¹

¹School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Mathematical Biosciences and Engineering : MBE

|January 19, 2023

Summary

This study introduces an improved tea bud detection algorithm using GhostNet and YOLOv5, achieving higher precision and speed for complex backgrounds. The enhanced model significantly boosts detection accuracy, especially for small tea buds.

Keywords:

GhostNet YOLOv5 coordinate attention deep learning tea bud detection

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Area of Science:

Computer Vision
Agricultural Technology

Background:

Tea bud detection faces challenges due to complex backgrounds and small target sizes, impacting accuracy and speed.
Existing methods often struggle with these specific issues in agricultural applications.

Purpose of the Study:

To develop a more accurate and faster tea bud detection algorithm.
To address limitations in current computer vision models for agricultural tasks.

Main Methods:

Integration of GhostNet for parameter reduction and speed enhancement.
Incorporation of a coordinated attention mechanism in the backbone for improved feature extraction.
Utilizing a bi-directional feature pyramid network (BiFPN) for effective feature fusion.
Employing Efficient Intersection over Union (EIOU) as a localization loss function.

Main Results:

The proposed GhostNet-YOLOv5 achieved a precision of 76.31%, outperforming Faster RCNN, YOLOv5, and YOLOv5-Lite.
Demonstrated significant improvements in F1 score across varying bud quantities, shooting angles, and illumination conditions compared to YOLOv5.

Conclusions:

The GhostNet-YOLOv5 algorithm offers superior performance in tea bud detection.
The integration of GhostNet, attention mechanisms, BiFPN, and EIOU effectively enhances detection accuracy and speed for small objects in complex environments.