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Real-time dense small object detection algorithm based on multi-modal tea shoots.

Luyu Shuai^1,2, Ziao Chen³, Zhiyong Li^1,2

¹College of Information Engineering, Sichuan Agricultural University, Ya'an, China.

Frontiers in Plant Science

|August 3, 2023

Summary

This summary is machine-generated.

This study introduces a multimodal optimization approach using improved YOLOv5 for accurate tea shoot detection, enhancing accuracy in challenging conditions. The method effectively addresses issues like lighting and occlusion, improving dense small object detection performance.

Keywords:

RGB-D-IR attention mechanism dense small object detection frequency domain multimodal image fusion scale matching tea shoots

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Area of Science:

Computer Vision
Agricultural Technology
Machine Learning

Background:

Tea shoot recognition faces challenges due to variable lighting, similar background colors, and leaf occlusion.
Accurate detection of dense, small tea shoots is crucial for agricultural automation.

Purpose of the Study:

To develop a real-time algorithm for accurate detection of dense, small tea shoots.
To overcome limitations of single-modality image recognition in tea cultivation.

Main Methods:

Proposed a multimodal optimization approach using RGB, depth, and infrared images.
Improved the YOLOv5 model and integrated multimodal fusion techniques, including a novel FFA module.
Implemented an objective-based scale matching method with transfer learning.

Main Results:

The improved YOLOv5 model achieved a 1.7% higher mAP50 than the benchmark with reduced computational cost.
Multimodal fusion significantly increased mAP50, with the FFA module reaching 0.827.
Scale matching and pre-training further improved mAP values by 1-1.4%.

Conclusions:

Multimodal optimization offers a robust solution for dense, small object detection in agriculture.
The proposed FFA module and scale matching enhance detection accuracy under complex environmental conditions.
This research provides a technical foundation for advanced tea cultivation automation.