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SFHG-YOLO: A Simple Real-Time Small-Object-Detection Method for Estimating Pineapple Yield from Unmanned Aerial

Guoyan Yu^1,2,3, Tao Wang¹, Guoquan Guo¹

¹School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This summary is machine-generated.

This study introduces SFHG-YOLO, a new method for accurately counting pineapple buds from drone imagery to improve yield estimation. The model excels at detecting small, dense objects, enhancing agricultural technology.

Keywords:

adaptive contextual information fusion deep learning high-density object detection lightweight network small object detection unmanned aerial vehicle

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Area of Science:

Agricultural Technology
Computer Vision
Machine Learning

Background:

Estimating pineapple yield requires accurate counting of pineapple buds using Unmanned Aerial Vehicle (UAV) photography.
Existing algorithms struggle with real-time performance and accuracy for detecting small objects like pineapple buds in UAV imagery.

Purpose of the Study:

To propose the SFHG-YOLO method, a lightweight network model, to improve the detection of small, high-density pineapple buds in UAV vision.
To enhance detection accuracy and resilience for small objects by improving spatial attention and context information fusion.

Main Methods:

Utilized coordinate attention module and MobileNetV3 to construct a lightweight network.
Developed enhanced spatial attention and adaptive context information fusion modules to improve perception of tiny objects.
Used YOLOv5s as the baseline for comparison.

Main Results:

The SFHG-YOLO model achieved significant improvements: 7.4% in mAP@0.5 and 31% in mAP@0.5:0.95 compared to YOLOv5s.
Demonstrated superior performance in detecting high-density small objects, considering model size and computational cost.

Conclusions:

The SFHG-YOLO method offers a reliable and accurate approach for detecting small pineapple buds.
This technique provides a valuable tool for improving pineapple yield estimation through enhanced UAV-based object detection.