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Multiscale apple recognition method based on improved CenterNet.

¹College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, China.

|April 18, 2024

Summary

This summary is machine-generated.

This study introduces a fast CenterNet apple recognition method to improve real-time detection for apple-picking robots in complex environments. The enhanced YoloV5 model achieved high accuracy, even in challenging dark or occluded conditions.

Keywords:

Apple recognition Improve YoloV5 ROI Resnet-44

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Area of Science:

Robotics
Computer Vision
Agricultural Technology

Background:

Traditional apple-picking robots struggle with real-time apple detection in complex agricultural settings.
Accurate fruit identification is crucial for efficient automated harvesting.

Purpose of the Study:

To develop an efficient and accurate apple recognition method for dense scenes, enhancing robotic harvesting capabilities.
To improve the real-time detection of multiple apple targets in challenging environments.

Main Methods:

A fast CenterNet apple recognition method was proposed, utilizing a resnet-44 fully convolutional network, region of interest network (RPN), and region of interest (ROI).
An improved YoloV5 network model was employed for experimental validation.

Main Results:

The improved YoloV5 model achieved high recognition accuracies of 94.1% and 95.8% for apples in nighttime conditions.
The method demonstrated improved recognition of occluded and dark-light features, showing increased robustness on actual datasets.

Conclusions:

The proposed CenterNet method, particularly with the improved YoloV5 model, significantly enhances apple detection efficiency and accuracy for robotic applications.
The developed model is robust and effective in complex, low-light, and occluded scenarios, paving the way for more advanced automated harvesting.