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Quasi Real-time Apple Defect Segmentation Using Deep Learning.

Home
Quasi Real-time Apple Defect Segmentation Using Deep Learning.

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Quasi Real-Time Apple Defect Segmentation Using Deep Learning.

Mirko Agarla¹, Paolo Napoletano¹, Raimondo Schettini¹

¹Dipartimento di Informatica, Sistemistica e Comunicazione, Università Milano-Bicocca, 20126 Milano, Italy.

Sensors (Basel, Switzerland)

|September 28, 2023

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a deep learning model for automated apple defect segmentation, improving accuracy and efficiency for agricultural quality control. The method achieves real-time performance and shows comparable results using standard RGB images.

Keywords:

apple defect segmentation multispectral imaging real-time deep learning visual inspection

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Area of Science:

Computer Vision
Agricultural Technology
Machine Learning

Background:

Automated defect segmentation is crucial for apple quality control and food safety in agriculture.
Existing methods often lack the accuracy or efficiency required for real-time applications.

Purpose of the Study:

To develop a deep learning model for accurate and efficient automated segmentation of apple defects.
To enhance the model's performance and applicability using data synthesis and exploring RGB image inputs.

Main Methods:

A novel convolutional neural network (CNN) with a U-shaped architecture and targeted skip-connections was employed.
An ad-hoc data synthesis technique was developed to augment the dataset and mitigate overfitting.
The model was evaluated on multi-spectral apple images and compared with general-purpose segmentation architectures.

Main Results:

The proposed model significantly outperformed existing general-purpose deep learning architectures in segmentation accuracy.
The method demonstrated high computational efficiency, enabling real-time (GPU) and quasi-real-time (CPU) visual inspection.
Using only RGB images yielded accuracy nearly comparable to multi-spectral images, enhancing practical applicability.

Conclusions:

The developed deep learning approach offers a superior solution for automated apple defect segmentation in agricultural settings.
The model's real-time capabilities and adaptability to RGB imagery make it suitable for practical, large-scale visual inspection systems.
This research advances automated quality control in the fruit industry, ensuring better food safety and product quality.