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Related Experiment Video

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DeepMDSCBA: An Improved Semantic Segmentation Model Based on DeepLabV3+ for Apple Images.

Lufeng Mo1, Yishan Fan1, Guoying Wang1

  • 1College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.

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|December 23, 2022
PubMed
Summary

A new deep learning model, DeepMDSCBA, significantly improves semantic segmentation accuracy for apple images, crucial for automating the apple industry. This method enhances speed and precision, even with complex backgrounds and rotten fruit.

Keywords:
apple imageconvolutional block attention moduledepthwise separable convolutionsemantic segmentation

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Area of Science:

  • Computer Vision
  • Agricultural Technology
  • Deep Learning

Background:

  • Semantic segmentation of apples is vital for agricultural automation.
  • Existing methods like FCN and UNet struggle with complex backgrounds and rotten apple detection.
  • These limitations hinder efficient and accurate automated apple processing.

Purpose of the Study:

  • To propose a novel deep learning model, DeepMDSCBA, for enhanced semantic segmentation of apple images.
  • To improve the speed and accuracy of apple segmentation, particularly for images with challenging characteristics.
  • To address the limitations of current semantic segmentation techniques in the context of apple quality assessment.

Main Methods:

  • Developed DeepMDSCBA, a model based on DeepLabV3+ architecture.
  • Integrated a lightweight MobileNet module for efficient feature extraction.
  • Incorporated depthwise separable convolution and a Convolutional Block Attention module to refine feature details and reduce background noise.

Main Results:

  • DeepMDSCBA achieved a Pixel Accuracy (PA) of 95.3% and Mean Intersection over Union (MIoU) of 87.1%.
  • Demonstrated improvements of 3.4% in PA and 3.1% in MIoU compared to DeepLabV3+.
  • Outperformed other semantic segmentation networks like UNet and PSPNet, showing robustness across various conditions (rot, variety, background).

Conclusions:

  • The DeepMDSCBA model offers superior performance for semantic apple image segmentation.
  • The model's design effectively handles complex scenarios, including varying degrees of rot and diverse apple varieties.
  • DeepMDSCBA represents a significant advancement for automated apple industry applications.