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An Improved Boundary-Aware U-Net for Ore Image Semantic Segmentation.

Wei Wang1,2, Qing Li1,2, Chengyong Xiao1,2

  • 1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

A new multi-task learning network improves ore image segmentation accuracy for better particle size statistics. The model enhances feature extraction and boundary detection, outperforming standard U-Net for mining applications.

Keywords:
U-Netboundary mask fusion blockimproved encodermulti-task learningore image segmentation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Mineral Processing

Background:

  • Accurate ore particle size statistics are crucial for mine operations, relying heavily on precise ore image segmentation.
  • Challenges in ore image segmentation include variations in ore size/shape, dust, lighting, complex textures, shadows, and ore adhesion, leading to under-segmentation issues.
  • Creating large, labeled datasets for complex ore images is difficult.

Purpose of the Study:

  • To propose a novel, multi-task learning network based on U-Net for improved ore image segmentation.
  • To address limitations of small datasets and enhance feature extraction capabilities.
  • To improve the accuracy of particle size statistics in mining operations.

Main Methods:

  • Developed a multi-task learning network utilizing a U-Net architecture.
  • Incorporated an improved encoder based on Resnet18 for enhanced feature extraction.
  • Designed a decoder with a boundary subnetwork and a mask subnetwork, fused via a boundary mask fusion block (BMFB).

Main Results:

  • Achieved 92.07% pixel accuracy, 86.95% Intersection over Union for ore masks (IOU_M), and 52.32% IOU for ore boundaries (IOU_B).
  • Reduced the average statistical ore particle size error rate (ASE) to 20.38%.
  • Demonstrated improvements over the benchmark U-Net in pixel accuracy (0.65%), IOU_M (1.01%), and IOU_B (5.78%), with a significant reduction in ASE (12.11%).

Conclusions:

  • The proposed multi-task learning network effectively addresses challenges in ore image segmentation, particularly under-segmentation.
  • The Resnet18-based encoder and dual-subnetwork decoder significantly improve feature extraction and boundary detection.
  • The model enhances the reliability of particle size statistics, offering practical benefits for mining operations.