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Localization and Pixel-Confidence Network for Surface Defect Segmentation.

Yueyou Wang¹, Zixuan Xu², Li Mei¹

¹Aerospace Research Institute of Materials and Processing Technology, Beijing 100076, China.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This summary is machine-generated.

This study introduces a novel two-stage deep learning network for industrial surface defect segmentation. The enhanced model improves accuracy in segmenting imbalanced defect areas and fine gaps, boosting automated quality assurance.

Keywords:

deep learning machine vision surface defect segmentation two-stage model

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Area of Science:

Computer Vision
Artificial Intelligence
Industrial Automation

Background:

Deep learning for surface defect segmentation faces challenges with imbalanced data and over-segmentation of fine defect gaps.
Existing methods struggle to maintain high accuracy in complex industrial inspection scenarios.

Purpose of the Study:

To develop an advanced deep learning network for robust surface defect segmentation.
To address the limitations of imbalanced area distribution and over-segmentation in defect detection.

Main Methods:

A two-stage image segmentation network integrating a Defect Localization Module and a Pixel Confidence Module was proposed.
The Defect Localization Module performs coarse defect region localization, embedding features into the second stage.
The Pixel Confidence Module refines predictions by analyzing neighboring pixel distributions.

Main Results:

The proposed network demonstrated improved segmentation performance on both self-built and public datasets.
Significant gains were observed in mean Average Precision (mPA) and mean Intersection over Union (mIoU).
Specifically, improvements of 1.58%±0.80% in mPA and 1.35%±0.77% in mIoU were noted on the Carbon Fabric Defect Dataset.

Conclusions:

The novel two-stage network effectively handles imbalanced data and fine gap segmentation challenges.
This approach enhances the reliability of automated quality assurance in industrial production.
The method shows promise for advancing automated inspection systems in manufacturing.