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Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on

Jian Song¹, Yingzhong Tian¹, Xiang Wan²

¹Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.

Sensors (Basel, Switzerland)

|January 8, 2025

Summary

This summary is machine-generated.

This study introduces a novel transfer learning multi-channel fusion network for automotive pipeline surface defect detection. The method achieves high accuracy (97.78%) and speed (153.8 FPS) by fusing diverse defect detection channels.

Keywords:

fast surface quality screening fusion decision making surface defect detection transfer learning

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Area of Science:

Computer Vision
Machine Learning
Automotive Engineering

Background:

Online surface detection of automotive pipelines faces challenges like low defect rates and complex defect morphologies.
Existing methods struggle with small-sample, long-tailed data and variable defect appearances.

Purpose of the Study:

To develop an accurate and efficient method for detecting surface defects in automotive pipelines.
To address limitations of traditional methods using deep neural networks and transfer learning.

Main Methods:

Proposed a transfer learning multi-channel fusion decision network combining traditional visual detection and deep learning.
Designed network channels for specific defect types and used dynamic weights for decision-level fusion.
Implemented an improved Region of Interest (ROI) detection algorithm to enhance efficiency.

Main Results:

Achieved a detection accuracy of 97.78% on an automotive pipeline surface defect dataset.
Reached a detection speed of 153.8 Frames Per Second (FPS).
Demonstrated the network's ability to balance real-time detection needs with high accuracy.

Conclusions:

The multi-channel fusion network effectively synthesizes advantages of different network structures.
This approach overcomes limitations of single-channel networks for automotive pipeline surface defect detection.
The method supports both high-quality detection and rapid processing requirements.