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DAssd-Net: A Lightweight Steel Surface Defect Detection Model Based on Multi-Branch Dilated Convolution Aggregation

Ji Wang¹, Peiquan Xu^1,2, Leijun Li³

¹School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

Sensors (Basel, Switzerland)

|July 8, 2023

Summary

A new lightweight model, DAssd-Net, accurately detects steel surface defects using multi-branch dilated convolutions. It achieves high accuracy with a significantly smaller model size compared to YOLOv8.

Keywords:

attention mechanism dilated convolutional lightweight model object detection surface defect detection

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Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Steel production is prone to surface defects like cracks and pores.
Accurate and timely defect detection is crucial for maintaining steel quality and performance.

Purpose of the Study:

To develop a lightweight and efficient model for steel surface defect detection.
To improve the accuracy and reduce the computational complexity of defect detection systems.

Main Methods:

Proposed DAssd-Net model featuring multi-branch dilated convolution aggregation.
Incorporated Dilated Convolution and Channel/Spatial Attention Fusion Modules (DCM/DSM) for enhanced feature extraction.
Utilized heat map visualization for model analysis and receptive field optimization.

Main Results:

DAssd-Net achieved 81.97% mean Average Precision (mAP) on the NEU-DET dataset.
The model size is only 18.7 MB, significantly smaller than existing models.
Outperformed YOLOv8 by 4.69% in mAP while being substantially more lightweight.

Conclusions:

DAssd-Net offers a highly effective and lightweight solution for steel surface defect detection.
The model's architecture enhances spatial awareness and suppresses redundant channel features.
Demonstrates a promising approach for real-time industrial defect monitoring.