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Mixed Receptive Fields Augmented YOLO with Multi-Path Spatial Pyramid Pooling for Steel Surface Defect Detection.

Kewen Xia¹, Zhongliang Lv¹, Chuande Zhou¹

¹College of Mechanical and Power Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.

Sensors (Basel, Switzerland)

|June 10, 2023

Summary

This summary is machine-generated.

This study introduces an improved YOLOv5s model for steel surface defect detection, enhancing accuracy for various defects like crazing and inclusions by optimizing feature extraction and scale adaptation.

Keywords:

YOLOv5s mixed receptive fields multi-path spatial pyramid pooling re-parameterized conv steel surface defect detection

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Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Steel surface defect detection faces challenges with low efficiency and accuracy due to texture interference and scale variations.
Existing methods struggle to effectively handle complex textures and diverse defect sizes.

Purpose of the Study:

To enhance the detection efficiency and accuracy of steel surface defects.
To address limitations in feature extraction and scale adaptation in current defect detection models.

Main Methods:

An improved YOLOv5s model incorporating a re-parameterized large kernel C3 module for better feature extraction.
A feature fusion structure with a multi-path spatial pyramid pooling module to adapt to scale variations.
A novel training strategy using scale-specific kernel sizes to optimize receptive field adaptation.

Main Results:

Significant accuracy improvements for crazing (14.4%) and rolled-in scale (11.1%) defects.
Enhanced detection accuracy for inclusion (10.5%) and scratched (6.6%) defects.
Achieved a mean average precision (mAP) of 76.8%, outperforming YOLOv5s by 8.6% and YOLOv8s by 3.7%.

Conclusions:

The proposed improved YOLOv5s model effectively overcomes challenges in steel surface defect detection.
The novel modules and training strategy significantly boost detection accuracy and efficiency for diverse defect types and scales.