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YOLOv8s-BISW a Surface Defect Detection Algorithm for Stainless Steel Pipes.
Ziyi Yang1, Runwei Gu2, Likai Zhu1
1School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
This study introduces YOLOv8s-BISW, an advanced algorithm for detecting surface defects in stainless steel pipes. The improved method enhances accuracy and real-time performance for critical industrial applications.
Area of Science:
- Materials Science
- Computer Vision
- Industrial Automation
Background:
- Stainless steel pipes are vital in industries like oil/gas and nuclear power.
- Surface defects compromise mechanical integrity and operational safety.
- Existing inspection methods struggle with feature extraction, interference, and small-target detection.
Purpose of the Study:
- To develop an improved defect detection algorithm for stainless steel pipes.
- To enhance accuracy, robustness, and real-time performance in surface defect inspection.
- To address limitations of current methods in feature extraction and small-target detection.
Main Methods:
- Proposed YOLOv8s-BISW algorithm integrating Bidirectional Feature Pyramid Network (BiFPN), Spatial Group-wise Enhance (SGE) attention, and Wise Intersection over Union (WIoU) loss.
- Image enhancement using Gamma correction and Contrast Limited Adaptive Histogram Equalization (CLAHE).
- Multi-scale feature fusion and attention mechanisms for improved defect representation.
Main Results:
- Achieved a mean Average Precision (mAP) of 0.979 on the Stainless-steel Tube Flaw (STF) dataset.
- Outperformed the original YOLOv8s in precision, recall, and mAP by 0.007, 0.010, and 0.033, respectively.
- Demonstrated superior performance compared to SSD, YOLOv3, and Faster R-CNN with an average detection time of 3.7 ms per image.
Conclusions:
- The YOLOv8s-BISW algorithm offers a significant advancement in automated surface defect detection for stainless steel pipes.
- The method provides a balance between high accuracy and real-time processing, crucial for intelligent manufacturing.
- This research offers practical value for quality control in industrial settings requiring reliable pipe inspection.
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