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Related Experiment Video

Updated: Jun 13, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Published on: May 15, 2017

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 summary is machine-generated.

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.

Keywords:

BiFPN Vision-based inspection WIoU stainless steel pipe surface defect

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Last Updated: Jun 13, 2026

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Failure of Cleaning Verification in Pharmaceutical Industry Due to Uncleanliness of Stainless Steel Surface

Failure of Cleaning Verification in Pharmaceutical Industry Due to Uncleanliness of Stainless Steel Surface

Published on: August 11, 2017

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.