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Measurements of Strain

Measurements of Strain

Strain quantifies the deformation of a material under force, typically measured as normal strain, which represents the change in length when compared with the original length. Electrical strain gauges are used for enhanced accuracy. These devices consist of a conductive wire mounted on a paper backing that adheres to the material's surface. These gauges operate on the piezoresistive effect, where the wire's electrical resistance changes in response to mechanical deformation. The strain...

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An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments

Qiaoqiao Xiong^1,2, Qipeng Chen^3,4,5, Saihong Tang⁶

¹Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia.

Scientific Reports

|January 22, 2025

Summary

This study introduces VEE-YOLO, an enhanced deep learning model for industrial defect detection. It significantly improves accuracy and speed for small, complex defects, making it ideal for real-world applications.

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Area of Science:

Computer Vision
Machine Learning
Industrial Automation

Background:

Deep learning excels in defect detection but struggles with small, occluded, or poorly lit industrial parts.
Existing models face challenges in feature extraction and handling diverse object aspect ratios under complex conditions.

Purpose of the Study:

To develop a robust and high-performance defect detection model, VEE-YOLO, based on YOLOv8-n.
To enhance feature extraction, incorporate spatial and channel information, and improve bounding box regression for industrial defect detection.

Main Methods:

Introduced GSConv and the VOVGSCSP module for improved feature extraction and reusability.
Integrated efficient multi-scale attention for enhanced inter-channel information encoding and feature fusion.
Utilized EIoU Loss to address bounding box aspect ratio variability and sample imbalance.

Main Results:

VEE-YOLO model size reduced from 6.096 MB to 5.486 MB.
Detection speed increased from 179 FPS to 244 FPS.
Achieved a mean Average Precision (mAP) of 0.926 on a dataset for detecting stranded elastic needle defects.

Conclusions:

VEE-YOLO demonstrates superior performance in speed and accuracy for industrial defect detection.
The model's enhancements make it highly suitable for deployment in complex industrial environments.
VEE-YOLO offers a significant advancement for deep detection models in real-world manufacturing settings.