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TCQI-YOLOv5: A Terminal Crimping Quality Defect Detection Network
Yingjuan Yu1, Dawei Ren1, Lingwei Meng1
1School of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|December 31, 2025
Summary
This study introduces TCQI-YOLOv5, an AI model for automated terminal crimping quality inspection (TCQI) in automotive wiring harnesses. It significantly enhances defect detection accuracy and speed for reliable signal transmission.
Area of Science:
- Automotive Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Automotive wiring harnesses rely on high-quality terminal crimps for signal integrity.
- Manual inspection of terminal crimping quality is inefficient and error-prone.
Purpose of the Study:
- To develop an automated system for accurate and efficient terminal crimping quality inspection (TCQI).
- To improve upon existing methods by leveraging advanced deep learning techniques.
Main Methods:
- An improved YOLOv5 model (TCQI-YOLOv5) was developed.
- Feature extraction was enhanced with C2f structure, FasterNet, and Efficient Multi-scale Attention (EMA).
- SIOU loss function was used to improve bounding box localization.
Main Results:
- TCQI-YOLOv5 achieved a mean average precision (mAP) of 98.3%.
- The model demonstrated superior accuracy in detecting subtle defects like shallow insulation crimps.
- Detection speed meets real-time industrial application requirements.
Conclusions:
- TCQI-YOLOv5 offers an efficient and accurate solution for automated terminal crimping quality inspection.
- The model shows strong potential for practical deployment in the automotive industry.
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