AI-Powered Model for Defect Detection and Classification for High-Quality Automotive Manufacturing

This paper presents Detect-iPro, which uses the AI to detect defects at the automotive relay box, assign weights, and track faults in real time. The system relies upon the highly precise but also fast object detection with the YOLOv8 model of deep learning and is easy to integrate with the assembly lines with the help of high-resolution imaging and a convenient graphical user interface. The continuous improvement of the processes and regulatory compliance is also possible through automated data logging as well as real-time feedback that not only ensures a better traceability but also allows the improvement of the process in question. Thorough testing in factory-conditions shows considerable improvements in accuracy, speed and efficiency, and predetermines new standards in automotive quality control. The paper is completed with the discussion of the system limitations, future research principles, and how AI can transform smart manufacturing.

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Publication Details

Journal
Pakistan Journal of Engineering Technology & Science
Published
2025-12-27
DOI
https://doi.org/10.22555/pjets.v13i2.1397
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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article

AI-Powered Model for Defect Detection and Classification for High-Quality Automotive Manufacturing

Imran Aziz, Mohsin Vohra, Syeda Bushra Shabeeh, Jawad Ali Arshad
Pakistan Journal of Engineering Technology & Science
Machine Fault Diagnosis Techniques
article

AI-Powered Model for Defect Detection and Classification for High-Quality Automotive Manufacturing

Imran Aziz, Mohsin Vohra, Syeda Bushra Shabeeh, Jawad Ali Arshad
article en

Abstract

This paper presents Detect-iPro, which uses the AI to detect defects at the automotive relay box, assign weights, and track faults in real time. The system relies upon the highly precise but also fast object detection with the YOLOv8 model of deep learning and is easy to integrate with the assembly lines with the help of high-resolution imaging and a convenient graphical user interface. The continuous improvement of the processes and regulatory compliance is also possible through automated data logging as well as real-time feedback that not only ensures a better traceability but also allows the improvement of the process in question. Thorough testing in factory-conditions shows considerable improvements in accuracy, speed and efficiency, and predetermines new standards in automotive quality control. The paper is completed with the discussion of the system limitations, future research principles, and how AI can transform smart manufacturing.

Pakistan Journal of Engineering Technology & ScienceVol. 13(2)
Iqra University (PK), Sir Syed University of Engineering and Technology (PK)
Industry, innovation and infrastructure
Openalex Percentile: Top 33%
Machine Fault Diagnosis Techniques
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