Screw Anomaly Detection Comparison of YoloV8 with Variational Auto Encoders and Generative Adversarial Networks

Manoj Hudnurkar, Geeta Sahu, Suhas Ambekar, Janhavi Vadke, Kartik Kulbhaskar Singh

2025

Abstract

This research introduces a novel approach to anomaly detection in screw manufacturing processes by synergising YoloV8 (You Only Look Once) and hybrid Variational Auto encoders (VAE) and Generative Adversarial Networks (GAN). In our present undertaking, we are utilizing a thoughtfully curated dataset from Kaggle. Our primary emphasis is accurately detecting anomalies, particularly subtle irregularities in specific image areas of the screws. Our research underscores the importance of authentic datasets and involves the assessment of advanced methods, explicitly focusing on analysing the MVTec Anomaly Detection dataset for screws. The YoloV8 model showcases its ability to accurately reconstruct images and detect anomalies, showing great potential for applications in maintaining high manufacturing quality standards. Also, VAE and GAN results are acceptable. When YoloV8 is compared against VAE & GAN, the results in YoloV8 provide the highest accuracy with precision & recall. A comprehensive quantitative evaluation of the overall framework's performance in distinguishing between normal and abnormal cases is achieved by including a classification report that provides precision, recall, and F1-score metrics. According to the results, the accuracy attained while applying VAE-GAN is approximately 90%, while the accuracy attained when employing YoloV8 is between 95% and 97%, with high-speed performance. As a result, YoloV8 performs well and processes information more quickly than other traditional methods. These results highlight the importance of using customized datasets and suggest exciting opportunities for improving anomaly detection techniques in the manufacturing industry.

Paper Citation

in Harvard Style

Hudnurkar M., Sahu G., Ambekar S., Vadke J. and Singh K. (2025). Screw Anomaly Detection Comparison of YoloV8 with Variational Auto Encoders and Generative Adversarial Networks. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 598-608. DOI: 10.5220/0013598100004664

in Bibtex Style

@conference{incoft25,
author={Manoj Hudnurkar and Geeta Sahu and Suhas Ambekar and Janhavi Vadke and Kartik Kulbhaskar Singh},
title={Screw Anomaly Detection Comparison of YoloV8 with Variational Auto Encoders and Generative Adversarial Networks},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={598-608},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013598100004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Screw Anomaly Detection Comparison of YoloV8 with Variational Auto Encoders and Generative Adversarial Networks
SN - 978-989-758-763-4
AU - Hudnurkar M.
AU - Sahu G.
AU - Ambekar S.
AU - Vadke J.
AU - Singh K.
PY - 2025
SP - 598
EP - 608
DO - 10.5220/0013598100004664
PB - SciTePress