Date of Award


Publication Type


Degree Name



Computer Science


Anomaly Detection;Anomaly Localization;Computer vision;Deep learning;Electric connectors;Transformer


Ziad Kobti



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Anomaly detection is of utmost importance in the realm of industrial defect identification, particularly when employing computer vision-based inspection mechanisms within quality control systems. This research introduces the Many-to-One (M2O) framework, which relies on a multi-level transformer encoder combined with a single transformer decoder, which forms many-to-one relation in the framework for detecting and localizing anomalies. The rise of Industry 4.0 and electric vehicles has increased interest in this area. Although previous research has made significant contributions, challenges still exist in this area. It is crucial to develop models that can generalize well and overcome time complexity problems that affect model performance. The proposed M2O framework aims to address these challenges and improve the robust- ness and efficiency of anomaly detection and localization in this domain. M2O is a reconstruction framework that utilizes transformer-based architecture and employs a novel module called Multi-Level Feature Fuse to address these challenges. In order to establish a benchmark for industrial electrical connectors, we have introduced a novel dataset named ECAD, which contains real-world anomalies. This dataset has the potential to inspire further research in this field. Through evaluation against MVtec AD, BTAD, and ECAD, we have demonstrated that M2O outperforms existing methods. Extensive comparisons have established M2O’s ability to overcome previous limitations, making it a robust solution for detecting anomalies in industrial environments.