Authors : Kunal G. Borase; Dhanashree Meshram; Sowmiya Radhakrishnan; Praveen Kumar Burra; Bharani Kumar Depuru

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/yc82cru9

Scribd : https://tinyurl.com/yepu29pw

DOI : https://doi.org/10.38124/ijisrt/25mar1263

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Abstract : This paper presents an AI-powered system designed to automate the identification and cataloging of electric switchgear components, improving inventory management and minimizing errors caused by manual classification. Traditional identification methods rely on human efforts, which are labor-intensive and prone to misclassification, leading to inefficiencies in warehouse operations. To overcome these challenges, we leveraged YOLO-based deep learning models to classify switchgear components accurately while ensuring seamless integration with inventory records. Our approach involved training YOLO models to classify switchgear components based on their unique visual features. The model matches each identified component against a Master Data Sheet containing essential details such as part numbers, dimensions, weight, and material specifications. By leveraging YOLO’s advanced feature extraction and classification capabilities, our system achieves high precision in distinguishing visually similar components, ensuring reliable and real-time processing suitable for industrial deployment. During model development, we addressed critical challenges such as variations in lighting conditions, different orientations of components, and cluttered warehouse environments. Extensive data augmentation techniques[10] and model fine-tuning were applied to enhance robustness and maintain high classification accuracy across diverse scenarios. The final AI model achieves up to 95% accuracy, significantly reducing manual identification efforts by 70%, demonstrating its effectiveness in real-world applications. By automating switchgear component identification, our system significantly enhances inventory tracking, minimizes human errors, and optimizes warehouse efficiency. This research highlights the transformative potential of YOLO-based AI automation in industrial inventory management, paving the way for future advancements in intelligent spare part classification and cataloging.

Keywords : Image Classification, Component Identification, YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, Deep Learning, Data Augmentation, CRISP-ML(Q), Inventory Management, Resource Optimization, AI Automation, Warehouse Efficiency.

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