Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Abhiram, S.; Gupta, Heena; James, C.K. Title A name given to the resource Real-Time Fabric Defect Detection Using a Lightweight YOLOv8 Model on Edge Devices Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IC-DECON 2025 - 2025 International Conference on Data, Energy and Communication Network, Proceedings; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/DECoN67170.2025.11448065" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/DECoN67170.2025.11448065</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037313746?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037313746?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Abhiram S., Christ (Deemed to be University), Dept of Statistics and Data Science, Bengaluru, India; Gupta H., Christ (Deemed to be University), Dept of Statistics and Data Science, Bengaluru, India; James C.K., Christ (Deemed to be University), Dept of Statistics and Data Science, Bengaluru, India Description An account of the resource The detection of defects in fabric is a critical process for maintaining quality standards and reducing economic losses in the textile industry. Traditional inspection methods, which rely on human operators, are often slow, inconsistent, and susceptible to error. This research introduces an innovative solution that harnesses Edge AI and deep learning to facilitate real-time, on-site defect detection. We developed a highly efficient and lightweight model based on the YOLOv8 architecture, specifically tailored for deployment on resource-constrained edge devices like NVIDIA Jetson Nano or Raspberry Pi. Through a process of comprehensive literature analysis and domain expertise, a compact, high-precision model was trained on diverse fabric defect datasets. To ensure optimal performance on edge hardware, we employed advanced optimization techniques like quantization and pruning. The primary offering of the work are threefold: the making of a streamlined YOLOv8-based model for fabric defect detection, a comparative analysis of various edge inference strategies, and a proposed system architecture for real-time embedded deployment. This study effectively demonstrates the practical application of advanced AI to solve longstanding challenges in textile quality control. Future efforts will be directed towards extensive real-world operational testing and exploring localized Model Training with Federated Learning enhancement. 2025 IEEE. Subject The topic of the resource deep learning; Edge AI; embedded systems; fabric defect detection; quality control; real-time systems; YOLOv8 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159442-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online