Real-Time Fabric Defect Detection Using a Lightweight YOLOv8 Model on Edge Devices
subject: deep learning; Edge AI; embedded systems; fabric defect detection; quality control; real-time systems; YOLOv8
description: 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.
creator: Abhiram, S.; Gupta, Heena; James, C.K.
source: IC-DECON 2025 - 2025 International Conference on Data, Energy and Communication Network, Proceedings;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833159442-8;
language: English
type: Conference paper
coverage: 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