Textile Defect Detection Using Artificial Intelligence and Computer Vision—A Preliminary Deep Learning Approach

Abstract

Fabric defect detection is essential for quality assurance in textile manufacturing, where manual inspection is inefficient and error-prone. This paper presents a real-time deep learning-based system leveraging YOLOv11 for detecting defects such as holes, color bleeding and creases on solid-colored, patternless cotton and linen fabrics using edge computing. The system runs on an NVIDIA Jetson Orin Nano platform and supports real-time inference, Message Queuing Telemetry (MQTT)-based defect reporting, and optional Real-Time Messaging Protocol (RTMP) video streaming or local recording storage. Each detected defect is logged with class, confidence score, location and unique ID in a Comma Separated Values (CSV) file for further analysis. The proposed solution operates with two RealSense cameras placed approximately 1 m from the fabric under controlled lighting conditions, tested in a real industrial setting. The system achieves a mean Average Precision (mAP@0.5) exceeding 82% across multiple synchronized video sources while maintaining low latency and consistent performance. The architecture is designed to be modular and scalable, supporting plug-and-play deployment in industrial environments. Its flexibility in integrating different camera sources, deep learning models, and output configurations makes it a robust platform for further enhancements, such as adaptive learning mechanisms, real-time alerts, or integration with Manufacturing Execution System/Enterprise Resource Planning (MES/ERP) pipelines. This approach advances automated textile inspection and reduces dependency on manual processes.