AI Based Fabric Inspection System

Textile industry continues to face challenges in ensuring consistent fabric quality due to its reliance on manual inspection processes. This study introduces an AI-driven automated fabric inspection system designed to detect surface defects in real time using deep learning and computer vision techniques. The system is built around a Raspberry Pi 5 platform integrated with TensorFlow and a Pi Camera module. A convolutional neural network (CNN) model processes captured images to identify defects, prompting the system to halt fabric movement and activate a pump-based liquid applicator for marking the flawed regions. The proposed solution is portable, cost-effective, and offers high detection accuracy, making it particularly advantageous for small and medium-sized textile enterprises seeking to modernize their quality control operations. This approach reduces inspection time and improves accuracy in textile manufacturing environments. Experimental validation achieved 92% detection accuracy across multiple fabric types.