Real-Time Fabric Defect Detection on Jetson Orin Nano

Project Overview

This project implements a real-time fabric defect detection system using purely classical computer vision techniques, deployed and optimized on the Jetson Nano. The system avoids deep learning entirely and relies on multi-method classical analysis combined with IoU-based bounding box fusion for robust and interpretable defect localization. The solution is designed for low-power edge devices and is suitable for industrial textile inspection where cost, explainability, and real-time performance are critical.

Problem Statement

Manual fabric inspection is:

Time-consuming
Inconsistent
Prone to human error

While deep learning–based solutions exist, they:

Require large labeled datasets
Demand high computational resources
Are expensive to deploy

Objective:
Develop a low-cost, real-time, and interpretable fabric defect detection system using classical computer vision, deployable on an edge device without any training or labeled data.

Methodology

The system uses multiple independent classical detectors, each capturing different defect characteristics. Their outputs are fused using an IoU-based strategy to produce stable final detections.

Classical Techniques Used

GLCM Texture Analysis – detects texture irregularities
FFT Frequency Analysis – detects disruptions in periodic patterns
Gabor Wavelets – captures directional and repetitive textures
Statistical Local Variance – highlights abrupt anomalies
Background Subtraction – detects stains and fading
Edge Detection + Hough Transform – detects linear defects

Fusion Strategy

Bounding boxes from all detectors are merged using Intersection over Union (IoU)
Size-based and border-based filtering removes false positives

System Pipeline


Camera Frame
↓
Preprocessing (Grayscale, Normalization)
↓
Parallel Classical Detectors
↓
Bounding Box Extraction
↓
IoU-Based Box Fusion
↓
Post-Processing Filters
↓
Final Defect Localization