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.
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.
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.
- 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
- Bounding boxes from all detectors are merged using Intersection over Union (IoU)
- Size-based and border-based filtering removes false positives
Camera Frame
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Preprocessing (Grayscale, Normalization)
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Parallel Classical Detectors
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Bounding Box Extraction
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IoU-Based Box Fusion
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Post-Processing Filters
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Final Defect Localization
- Real-time processing using live USB camera feed
- Resolution optimized to 640×480 for performance
- No disk I/O during runtime
- Lightweight classical algorithms ensure stable FPS
This makes the system suitable for on-device industrial inspection.
- Fully real-time execution on Jetson Nano
- No model training or dataset labeling required
- Low power consumption
- High interpretability and explainability
- Textile manufacturing quality control
- Automated inspection on production lines
- Low-cost alternatives to GPU-based vision systems
- Edge-based visual monitoring systems
- Detector confidence voting
- Further runtime optimization
- Optional deep learning verifier
- Integration with industrial conveyor systems
Demo.mp4
Demo.Video.mp4
This project is licensed under the MIT License.
Awais Asghar Electrical Engineering | Computer Vision | Edge AI
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