This project outlines a step-by-step guide to develop a rust detection model using image recognition with Python and TensorFlow. The model is trained to classify images as corrosion or no corrosion.
The above diagram gives an overview of the generalised approach towards rust detection using tensorflow.
Welcome to the Microscopic Feature Extraction for Rust Detection Repository!
This repository hosts an innovative methodology for extracting microscopic features from macroscopic images, with a specific focus on rust detection. Leveraging the power of TensorFlow, a leading deep learning framework, and sophisticated image processing techniques, this project aims to uncover subtle details that often elude the naked eye.
Image Preprocessing: Get started by loading and preprocessing your images. Convert images to a standardized format and normalize pixel values for consistent analysis.
Segmentation: Utilize advanced image segmentation techniques like thresholding, watershed, and U-Net to isolate regions of interest, such as rust or scratch-like features.
Texture Analysis: Dive into texture analysis using gray-level co-occurrence matrix (GLCM) or local binary pattern (LBP) methods to reveal hidden patterns in the images.
Feature Extraction with CNNs: Employ convolutional neural networks (CNNs) to extract high-level features from segmented images. Utilize pre-trained models or create custom architectures with TensorFlow's APIs.
Data Augmentation and Transfer Learning: Enhance model robustness through data augmentation techniques, including rotation, flipping, and variations in contrast. Make use of transfer learning to leverage existing knowledge from larger datasets.
Model Training and Validation: Train your model to distinguish between rust and other imperfections. Use binary cross-entropy loss and optimize with the Adam optimizer. Rigorous validation ensures real-world performance.
Post-Processing and Deployment: Fine-tune predictions through post-processing methods, like filtering out false positives and applying ensemble techniques. Deploy the model to production systems for reliable rust detection.
Contributions are welcome! If you have suggestions, improvements, or new features to add, please create a pull request.
Author : Anurag Roy and Amartya Santra
Documentation : Soumodip Saha