crack_detection

🧠 Concrete Crack Detection Using InceptionV3

Crack Detection in Concrete Surfaces Using Deep Learning This repository contains the clean and optimized TensorFlow/Keras implementation of a crack detection system for concrete structures using the InceptionV3 convolutional neural network. The model is trained to classify high-resolution images of concrete surfaces as Crack or No-Crack, enabling accurate structural health monitoring for bridges, buildings, and civil infrastructure.

The system is based on concepts from “Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks”, but upgraded with transfer learning, improved preprocessing, and a lightweight inference pipeline suitable for Jetson Nano ,Raspberry Pi, Edge devices, and real-time monitoring systems.

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📌 Features

• ✔ Transfer learning with InceptionV3
• ✔ High accuracy crack / no-crack classification
• ✔ Preprocessed dataset with data augmentation
• ✔ Exportable model (.h5 and TFLite)
• ✔ Ready for real-time deployment
• ✔ Lightweight inference script included

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📦 Dependencies required:-

TensorFlow/Keras, SimpleITK, Dataset - The dataset can be downloaded or use the dataset using Kaggle api keys from this link- https://www.kaggle.com/datasets/arunrk7/surface-crack-detection

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📊 Dataset:-

Surface Crack Detection Dataset

Context Concrete surface cracks are major defect in civil structures. Building Inspection which is done for the evaluation of rigidity and tensile strength of the building. Crack detection plays a major role in the building inspection, finding the cracks and determining the building health.

Content The datasets contains images of various concrete surfaces with and without crack. The image data are divided into two as negative (without crack) and positive (with crack) in separate folder for image classification. Each class has 20000images with a total of 40000 images with 227 x 227 pixels with RGB channels. The dataset is generated from 458 high-resolution images (4032x3024 pixel) with the method proposed by Zhang et al (2016). High resolution images found out to have high variance in terms of surface finish and illumination condition. No data augmentation in terms of random rotation or flipping or tilting is applied.

Acknowledgements This dataset is taken from the website Mendeley Data - Crack Detection, contributed by Çağlar Fırat Özgenel.

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📋 Note:-

• Use Tensorflow to load the crack_detection.h5 model.