Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Concrete Crack Images dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.
INTRODUCTION: In this study, the research team developed a computerized vision system to recognize whether there are cracks on concrete surfaces. The dataset contains concrete images with different surface finishes and illumination conditions. The photos were collected from various college campus buildings. The dataset is divided into two classes, negative and positive, and each type has 20,000 images with 227 x 227 pixels.
ANALYSIS: The DenseNet201 model’s performance achieved an accuracy score of 99.87% after 5 epochs using a separate validation dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 99.86%.
CONCLUSION: In this iteration, the TensorFlow DenseNet201 CNN model appeared suitable for modeling this dataset.
Dataset ML Model: Binary-Class classification with numerical features
Dataset Used: Özgenel, Çağlar Fırat (2019), “Concrete Crack Images for Classification”, Mendeley Data, V2, doi: 10.17632/5y9wdsg2zt.2
Dataset Reference: https://data.mendeley.com/datasets/5y9wdsg2zt/2
One source of potential performance benchmarks: https://www.kaggle.com/datasets/arunrk7/surface-crack-detection
The HTML formatted report can be found here on GitHub.