Concrete & Pavement Crack Dataset

Title: Crack Detection in Concrete and Pavement using Convolutional Neural Networks

Summary: This dataset contains 30,000 images of concrete and pavement surfaces, classified into two categories: crack and non-crack. The images were obtained from the Nigerian Army University Biu in Borno state, Nigeria, and collected by Omoebamije Oluwaseun, a civil engineering student, for his final year project. The images were collected using a DJI Mavic 2 Enterprise drone (for the high-ups) and a smartphone (for the ones beneath the average window height). The dataset was saved in RGB, JPEG format and downsized to 227 x 227 pixels.

Content: The dataset consists of two folders: "positive" and "negative", containing images of cracked and non-cracked concrete surfaces, respectively. Each image in the dataset is in JPEG format, with a resolution of 227 x 227 pixels in RGB format.

Usefulness: This dataset can be used for training and testing convolutional neural networks (CNNs) for crack detection in concrete. The dataset has been used by the author to achieve over 98% accuracy on his model, and it can be used for research purposes only. The author must be properly referenced if the dataset is used for any purpose.

Details:

Source: Nigerian Army University Biu, Borno state, Nigeria
Collector: Omoebamije Oluwaseun
Format: RGB, JPEG
Resolution: 227 x 227 pixels
Classes: crack, non-crack
Total images: 30,000