Digital images of defective and good condition tyres

Published: 22 August 2023| Version 1 | DOI: 10.17632/bn7ch8tvyp.1
, Karthick S,


The dataset contains 1854 digital tyres images, categorized into two classes: defective and good condition. Each image is in a digital format and represents a single tyre. The images are labelled based on their condition, i.e., whether the tyre is defective or in good condition. This dataset can be used for various machine learning and computer vision applications, such as image classification and object detection. Researchers and practitioners in transportation, the automotive industry, and quality control can use this dataset to train and test their models to identify the condition of tyres from digital images. The dataset provides a valuable resource to develop and evaluate the performance of algorithms for the automatic detection of defective tyres. The dataset may also help improve the tyre industry's quality control process and reduce the chances of accidents due to faulty tyres. The availability of this dataset can facilitate the development of more accurate and efficient inspection systems for tyre production.


Steps to reproduce

A mobile phone camera with high resolution ( 3000*3000) was used to collect the images with different orientations. The images were collected in a good lighting environment. Collected image samples of tyres, including both defective and good condition tyres, then Labelled based on their condition, i.e., whether it is defective or in good condition. The tyres of small, medium and heavy commercial vehicles were collected. Organize the images and their labels in a structured format


Velammal Engineering College


Materials Science, Computer Vision, Image Processing, Machine Learning, Quality Control, Manufacturing, Automotive Industry