Published April 1, 2022 | Version v1
Dataset Open

Images of Public Streetlights with Operational Monitoring using Computer Vision Techniques

Description

This dataset consists of ~350k JPEG images of streetlight columns installed on a public road infrastructure located in the city of Bristol, UK.

Each streetlight is photographed by a Raspberry Pi Camera Module v1, installed on each lamppost, providing a unique camera placement, photographic angle, and distance from the streetlight. Several streetlights are partially obstructed by vegetation or are outside the Field of View (FoV) of the Raspberry Pi camera. Finally, the cameras facing the sky are susceptible to weather conditions (e.g., rain, snow, direct sunlight, etc.) that can partially or entirely alter the quality of the images taken.

The above provides a unique and diverse dataset of images that can be used for training tools and machine learning models for inspection, monitoring and maintenance use-cases within Smart Cities applications.

Notes

For users willing to test our dataset before downloading the complete one, we provide an example dataset containing a subset of the images (a week of collected images from 17 lighting columns). This can be found inside the "streetcare-dataset-example.zip" file. The complete dataset is found within the "streetcare-dataset-complete.zip" file. A user should download all the available "streetcare-dataset-complete.[z01, z02, z03, z04, zip]" files and later unzip them all at once. For a Linux system, this could be done from the terminal using "zip" & "unzip" tools. Windows and MacOS operating systems do not handle very well split zipped files with the pre-installed standard applications. As an alternative, a user could use 7-zip Utility (https://www.7-zip.org/) for Windows or The Unarchiver (https://theunarchiver.com/) for MacOS. In both cases right-clicking on the "streetcare-dataset-complete.zip" will give a number of options for unzipping all files at once.

Files

README.md

Files (158.2 GB)

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Additional details

Related works

Is supplemented by
Journal article: https://arxiv.org/abs/2203.16915 (URL)