A Curated RGB Object Detection Dataset of Urban Electrical Distribution Assets with YOLO Annotations

Open, well-documented datasets are essential for the reproducible development of vision systems for urban utility management. This Data Descriptor presents a curated RGB object-detection benchmark of four classes associated with electrical distribution and street-level utility assets: Inspection Chamber, Overhead-to-Underground Transition, General Protection Box, and Transformer Substation. The public release contains 997 valid image-label pairs partitioned into 698 training, 150 validation, and 149 test images. Images were acquired during 2019 in multiple localities across Spain, predominantly with a mobile phone and, in occasional cases, using Google Maps as a complementary visual source, and were manually annotated with LabelImg before export to YOLO format. During curation, four invalid image-label pairs were removed because at least one YOLO bounding box exceeded the normalized image domain. The benchmark contains 1,939 object instances, with marked class imbalance: General Protection Box accounts for 50.2% of objects whereas Transformer Substation represents 4.7%. Images are heterogeneous in size and viewpoint, ranging from 90 × 170 to 4160 × 4032 pixels, with a median resolution of 619 × 544 pixels and a median of two annotated objects per image. The public GitHub release is organized into images/, labels/, and metadata/ directories; metadata stores split definitions, classes.txt, data.yaml, inventory information, annotation schema documentation, and diagnostic summary figures. Beyond detector benchmarking, the dataset can support scalable mapping of visible distribution-grid assets, with potential value for smart-city digital twins and data-informed EV charging deployment.