DOI: 10.5281/zenodo.18073448
title: Indian Toll-Plaza Dataset

description: Indian Toll-Plaza (ITP) Dataset Description

The Indian Toll-Plaza (ITP) dataset is a large-scale, real-world visual dataset designed to support research on toll-plaza detection, tracking, and perception tasks for autonomous driving systems. The dataset addresses the lack of publicly available, task-specific data for toll-plaza environments.

Dataset Overview

The ITP dataset contains a total of 12,500 high-resolution image frames, comprising:





2,500 manually annotated toll-plaza images, and




10,000 non-toll-plaza images representing diverse road scenes.



Scene Diversity and Data Collection

To capture the complexity of real-world driving environments, data are collected across:





Road types: highways, urban roads, rural roads, and campus roads




Geographical regions: North, South, East, West, and Central India




Cities and highways: Jaipur, Delhi, Patna, Bhopal, Jalandhar, and surrounding national and state highways




Time conditions: morning, evening, night, dawn, and dusk




Weather conditions: sunny, cloudy, rainy, foggy, smoky, and snowy




Traffic densities: crowded, normal, and low



All images are stored in PNG format to preserve visual quality and annotation are saved in TXT file format.

Annotation Protocol

Manual annotations are performed only for toll-plaza images, focusing on bounding-box level labels suitable for object detection tasks. The remaining non-toll-plaza images are provided as background data to support binary classification, imbalance analysis, and robust detector training. Additionally, a large portion of unlabeled data is included to facilitate semi-supervised and self-supervised learning research.

Dataset Splits

For effective model training and evaluation, the dataset is divided into training, validation, and testing subsets using a 70% / 20% / 10% split, respectively. The split is performed at the road level, ensuring that all images from a specific road appear in only one subset. This strategy prevents data leakage and enhances model generalization.

Two experimental settings are supported:





Balanced setup:





2,500 toll-plaza images




2,500 non-toll-plaza images






Unbalanced setup:





2,500 toll-plaza images




10,000 non-toll-plaza images





These configurations allow systematic evaluation of model robustness under realistic class-imbalance scenarios.

Each split maintains diversity across:





Road categories (state and national highways)




Geographic regions




Weather and illumination conditions



Benchmark Evaluation

The dataset is benchmarked using a YOLOv7-based detection model under both balanced and unbalanced class distributions. Experimental results demonstrate improved precision and reliable toll-plaza detection performance, highlighting the dataset’s suitability for autonomous driving perception tasks. The ITP dataset plays a crucial role in understanding lane-choice behavior, safe stopping, and traffic management at toll plazas.

Future Extensions

Future versions of the ITP dataset will include:





Detailed annotations for traffic signs, lane markings, and road infrastructure




Pixel-level semantic segmentation labels




Extended annotations to support multi-task learning and scene understanding



Additionally, specialized deep neural network-based detectors will be developed to leverage these annotations and further enhance autonomous-driving perception capabilities.

 
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