AutoNaVIT : Vision-Based Path and Obstacle Segmentation Dataset for Autonomous Driving - CSV Compatible

AutoNaVIT is a carefully designed dataset intended to advance research in autonomous navigation, semantic scene understanding, and deep learning-based object segmentation. This release includes only the annotation labels in CSV format, corresponding to high-resolution frames extracted from a driving sequence recorded at Vellore Institute of Technology – Chennai Campus (VIT-C). The corresponding images will be provided in Version 2 of the dataset.

The dataset comprises manually annotated bounding boxes for three key classes that are critical for path planning and perception in autonomous vehicle systems:

Kerb – 1,377 instances

Obstacle – 258 instances

Path – 532 instances

All annotations were generated using Roboflow, with precise, human-verified labeling for consistent, high-quality data—essential for training robust models that generalize well to real-world urban and semi-urban driving scenarios.

Data Capture Specifications
The video footage used for annotation was recorded using a Sony IMX890 camera sensor under stable daylight conditions, with the following details:

Sensor Size: 1/1.56", 50 MP

Lens: 6P optical configuration

Aperture: ƒ/1.8

Focal Length: 24mm equivalent

Pixel Size: 1.0 µm

Features: Optical Image Stabilization (OIS), PDAF autofocus

Video Duration: 4 minutes 11 seconds

Frame Rate: 2 FPS

Total Annotated Frames: 504

Format Compatibility and Model Support
AutoNaVIT’s annotations are made available in standard CSV format, enabling direct compatibility with the following three models:

Multiclass

TensorFlow CSV

RetinaNet

Since CSV is a highly adaptable format, the annotations can be easily modified or reformatted to suit other deep learning models or pipelines that support CSV-based label structures.

Benchmark Results
To validate the dataset's effectiveness, a segmentation model using YOLOv8 was trained with the full dataset (images + annotations). The resulting performance metrics were:

Mean Average Precision (mAP): 96.5%

Precision: 92.2%

Recall: 94.4%

These metrics confirm the dataset’s value in developing perception systems for autonomous vehicles, particularly for object detection and path segmentation tasks.

Disclaimer and Attribution Requirement
By accessing or using this dataset, users agree to the following terms under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0):

The dataset is available for non-commercial academic and research purposes only.

Proper attribution must be included as:
“Dataset courtesy of Vellore Institute of Technology – Chennai Campus.”
This citation must appear in all forms of publication, presentation, or dissemination using this dataset.

Redistribution, commercial usage, public hosting, or modification of the dataset is not permitted without explicit written consent from VIT-C.

Use of the dataset indicates acceptance of these conditions. All rights not explicitly granted are reserved by VIT-C.