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

AutoNaVIT is a meticulously curated dataset developed to assist research in autonomous navigation, scene understanding, and deep learning-based object segmentation. This release contains only the annotation labels in TXT format corresponding to high-resolution frames extracted from a recorded driving sequence at Vellore Institute of Technology – Chennai Campus (VIT-C). The corresponding images will be made available in Version 2 of the dataset soon.

The dataset features manually annotated bounding boxes and labels for three essential classes critical for autonomous vehicle navigation:

Kerb – 1,377 instances

Obstacle – 258 instances

Path – 532 instances

All annotations were created using Roboflow, ensuring high fidelity and consistency, which is vital for real-world autonomous driving applications in both urban and semi-urban environments.

Data Capture Specifications
Source imagery was recorded using a Sony IMX890 sensor with the following specifications:

Sensor Size: 1/1.56", 50 MP

Lens: 6P, ƒ/1.8, 24mm equivalent, 1.0 µm pixels

Features: OIS (Optical Image Stabilization), PDAF autofocus

Video Duration: 4 min 11 sec

Frame Rate: 2 FPS

Total Annotated Frames: 504

Format Compatibility and Model Support
AutoNaVIT annotations are provided in standard TXT format, enabling direct compatibility with the following 13 models:

yolokeras

yolov4pytorch

darknet

yolov5-obb

yolov8-obb

imt-yolov6

yolov4scaled

yolov5pytorch

yolov7pytorch

yolov8

yolov9

yolov11

yolov12

As the dataset adheres to standard YOLO TXT annotations, it can easily be adapted for other models or frameworks that support TXT-based annotations.

Benchmark Results
To evaluate the dataset’s performance, a YOLOv8-based segmentation model was trained on the complete dataset (images + annotations). The model achieved:

Mean Average Precision (mAP): 96.5%

Precision: 92.2%

Recall: 94.4%

These results confirm the dataset's high utility and reliability in training segmentation models for autonomous vehicle perception systems.

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

Usage is permitted solely for non-commercial academic and research purposes.

Proper attribution must be given, stating:
“Dataset courtesy of Vellore Institute of Technology – Chennai Campus.”
This acknowledgment must be included in all forms of publication, presentation, or dissemination of work utilizing this dataset.

Redistribution, commercial use, modification, or public hosting of the dataset is prohibited without explicit written permission from VIT-C.

Use of this dataset implies acceptance of these terms. All rights not explicitly granted are reserved by VIT-C.