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 instancesObstacle – 258 instancesPath – 532 instancesAll 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 SpecificationsSource imagery was recorded using a Sony IMX890 sensor with the following specifications:Sensor Size: 1/1.56", 50 MPLens: 6P, ƒ/1.8, 24mm equivalent, 1.0 µm pixelsFeatures: OIS (Optical Image Stabilization), PDAF autofocusVideo Duration: 4 min 11 secFrame Rate: 2 FPSTotal Annotated Frames: 504Format Compatibility and Model SupportAutoNaVIT annotations are provided in standard TXT format, enabling direct compatibility with the following 13 models:yolokerasyolov4pytorchdarknetyolov5-obbyolov8-obbimt-yolov6yolov4scaledyolov5pytorchyolov7pytorchyolov8yolov9yolov11yolov12As the dataset adheres to standard YOLO TXT annotations, it can easily be adapted for other models or frameworks that support TXT-based annotations.Benchmark ResultsTo 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 RequirementBy 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.