AutoNaVIT : Vision-Based Path and Obstacle Segmentation Dataset for Autonomous Driving - TXT Compatible
收藏资源简介：
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.
AutoNaVIT是一款精心甄选构建的数据集，旨在助力自动驾驶导航、场景理解以及基于深度学习的目标分割领域的研究。本次发布仅包含对应于金奈校区维洛尔理工大学（Vellore Institute of Technology – Chennai Campus，VIT-C）录制的驾驶序列中提取的高分辨率帧的TXT格式标注标签。配套图像将在数据集第二版中尽快推出。 该数据集包含针对自动驾驶导航至关重要的三大核心类别的人工标注边界框与标签： 路缘石（Kerb）：1377个实例 障碍物（Obstacle）：258个实例 通行路径（Path）：532个实例 所有标注均通过Roboflow完成，确保了高保真度与一致性，这对于城市及半城市环境下的实际自动驾驶应用至关重要。 数据采集规格 原始图像由索尼IMX890传感器录制，具体参数如下： 传感器尺寸：1/1.56英寸，5000万像素 镜头：6组镜片，光圈ƒ/1.8，等效焦距24mm，单像素尺寸1.0μm 功能：光学图像防抖（Optical Image Stabilization，OIS）、相位检测自动对焦（PDAF） 视频时长：4分11秒 帧率：2 FPS 总标注帧数：504 格式兼容性与模型支持 AutoNaVIT标注采用标准TXT格式，可直接兼容以下13款模型： yolokeras、yolov4pytorch、darknet、yolov5-obb、yolov8-obb、imt-yolov6、yolov4scaled、yolov5pytorch、yolov7pytorch、yolov8、yolov9、yolov11、yolov12 由于该数据集遵循标准YOLO TXT标注格式，因此可轻松适配其他支持基于TXT格式标注的模型或框架。 基准测试结果 为评估该数据集的性能，研究人员基于完整数据集（图像+标注）训练了一款基于YOLOv8的分割模型，该模型达成如下指标： 平均精度均值（Mean Average Precision，mAP）：96.5% 精确率（Precision）：92.2% 召回率（Recall）：94.4% 上述结果证实了该数据集在训练自动驾驶感知系统的分割模型方面具备极高的实用性与可靠性。 免责声明与署名要求 用户访问或使用本数据集即表示同意《知识共享署名-非商业性使用-禁止演绎4.0国际许可协议》（CC BY-NC-ND 4.0）中的条款： 仅允许将本数据集用于非商业性学术与研究用途。 必须进行恰当署名，声明内容为：“数据集由金奈校区维洛尔理工大学（VIT-C）提供。” 该致谢声明需包含在所有使用本数据集的出版物、演示或成果传播中。 未经金奈校区维洛尔理工大学（VIT-C）明确书面许可，禁止对数据集进行重新分发、商业使用、修改或公开托管。 使用本数据集即视为接受上述条款。本数据集保留未明确授予的所有权利。
