Multi-instance vehicle dataset with annotations with captured in outdoor diverse settings

We collected and annotated a dataset containing 105,544 annotated vehicle instances from 24700 image frames within seven different videos, sourced online under creative commons license. The video frames are annotated using DarkLabel tool. In the interest of reusability and generalisation of the deep learning model, we consider the diversity within the collected dataset. This diversity includes changes of lighting amongst the video, as well as other factors such as weather conditions, angle of observation, varying speed of the moving vehicles, traffic flow, and road conditions etc. The videos collected obviously include stationary vehicles, to perform the validation of stopped vehicle detection method. It can be noticed that the road conditions (e.g., motorways, city, country roads), directions, data capture timings and camera views, vary in the dataset producing annotated dataset with diversity. the dataset may have several uses such as vehicle detection, vehicle identification, stopped vehicle detection on smart motorways and local roads (smart city applications) and many more.本研究收集并标注了一个数据集，该数据集包含来自7段不同视频的24700帧图像中的105544个车辆标注实例，所有素材均在知识共享许可（Creative Commons）协议下从网络获取。该数据集的视频帧采用DarkLabel标注工具完成标注。为提升深度学习模型的可复用性与泛化能力，本数据集在构建过程中充分考量了样本多样性：此类多样性涵盖视频内的光照变化，同时还包含天气状况、观测角度、移动车辆的行驶速度、交通流量以及道路状况等多种影响因素。采集的视频样本中包含静止车辆，用于开展静止车辆检测方法的验证工作。本数据集涵盖了不同的道路场景（例如高速公路、城市道路、乡村道路）、行驶方向、数据采集时间以及相机视角，进一步丰富了标注数据集的多样性。该数据集可应用于多个任务场景，例如车辆检测、车辆识别、智慧高速公路与城市道路的静止车辆检测（智慧城市应用）等诸多领域。