A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.现有细粒度数据集多聚焦于分类任务，且通常采集于受控拍摄环境，拍摄时相机以目标物体为聚焦中心。本文首次提出面向真实野外场景的细粒度车辆检测（Fine-Grained Vehicle Detection, FGVD）数据集，其采集自搭载于汽车的移动相机。该数据集包含5502张场景图像，涵盖210个独有的细粒度类别标签，涉及多类车辆并采用三级层级结构进行组织。尽管此前的分类数据集已涵盖不同车型的汽车，但本FGVD数据集新增了针对两轮车、机动三轮出租车与卡车的分类类别标签。本FGVD数据集极具挑战性：场景内的车辆处于复杂交通环境中，类别内与类别间在车型、尺度、姿态、遮挡情况以及光照条件上均存在显著差异。当前主流目标检测器（如YOLOv5与Faster R-CNN）在本数据集上表现欠佳，原因在于此类检测器缺乏层级化建模能力。本文不仅给出了现有目标检测器在FGVD数据集上的基准实验结果，还展示了将现有检测器与最新提出的层级残差网络（Hierarchical Residual Network, HRN）分类器相结合后在本任务上的实验结果。最后，实验证明在所有细粒度数据集中，FGVD数据集的车辆图像是分类难度最高的。