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（fine-grained dataset）主要聚焦于分类任务，且多采集于受控拍摄场景，即相机以目标物体为拍摄中心。本文提出首个野外自然场景下的细粒度车辆检测（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幅场景图像，涵盖210个唯一细粒度标签，对应多类车辆类型，并采用三级层级结构进行组织。尽管此前的分类数据集也涵盖了不同类型汽车的品牌与型号，但FGVD数据集新增了针对两轮车、机动三轮车（autorickshaw）以及卡车的分类标签。FGVD数据集具有较强挑战性：数据集中的车辆处于复杂交通场景中，存在类内与类间在类型、尺度、姿态、遮挡情况以及光照条件上的多样差异。当前主流目标检测器如YOLOv5与Faster R-CNN在本数据集上表现不佳，原因在于其缺乏层级化建模能力。本文不仅为现有目标检测器在FGVD数据集上的表现提供了基准实验结果，还给出了将现有检测器与最新提出的层级残差网络（Hierarchical Residual Network, HRN）分类器相结合的FGVD任务实验结果。最后，本文证实：在现有细粒度数据集中，FGVD车辆图像的分类难度最高。