Vehicles, pedestrians, and riders are the most important and interesting objects for the perception modules of self-driving vehicles and video surveillance. However, the state-of-the-art performance of detecting such important objects (esp. small objects) is far from satisfying the demand of practical systems. Large-scale, rich-diversity, and high-resolution datasets play an important role in developing better object detection methods to satisfy the demand. Existing public large-scale datasets such as MS COCO collected from websites do not focus on the specific scenarios. Moreover, the popular datasets ( e.g., KITTI and Citypersons) collected from the specific scenarios are limited in the number of images and instances, the resolution, and the diversity. To attempt to solve the problem, we build a diverse high-resolution dataset (called TJU-DHD). The dataset contains 115354 high-resolution images (52% images have a resolution of $1624\times 1200$ pixels and 48% images have a resolution of at least 2, $560\times 1.440$ pixels) and 709 330 labeled objects in total with a large variance in scale and appearance. Meanwhile, the dataset has a rich diversity in season variance, illumination variance, and weather variance. In addition, a new diverse pedestrian dataset is further built. With the four different detectors ( i.e., the one-stage RetinaNet, anchor-free FCOS, two-stage FPN, and Cascade R-CNN), experiments about object detection and pedestrian detection are conducted. We hope that the newly built dataset can help promote the research on object detection and pedestrian detection in these two scenes. The dataset is available at https://github.com/tjubiit/TJU-DHD .

中文翻译：

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TJU-DHD：用于对象检测的多种高分辨率数据集

对于自动驾驶汽车和视频监控的感知模块而言，车辆，行人和骑行者是最重要和最有趣的对象。但是，检测此类重要对象（尤其是小对象）的最新性能远远不能满足实际系统的需求。大规模，丰富多样和高分辨率的数据集在开发更好的对象检测方法以满足需求方面起着重要作用。从网站收集的现有公共大规模数据集（例如MS COCO）并不关注特定场景。此外，流行的数据集（ 例如，从特定场景收集的KITTI和Citypersons的图像和实例数量，分辨率和多样性受到限制。为了尝试解决该问题，我们构建了一个多样化的高分辨率数据集（称为TJU-DHD）。数据集包含115354个高分辨率图像（52％图像的分辨率为 $ 1624 \次1200 $ 像素和48％的图像的分辨率至少为2， $ 560 /次1.440 $ 像素）和709 330个标记的对象，它们在比例和外观上都有很大的差异。同时，该数据集在季节方差，照度方差和天气方差方面具有丰富的多样性。此外，进一步建立了新的多样化行人数据集。使用四个不同的探测器（ 即一阶段RetinaNet，免锚FCOS，两阶段FPN和Cascade R-CNN），进行了目标检测和行人检测的实验。我们希望新建的数据集能够帮助促进这两个场景中物体检测和行人检测的研究。数据集位于https://github.com/tjubiit/TJU-DHD 。