  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 115,354 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.
