… object detection in autonomous driving scenarios are discussed in this section. Section IV
introduces deep learning (DL) and the factors that make DL a powerful technique in computer …

… The goal of object detection is to detect all instances of objects from one or several known
classes, such as people, cars, or faces in an image. Typically only a small number of objects …

… In addition, we identify major open issues regarding the … Although we restrict our attention
to object detection in still … 3) We present and discuss open issues at the problemlevel and …

… Finally, we provide valuable insights of challenges and open issues. … of existing methods
on three open datasets. Section VI discusses open issues and Section VII concludes this work. …

… Finally, we discuss several open issues of SOD and outline future research directions. All
the saliency prediction maps, our constructed dataset with annotations, and codes for …

… In this paper, we give a comprehensive survey of recent advances in visual object detection
… object detection frameworks and organize the survey into three major parts: (i) detection …

… Some of the recent surveys focus directly on generic object detection issues rather than …
and evolve several environmental changes to detect objects in the open world. The future …

… framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises
… To alleviate these issues, we introduce a foreground objectness branch Fobj : RD → [0, 1] …

… layer is to fix the misalignment issues in RoI pooling layer. It … high accuracy, yet there are
some issues related to real-time … Open Images v5 is a standard dataset comprising 1.9 million …

… the unknown object detection capabilities of an object detector. … that helps improve the
unknown object detection. Next, we … • Identifies and addresses two inherent issues in road scene …