  With the emergence of foundation models, deep learning-based object detectors
have shown practical usability in closed set scenarios. However, for real-world
tasks, object detectors often operate in open environments, where crucial
factors (e.g., data distribution, objective) that influence model learning are
often changing. The dynamic and intricate nature of the open environment poses
novel and formidable challenges to object detectors. Unfortunately, current
research on object detectors in open environments lacks a comprehensive
analysis of their distinctive characteristics, challenges, and corresponding
solutions, which hinders their secure deployment in critical real-world
scenarios. This paper aims to bridge this gap by conducting a comprehensive
review and analysis of object detectors in open environments. We initially
identified limitations of key structural components within the existing
detection pipeline and propose the open environment object detector challenge
framework that includes four quadrants (i.e., out-of-domain, out-of-category,
robust learning, and incremental learning) based on the dimensions of the data
/ target changes. For each quadrant of challenges in the proposed framework, we
present a detailed description and systematic analysis of the overarching goals
and core difficulties, systematically review the corresponding solutions, and
benchmark their performance over multiple widely adopted datasets. In addition,
we engage in a discussion of open problems and potential avenues for future
research. This paper aims to provide a fresh, comprehensive, and systematic
understanding of the challenges and solutions associated with open-environment
object detectors, thus catalyzing the development of more solid applications in
real-world scenarios. A project related to this survey can be found at
https://github.com/LiangSiyuan21/OEOD_Survey.
