Object Detectors in the Open Environment: Challenges, Solutions, and Outlook
Siyuan Liang, Wei Wang, Ruoyu Chen, Aishan Liu, Boxi Wu, Ee-Chien, Chang, Xiaochun Cao, and Dacheng Tao

TL;DR
This paper provides a comprehensive review of object detectors in open environments, analyzing their unique challenges, solutions, and future research directions to enable more reliable real-world applications.
Contribution
It introduces a novel open environment object detector challenge framework with four quadrants and systematically reviews solutions and benchmarks for each.
Findings
Identified limitations in existing detection pipeline components.
Proposed a four-quadrant challenge framework for open environments.
Benchmarking results over multiple datasets.
Abstract
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…
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Taxonomy
TopicsInfrared Target Detection Methodologies
MethodsSparse Evolutionary Training
