Object Detectors in the Open Environment: Challenges, Solutions, and Outlook

TL;DR

This paper addresses object detection in open environments by introducing a four-quadrant framework (out-of-domain, out-of-category, robust learning, incremental learning) that captures data and target changes over time. It surveys the limitations of current detectors, proposes open-loss formulations, and systematically reviews solution families across each quadrant, accompanied by benchmarks on standard datasets. The work highlights data manipulation, feature learning, and optimization strategies for domain adaptation; discriminant-, side-information-, and arbitrary-information-based approaches for unknown categories; adversarial training and robust inference for malicious data; and replay-, model-, regularization-, and optimization-based strategies for incremental learning. By benchmarking across diverse datasets and outlining future research directions, the paper aims to catalyze safer, more generalizable detectors capable of operating reliably in real-world, dynamic environments.

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 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.

Figures (17)

• Figure 1: The escalating research interest and proportion of studies dedicated to the four major challenges encountered by open environment object detectors over the past six years (search with keywords from the https://arxiv.org/ website).
• Figure 2: The open environment object detector challenge framework, encompasses four quadrants including out-of-domain, out-of-category, robust learning, and incremental learning based on the dimensions of data/target changes.
• Figure 3: Illustration of solution categories for the out-of-domain challenge.
• Figure 4: Methods for addressing out-of-domain challenges.
• Figure 5: Illustration of solution categories for the out-of-category challenge.

Theorems & Definitions (4)

• Definition 1: Domain Adaptive Object Detection
• Definition 2: Unknown Category Object Detection
• Definition 3: Robust Learning on Malicious Data
• Definition 4: Incremental Learning on New Objects