Detect in Any Scene: An Agentic Framework for Object Detection with Experience-Aware Reasoning
arXiv:2605.31174v1 Announce Type: cross Abstract: Object detection in real-world scenarios remains challenging due to diverse image degradations and heterogeneous object distributions, which significantly hinder the generalization of existing detectors. Conventional approaches, including scene-specific representation learning and end-to-end pipeline design, are inherently limited by their reliance on predefined conditions and lack adaptability to dynamic environments. In this paper, we propose DetAS, an agentic detection framework that formulates object detection as a dynamic decision process.
The increasing complexity and unpredictability of real-world environments necessitate more adaptable and robust AI systems, pushing research towards agentic frameworks that can reason dynamically.
This development represents a significant step towards more generalized and resilient object detection systems, moving beyond predefined conditions to handle diverse and dynamic scenarios, which is crucial for autonomous systems and real-world AI applications.
Object detection is shifting from static, pre-trained models to dynamic, agent-based systems capable of experience-aware reasoning and adaptation, improving performance in challenging and variable conditions.
- · AI research & development
- · Autonomous vehicle developers
- · Robotics industry
- · Surveillance technology providers
- · Developers of static, brittle object detection models
- · Industries reliant on highly controlled environmental conditions for vision syst
- · Companies without R&D in agentic AI
Improved reliability and broader applicability of object detection in untamed real-world environments, accelerating deployment of AI-powered solutions.
Reduced need for extensive re-training or fine-tuning of models for new scenarios, lowering operational costs and increasing the pace of innovation.
Enhanced AI agents (like those in robotics or defense) will gain more autonomous decision-making capabilities due to better situational awareness, potentially leading to fully self-adapting systems.
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Read at arXiv cs.LG