arXiv:2606.31834v1 Announce Type: cross Abstract: Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detection (SFOD) methods rely on heavyweight architectures that prioritize accuracy alone. We show this trade-off is unnecessary: building on YOLOv10, an NMS-free dual-head detector, we achieve state-of-the-art adaptation accuracy while being faster and more compact. We observe that directly applying vanilla mean-teacher self-training to dual-head detectors leads to subopt
The rapid advancement of deep learning models for computer vision is driving innovation in optimizing these models for real-world deployment challenges.
This research demonstrates that high accuracy in AI models for critical applications like autonomous driving and robotics can be achieved without sacrificing efficiency, addressing a major bottleneck for deployment.
The trade-off between AI model accuracy and real-time performance, particularly in source-free domain adaptation, is being mitigated, making advanced AI practical for resource-constrained environments.
- · Autonomous driving companies
- · Robotics manufacturers
- · Surveillance technology providers
- · Edge AI hardware developers
- · Developers of inefficient AI models
- · Companies relying on heavy cloud inference for real-time tasks
More sophisticated and reliable AI systems will be deployed in real-time applications requiring low latency and memory.
Increased adoption of AI in previously constrained domains will accelerate innovation and market growth in autonomous systems.
The enhanced capability of source-free object detection could lead to faster adaptation of AI to new environments, potentially increasing the pace of technological obsolescence for older systems.
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Read at arXiv cs.AI