aiqbench analysis
This research advances autonomous navigation by reframing open-set detection as a three-way classification problem, effectively distinguishing between known objects, unknown obstacles, and background clutter without relying on arbitrary confidence thresholds. By addressing domain shifts through generalization and adaptation techniques, the proposed framework demonstrates improved safety and efficiency in scenarios featuring unseen entities or environmental changes. The work represents a significant methodological step forward for robust machine learning systems operating in dynamic, real-world environments.
Three-Way Open-Set Detection for Robust Autonomous Navigation
arXiv:2511.15343v2 Announce Type: replace-cross Abstract: Autonomous navigation in complex scenes requires reliable perception across scenarios that the model did not encounter during its training. Along its route, an autonomous framework encounters objects it was trained to recognize, obstacles it has never seen, and background structures that resemble objects. Each of the three must be handled differently. To tackle this, existing open-set and out-of-distribution detectors discard low-confidence detections with an objectness threshold and only then test the rest for novelty. By forcing a single threshold like this they introduce a trade-off where a low threshold adds background clutter to the detected objects, while a high one may discard needed novel objects. We instead formulate open-set detection as a three-way classification of each detection into known object, unknown object, or background, computed post hoc from the outputs of a pretrained detector. We develop methods for domain generalization and for domain adaptation, evaluated across different detector families and benchmarks up to a combined semantic and covariate domain shift. To test the framework in a navigation context, we conduct simulations parameterized by the measured detection performance. The results show that the three-way decision yields safer and more efficient missions than binary alternatives.
Abstract:Autonomous navigation in complex scenes requires reliable perception across scenarios that the model did not encounter during its training. Along its route, an autonomous framework encounters objects it was trained to recognize, obstacles it has never seen, and background structures that resemble objects. Each of the three must be handled differently. To tackle this, existing open-set and out-of-distribution detectors discard low-confidence detections with an objectness threshold and only then test the rest for novelty. By forcing a single threshold like this they introduce a trade-off where a low threshold adds background clutter to the detected objects, while a high one may discard needed novel objects. We instead formulate open-set detection as a three-way classification of each detection into known object, unknown object, or background, computed post hoc from the outputs of a pretrained detector. We develop methods for domain generalization and for domain adaptation, evaluated across different detector families and benchmarks up to a combined semantic and covariate domain shift. To test the framework in a navigation context, we conduct simulations parameterized by the measured detection performance. The results show that the three-way decision yields safer and more efficient missions than binary alternatives.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
arXiv:2511.15343 [cs.CV]
(or arXiv:2511.15343v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2511.15343 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 293 words
