Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions

@article{Chen2021MonitoringOD,
  title={Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions},
  author={Yuhang Chen and Chih-Hong Cheng and Jun Yan and Rongjie Yan},
  journal={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2021},
  pages={6688-6693}
}
While object detection modules are essential functionalities for any autonomous vehicle, the performance of such modules that are implemented using deep neural networks can be, in many cases, unreliable. In this paper, we develop abstraction-based monitoring as a logical framework for filtering potentially erroneous detection results. Concretely, we consider two types of abstraction, namely data-label abstraction and post-algorithm abstraction. Operated on the training dataset, the construction… 
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