| Title | An Object Detection Model Robust to Out-of-Distribution Data |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Park, Ho-rim, Hwang, Kyu-hong, Ha, Young-guk |
| Conference Name | 2021 IEEE International Conference on Big Data and Smart Computing (BigComp) |
| Keywords | Big Data, composability, Computational modeling, Conferences, Data models, Deep Learning, False Data Detection, Human Behavior, Neural networks, object detection, out-of-distribution data, pubcrawl, resilience, Resiliency, Uncertainty |
| Abstract | Most of the studies of the existing object detection models are studies to better detect the objects to be detected. The problem of false detection of objects that should not be detected is not considered. When an object detection model that does not take this problem into account is applied to an industrial field close to humans, false detection can lead to a dangerous situation that greatly interferes with human life. To solve this false detection problem, this paper proposes a method of fine-tuning the backbone neural network model of the object detection model using the Outlier Exposure method and applying the class-specific uncertainty constant to the confidence score to detect the object. |
| DOI | 10.1109/BigComp51126.2021.00057 |
| Citation Key | park_object_2021 |
An Object Detection Model Robust to Out-of-Distribution Data