Open-Set Automatic Target Recognition
This addresses the robustness issue for ATR in military and surveillance applications by enabling recognition of unknown classes, though it is incremental as it builds on existing open-set methods.
The paper tackles the problem of closed-set limitations in Automatic Target Recognition (ATR) algorithms by proposing an open-set framework with a Category-aware Binary Classifier (CBC) module, achieving improved performance over existing open-set methods on DSIAC and CIFAR-10 datasets.
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are extensively used in real-world scenarios such as military and surveillance applications. Existing ATR algorithms are developed for traditional closed-set methods where training and testing have the same class distribution. Thus, these algorithms have not been robust to unknown classes not seen during the training phase, limiting their utility in real-world applications. To this end, we propose an Open-set Automatic Target Recognition framework where we enable open-set recognition capability for ATR algorithms. In addition, we introduce a plugin Category-aware Binary Classifier (CBC) module to effectively tackle unknown classes seen during inference. The proposed CBC module can be easily integrated with any existing ATR algorithms and can be trained in an end-to-end manner. Experimental results show that the proposed approach outperforms many open-set methods on the DSIAC and CIFAR-10 datasets. To the best of our knowledge, this is the first work to address the open-set classification problem for ATR algorithms. Source code is available at: https://github.com/bardisafa/Open-set-ATR.