OPEN-SET AUTOMATIC TARGET RECOGNITION

Automatic Target Recognition (ATR) is a category of computer vi- sion 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 Tar- get Recognition framework where we enable open-set recognition capability for ATR algorithms. In addition, we introduce a plu- gin 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 meth- ods on the DSIAC and CIFAR-10 datasets. To the best of our knowledge, this is the first work to address the open-set classifi- cation problem for ATR algorithms. Source code is available at: https://github.com/bardisafa/Open-set-ATR.