Automatic target recognition (ATR) technology is likely to play an increasingly prevalent role in maintaining situational awareness in the modern battlefield. Progress in deep learning has enabled considerable progress in the development of ATR algorithms; however, these algorithms require large amounts of high-quality annotated data to train and that is often the main bottleneck. Synthetic data offers a potential solution to this problem, especially given recent proliferation of tools and techniques to synthesize custom data. Here, we focus on ATR, in the visible domain, from the perspective of a small drone, which represents a domain of growing importance to the Army. We describe custom simulators built to support synthetic data for multiple targets in a variety of environments. We describe a field experiment where we compared a baseline (YOLOv5) model, trained on off-the-shelf large generic public datasets, with a model augmented with specialized synthetic data. We deployed the models on a VOXL platform in a small drone. Our results showed a considerable boost in performance when using synthetic data of over 40% in target detection accuracy (average precision with at least 50% overlap). We discuss the value of synthetic data for this domain, the opportunities it creates, but also the novel challenges it introduces.
Deep learning has expedited important breakthroughs in research and commercial applications for next-generation technologies across many domains including Automatic Target Recognition (ATR). The success of these models in a specific application is often attributed to optimized hyperparameters: user-configured values controlling the model’s ability to learn from data. Tuning hyperparameters however remains a difficult and computationally expensive task contributing to deficient ATR model performance compared to set requirements. We present the efficacy of applying our developed hyperparameter optimization method to boost the effectiveness and performance of any given optimization method. Specifically, we use a generalized additive model surrogate homotopy hyperparameter optimization strategy to approximate regions of interest and trace minimal points over regions of the hyperparameter space instead of ineffectively evaluating the entire hyperparameter surface. We integrate our approach into SHADHO (Scalable Hardware-Aware Distributed Hyperparameter Optimization) a hyperparameter optimization framework that computes the relative complexity of each search space and then monitors the performance of the learning task over the trials. We demonstrate how our approach effectively finds optimal hyperparameters for object detection by conducting a model search to optimize multiple object detection algorithms on a subset of the DSIAC ATR Algorithm Development Image Database and finding models that achieve comparable or lower validation loss in fewer iterations than standard techniques and manual tuning practices.
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