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Computer vision (CV) algorithms have improved tremendously with the application of neural network-based approaches. For instance, Convolutional Neural Networks (CNNs) achieve state of the art performance on Infrared (IR) detection and identification (e.g., classification) problems. To train such algorithms, however, requires a tremendous quantity of labeled data, which are less available in the IR domain than for “natural imagery”, and are further less available for CV-related tasks. Recent work has demonstrated that synthetic data generation techniques provide a cheap and attractive alternative to collecting real data, despite a “realism gap” that exists between synthetic and real IR data.
In this work, we train deep models on a combination of real and synthetic IR data, and we evaluate model performance on real IR data. We focus on the tasks of vehicle and person detection, object identification, and vehicle parts segmentation. We find that for both detection and object identification, training on a combination of real and synthetic data performs better than training only on real data. This classification improvement demonstrates an advantage to using synthetic data for computer vision. Furthermore, we believe that the utility of synthetic data – when combined with real data – will only increase as the realism gap closes.
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