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Many problems in defense and automatic target recognition (ATR) require concurrent detection and classification of objects of interest in wide field-of-view overhead imagery. Traditional machine learning approaches are optimized to perform either detection or classification individually; only recently have algorithms expanded to tackle both problems simultaneously. Even highly performing parallel approaches struggle to disambiguate tightly clustered objects, often relying on external techniques such as non-maximum suppression. We have developed a hybrid detection-classification approach that optimizes the segmentation of closely spaced objects, regardless of size, shape, and object diversity. This improves overall performance for both the detection and classification problems. |
