Closely spaced object segmentation using a hybrid deep learning approach

Matthew D. Reisman; Jonathan J. Dalrymple; Adam G. Francisco; Latisha Konz; Timothy L. Overman

doi:10.1117/12.2586991

12 April 2021 Closely spaced object segmentation using a hybrid deep learning approach

Matthew D. Reisman, Jonathan J. Dalrymple, Adam G. Francisco, Latisha Konz, Timothy L. Overman

Proceedings Volume 11729, Automatic Target Recognition XXXI; 117290J (2021) https://doi.org/10.1117/12.2586991
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Matthew D. Reisman, Jonathan J. Dalrymple, Adam G. Francisco, Latisha Konz, and Timothy L. Overman "Closely spaced object segmentation using a hybrid deep learning approach", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290J (12 April 2021); https://doi.org/10.1117/12.2586991

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