JPL Technical Report Server

Deep Neural Network for Precision Multi-band Infrared Image Segmentation

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dc.contributor.author Lu, Thomas
dc.contributor.author Huyen, Alexander
dc.contributor.author Payumo, Kevin
dc.contributor.author Figueroa, Luis
dc.contributor.author Chow, Edward
dc.contributor.author Torres, Gil
dc.date.accessioned 2020-04-29T15:07:14Z
dc.date.available 2020-04-29T15:07:14Z
dc.date.issued 2018-04-17
dc.identifier.citation SPIE Defense + Commercial Sensing 2018, Orlando, Florida, April 17 - 19, 2018 en_US
dc.identifier.clearanceno 18-1698
dc.identifier.uri http://hdl.handle.net/2014/48128
dc.description.abstract Image segmentation is one of the fundamental steps in computer vision. Separating targets from background clutter with high precision is a challenging operation for both humans and computers. Currently, segmenting objects from IR images is done by tedious manual work. The implementation of a Deep Neural Network (DNN) to perform precision segmentation of multi-band IR video images is presented. A customized pix2pix DNN with multiple layers of generative encoder/decoder and discriminator architecture is used in the IR image segmentation process. Real and synthetic images and ground truths are employed to train the DNN. Iterative training is performed to achieve optimum accuracy of segmentation using a minimal number of training data. Special training images are created to enhance the missing features and to increase the segmentation accuracy of the objects. Retraining strategies are developed to minimize the DNN training time. Single pixel accuracy has been achieved in IR target boundary segmentation using DNNs. The segmentation accuracy between the customized pix2pix DNN and simple thresholding, GraphCut, simple neural network and ResNet models are compared. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2018 en_US
dc.subject deep learning en_US
dc.subject neural network en_US
dc.subject machine learning en_US
dc.subject training data en_US
dc.subject precision segmentation en_US
dc.subject infrared IR target en_US
dc.subject computer vision en_US
dc.title Deep Neural Network for Precision Multi-band Infrared Image Segmentation en_US
dc.type Preprint en_US


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