Mr. Lawrence Schirmer Profile

Lawrence Schirmer

at Lockheed Martin Space Systems Co

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Proceedings Article | 12 April 2021 Presentation + Paper

XATR with occlusion and super-resolution

Krystle Beaulieu, Shabab Siddiq, Sundip Desai, Nhat Nguyen, Moses Chan, Timothy Overman, Lawrence Schirmer

Proceedings Volume 11729, 1172904 (2021) https://doi.org/10.1117/12.2585704

KEYWORDS: Super resolution, Detection and tracking algorithms, Target recognition, Automatic target recognition, Transparency, Neural networks, Logic, Image resolution, Head, Convolution

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Standard ATR algorithms suffer from a lack of transparency into why the algorithm recognized a particular object as a target. We present an enhanced Explainable ATR algorithm that utilizes super-resolution networks to provide increased robustness. XATR is a two-level network, with the lower level using Region-based Convolution Neural Networks (R-CNNs) to recognize major parts of the target, known as vocabulary. The upper level employs Markov Logic Networks (MLN) and structure learning to learn the geometric and spatial relationships between the parts in the vocabulary that best describe the objects. Image degradation due to noise, blurring, decimation, etc., can severely impact XATR performance as feature content is irrevocably lost. We address this by introducing a novel super-resolution network. This network uses a dynamic u-net design. A ResNet is on the encoder path while the imagery is reconstructed with dynamically linked upsampling heads in the decoder path. The network is trained on high resolution and degraded imagery pairs to super-resolve the degraded imagery. The trained dynamic u-net then super-resolves unseen degraded imagery to improve XATR’s performance compared to lost performance when using the degraded imagery. In this paper, we perform experiments to 1) Determine the sensitivity of XATR to image corruption 2) Improve XATR performance with super-resolution and 3) Demonstrate XATR robustness to image degradation and occlusion. Our experiments demonstrate improved recall (+40%) and accuracy (+20%) on degraded images when super-resolution is applied.

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