Machine learning for longwave infrared image enhancement to improve maneuverability hazard detection

Charley C. Rhea; Stanton R. Price; Stephanie J. Price; Joshua R. Fairley; Steven R. Price

doi:10.1117/12.2585786

12 April 2021 Machine learning for longwave infrared image enhancement to improve maneuverability hazard detection

Charley C. Rhea, Stanton R. Price, Stephanie J. Price, Joshua R. Fairley, Steven R. Price

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117461C (2021) https://doi.org/10.1117/12.2585786
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Vehicle maneuverability is often supported in low-light scenarios through infrared (IR) imagery. However, if the imagery contains little temperature gradient, the raw images are less applicable. In order to maximize image effectiveness, a genetic algorithm (GA) is employed to explore various contrast enhancement operators to determine an optimal sequence of contrast enhancements. We propose a new image quality evaluator that incorporates the performance of a deep learning-based object detector and considers image spatial context through cell-structured configurations. The proposed technique is assessed both qualitatively and quantitatively for the task of maneuverability hazard detection.

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Citation Download Citation

Charley C. Rhea, Stanton R. Price, Stephanie J. Price, Joshua R. Fairley, and Steven R. Price "Machine learning for longwave infrared image enhancement to improve maneuverability hazard detection", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117461C (12 April 2021); https://doi.org/10.1117/12.2585786

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