Data generation for nighttime infrared scenes using enhanced domain migration learning

Yi Su; Jing Lu; Jia Ming

doi:10.1117/12.3032023

10 October 2024 Data generation for nighttime infrared scenes using enhanced domain migration learning

Yi Su, Jing Lu, Jia Ming

Proceedings Volume 13278, Seventh Global Intelligent Industry Conference (GIIC 2024); 1327804 (2024) https://doi.org/10.1117/12.3032023
Event: Seventh Global Intelligent Industry Conference (GIIC 2024), 2024, Shenzhen, China

Abstract

In nighttime infrared scenarios, data generation plays a pivotal role. However, the limited availability of nighttime infrared image datasets often constrains the model's generalization ability and accuracy. To mitigate this challenge, this study proposes a novel method for generating infrared image data by integrating adversarial training techniques with foreground enhancement methods. A foreground enhancement algorithm is employed to process visible light images, thereby enhancing the sensitivity of visible light images to targets during the CYCLEGAN model generation process. This approach effectively improves the quality of the generated nighttime infrared images and enhances the performance of target detection in such images, resulting in notable enhancements. Experimental results on publicly available infrared image datasets demonstrate that training with augmented data can significantly improve the performance of target detection models, providing a new solution for scenarios with limited nighttime infrared image data.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yi Su, Jing Lu, and Jia Ming "Data generation for nighttime infrared scenes using enhanced domain migration learning", Proc. SPIE 13278, Seventh Global Intelligent Industry Conference (GIIC 2024), 1327804 (10 October 2024); https://doi.org/10.1117/12.3032023

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