Object detection in conditional GAN transferred sensor images

Amit Bhatia

doi:10.1117/12.2566885

20 August 2020 Object detection in conditional GAN transferred sensor images

Amit Bhatia

Proceedings Volume 11511, Applications of Machine Learning 2020; 1151108 (2020) https://doi.org/10.1117/12.2566885
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Object detection is a central theme for many Artificial Intelligence (AI) applications such as autonomous vehicles, surveillance etc. The algorithms providing this capability rely on training data being available in corresponding sensor mode. Having co-located data from multiple sensor modes enhances the detection confidence, but the availability of training data in desired sensor mode is not always readily available, which slows down progress. In this paper, we investigate the ability to translate images from one sensor mode to another, on a single fixed camera dataset, using conditional Generative Adversarial Network (cGAN). Specifically, images are transferred from Electro-Optical (EO) to Infra-Red (IR) images and vice-versa using cGAN models, which are generative models that learn the data distribution in a minimax game setting. To investigate the usability of such transferred images, we apply object detection algorithm on ground truth and transferred images and compare their performance. The results indicate that transferred images match closely to real images and object detection has good performance on transferred images, especially when the object size is large.

Conference Presentation

Citation Download Citation

Amit Bhatia "Object detection in conditional GAN transferred sensor images", Proc. SPIE 11511, Applications of Machine Learning 2020, 1151108 (20 August 2020); https://doi.org/10.1117/12.2566885

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