Bandwidth constrained cooperative object detection in images

Diego Marez; Lena Nans; Samuel Borden

doi:10.1117/12.2636279

28 October 2022 Bandwidth constrained cooperative object detection in images

Diego Marez, Lena Nans, Samuel Borden

Proceedings Volume 12276, Artificial Intelligence and Machine Learning in Defense Applications IV; 122760J (2022) https://doi.org/10.1117/12.2636279
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

Cooperative autonomous systems, such as swarms, multi-camera systems, or fleets of self-driving vehicles, can better understand a given scene by sharing multiple modalities and varying viewpoints. This superposition of data adds robustness and redundancy to a system typically burdened with obstructions and unrecognizable, distant objects. Collaborative perception is a key component of cooperative autonomous systems where modalities can include camera sensors, LiDAR, RADAR, and depth images. Meanwhile, the amount of useful information that can be shared between agents in a cooperative system is constrained by current communication technologies (e.g. bandwidth limitations). Recent developments in learned compression can enable the training of end-to-end cooperative systems using deep learning with compressed communication in the pipeline. We explore the use of a deep learning object detector in a cooperative setting with a learned compression model facilitating communication between agents. To test our algorithm, this research will focus on object detection in the image domain as a proxy for one of the modalities used by collaborative systems.

Conference Presentation

Citation Download Citation

Diego Marez, Lena Nans, and Samuel Borden "Bandwidth constrained cooperative object detection in images", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 122760J (28 October 2022); https://doi.org/10.1117/12.2636279

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