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22 April 2020 Underwater exploration by AUV using deep neural network implemented on FPGA
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Abstract
Performing underwater exploration with Autonomous Underwater Vehicles (AUV) requires low power and high resolution techniques. New computer vision techniques can be used for underwater image classification on embedded devices. These techniques must face machine resource constraints to offer high performance and low power consumption. This paper presents how to implement a Deep Neural Network (DNN) on Field Programmable Gate Array (FPGA) to perform underwater exploration with an AUV. We introduce tools and methodology to adapt the technology to the underwater context. This paper is part of a work to create an embedded system that can fit into an AUV to perform real time analysis of the underwater environment (using video camera as main sensor) with high autonomy and endurance. This will be achieved by overcoming underwater exploration challenges as : low power consumption, high classification performance, shortage of high-quality labeled data to train algorithm.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tanguy Le Pennec, Maher Jridi, Catherine Dezan, Ayman Alfalou, and Franck Florin "Underwater exploration by AUV using deep neural network implemented on FPGA", Proc. SPIE 11400, Pattern Recognition and Tracking XXXI, 114000N (22 April 2020); https://doi.org/10.1117/12.2558606
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