MEMS-based serial LiDAR detection and imaging architecture for automated surveillance of undersea marine life

Fraser R. Dalgleish; Bing Ouyang; Anni K. Vuorenkoski; Brian Ramos; Yanjun Li; Zheng Cao; Jose Principe

doi:10.1117/12.2309997

24 May 2018 MEMS-based serial LiDAR detection and imaging architecture for automated surveillance of undersea marine life

Fraser R. Dalgleish, Bing Ouyang, Anni K. Vuorenkoski, Brian Ramos, Yanjun Li, Zheng Cao, Jose Principe

Author Affiliations +

Proceedings Volume 10677, Unconventional Optical Imaging; 1067726 (2018) https://doi.org/10.1117/12.2309997
Event: SPIE Photonics Europe, 2018, Strasbourg, France

Abstract

In situ detection, tracking, localization and identification of undersea marine life in their natural environment is an important aspect of marine biology, fisheries management, ecology and environmental impact studies in the vicinity of undersea infrastructure. However due to the challenging optical characteristics of the underwater environment, mainly due to attenuation and scattering, it is not operationally effective to observe marine life using conventional approaches, such as underwater cameras and lights operating in the visible spectrum. Images often appear dim and blurry and increasing the photon output of the flood lamps or strobes does not solve the issue, instead leading to the formation of image hotspots, and in turbid conditions also reducing image contrast and resolution due to increased back-scattering and blur/glow field effects due to increased forward-scattering. Perhaps more importantly, the introduction of bright broadband lighting into the underwater environment is known to induce behavioral changes in the animals being studied. The MEMS-based serial LiDAR (Light Detection and Ranging) detection and imaging system that was recently developed uses red (638 nm) pulsed laser diode illumination to be invisible and eye-safe to marine animals. Furthermore it has the potential to be very compact, and cost-effective. The equipment is designed for long-term, maintenance-free operations. It generates a sparse primary dataset that only includes detected anomalies, with dense identification-quality dataset being triggered within a scan cycle, thus allowing for efficient, real-time, automated, low bandwidth animal detection, classification and identification. This paper outlines the operating principles of the detection and imaging optical and electronic architecture, with an example of recent results obtain in turbid coastal conditions.

Citation Download Citation

Fraser R. Dalgleish, Bing Ouyang, Anni K. Vuorenkoski, Brian Ramos, Yanjun Li, Zheng Cao, and Jose Principe "MEMS-based serial LiDAR detection and imaging architecture for automated surveillance of undersea marine life", Proc. SPIE 10677, Unconventional Optical Imaging, 1067726 (24 May 2018); https://doi.org/10.1117/12.2309997

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