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arXiv · 2010.15509

Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors

Jawad N. Yasin·Sherif A. S. Mohamed·Mohammad-hashem Haghbayan·Jukka Heikkonen·Hannu Tenhunen·Muhammad Mehboob Yasin·Juha Plosila
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Abstract

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 $dB$. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.

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Jawad N. Yasin, Sherif A. S. Mohamed, Mohammad-hashem Haghbayan, Jukka Heikkonen, Hannu Tenhunen, Muhammad Mehboob Yasin, Juha Plosila. 2020-10-29. Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors. https://doi.org/10.1109/sensors47125.2020.9278914

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