Dr. Amjad Ali Amjad Profile

Dr. Amjad Ali Amjad

at Peking Univ.

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Proceedings Article | 30 May 2022 Presentation + Paper

Tracking of light beacons in highly turbid water and application to underwater docking

Riffat Tehseen Amjad, Mithilesh Mane, Amjad Ali Amjad, Wenmin Ge, Zejun Zhang, Jing Xu

Proceedings Volume 12118, 121180A (2022) https://doi.org/10.1117/12.2617191

KEYWORDS: Image segmentation, Detection and tracking algorithms, Light scattering, Scattering, Light emitting diodes, Image processing, Underwater imaging

Read Abstract +

The use of autonomous underwater vehicles (AUVs), to potentially carry out underwater exploration missions, is limited due to insufficient onboard battery and data storage capacity. To overcome this problem, underwater docking stations are used to provide the facility of underwater charging and data transfer for AUVs. These docking stations are designed to be installed in the dynamic ocean environment, where the turbidity and low-light conditions are key challenges to hinder the successful docking operation. The vision guidance algorithms based on active or passive markers are typically used to precisely guide the AUV towards the docking station. In this paper, we propose a vision-based guidance method, using lock-in detection, to mitigate the effect of turbidity, and to reject the unwanted light sources or noisy luminaries, simultaneously. The lock-in detection method locks on the blinking frequency of light beacons located at the docking station and successfully vanishes the effect of unwanted light at other frequencies. The proposed method uses two light beacons, emitting at a fixed frequency, installed at the simulated docking station and a single CMOS camera. Proof-of-the-concept experiments are performed to show the validity of the proposed approach. The obtained results show that our method is capable of recognizing the light beacons at different turbidity levels, and it can efficiently reject the unwanted light without using separate image processing for this step of the vision-based guidance algorithm. The effectiveness of the proposed method is validated by calculating the true positive rate of the detection method at each turbidity level.

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