000			01636nam a2200133 4500
008			250624b |||||||| |||| 00| 0 eng d
100			_aBala Naga Jyothi Vandavasi
245			_aAI-based channel infused coral identification and tracking algorithm for autonomous underwater vehicles
300			_aP 683-690
520			_aAutonomous underwater vehicles (AUVs) equipped with in-situ artificial intelligence (AI)-enabled image processing capability could help understand the influence of climate change on reef ecology, and evolve wellinformed policy decisions for sustaining coral reefs. The recent focus is on developing technologies to collect images of coral reefs autonomously over larger areas using AUVs with AI-aided image processing capabilities. This article presents the development of a computer vision and deep-learning-based coral identification and tracking algorithm trained on six distinct families of coral images acquired from the Andaman Islands using a remotely operated vehicle PROVe500 developed by the National Institute of Ocean Technology, Chennai. The YOLOv8-trained coral detection and classification model, along with the ByteTrack, have been used to identify and track the corals with mean average precision and recall rates of 97.4% and 96.8% respectively. The developed AI algorithm gives confidence in deploying AUVs to identify and track corals of interest or anomalies in real-time over a large spatial domain.
654			_aArtificial intelligence, _adetection _aautonomous underwater vehicle _a coral _atracking
773	0		_0125299 _9112524 _tCurrent Science _x 0011-3891
942			_cJA
999			_c131778 _d131778