Multi-class plant type detection in Great Lakes region using remotely operated vehicle and deep learning

Document Type

Conference Proceeding

Publication Date

2023

Department

Department of Applied Computing; Department of Biological Sciences

Abstract

Underwater plants can alter the biodiversity and function of aquatic ecosystems, leading to significant positive or negative environmental impacts. In the Great Lakes region, many efforts have been made to early identify and map underwater plants for cost-effective monitoring, management, and containment of potentially invasive species. However, traditional patrolling and visual inspection methods are time-consuming and labor-intensive. Recent advances in sensors, computing platforms, and machine learning algorithms enabled unprecedented achievements in various applications related to marine biodiversity and aquatic ecosystem. In this work, we use an ROV to collect a new underwater plant detection dataset comprising 414 underwater images of three main categories of aquatic plants, namely Leafy, Bushy, and Tapey at different lakes in the Upper Peninsula, Michigan, USA. Then we present a comparative analysis of the performance of common object detectors, including YOLOv8, Faster R-CNN, and RetinaNet on our dataset. The acquired results demonstrate the potential of such pre-trained detectors in detecting underwater plants in noisy images that are acquired by ROVs and building fully automated plant detection, mapping, and management systems.

Publication Title

Proceedings Volume 12527, Pattern Recognition and Tracking XXXIV

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