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

Ashraf Saleem; Ali Awad; Sidike Paheding; Amy Marcarelli

doi:10.1117/12.2660852

13 June 2023 Multi-class plant type detection in Great Lakes region using remotely operated vehicle and deep learning

Ashraf Saleem, Ali Awad, Sidike Paheding, Amy Marcarelli

Proceedings Volume 12527, Pattern Recognition and Tracking XXXIV; 1252705 (2023) https://doi.org/10.1117/12.2660852
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Ashraf Saleem, Ali Awad, Sidike Paheding, and Amy Marcarelli "Multi-class plant type detection in Great Lakes region using remotely operated vehicle and deep learning", Proc. SPIE 12527, Pattern Recognition and Tracking XXXIV, 1252705 (13 June 2023); https://doi.org/10.1117/12.2660852

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available