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Vitor G. Santos
Federal Institute of Rio Grande do Norte, Parnamirim, RN
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Diego S. Pereira
Federal Institute of Rio Grande do Norte, Parnamirim, RN
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Luis B. P. Nascimento
Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, RN
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Pablo J. Alsina
Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, RN
Keywords:
Boat, Object Detection, Convolutional Neural Network, YOLO, Unmanned Aerial Vehicles, Environmental Protection Area
Abstract
Boats and ships have always been used throughout history as one of the main types of transportation. In recent years, due to the fast evolution of deep learning techniques and online datasets available, convolutional neural networks (CNN) have been widely used for ship and boat detection applications, such as surveillance of marine resources, helping in maritime rescue, monitoring illegal marine activities, among others. In this paper, we present a robust and efficient CNN-based on state-of-the-art YOLO model to perform boat and other water vehicles detection. The training dataset was built considering boats of different sizes, located on the coast and sea and taken with drones and satellites. We also applied data augmentation techniques such as flipping, cropping and changing brightness to increase the number of samples and improve the model robustness. A case study is presented considering a multi Unmanned Aerial Vehicles (UAV) to detect boats in a Coral Reefs Environmental Protection Area (APARC), where human activity is limited. We evaluated the developed system considering a testing dataset with images of the case study, achieving a recognition rate of 87,2% and a mean average precision of 97,23%.
