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Open AccessArticle

Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs

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Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
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Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
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Department of Computer Engineering, Dom Bosco Catholic University, Campo Grande 79117-900, Brazil
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CPAQ, Federal University of Mato Grosso do Sul, Aquidauana 79200-000, Brazil
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Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, 01062 Dresden, Germany
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Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil
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Department of Forest Engineering, Santa Catarina State University, Lages 88520-000, Brazil
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(16), 3595; https://doi.org/10.3390/s19163595
Received: 11 June 2019 / Revised: 11 July 2019 / Accepted: 19 July 2019 / Published: 18 August 2019
Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds. View Full-Text
Keywords: object-detection; deep learning; remote sensing object-detection; deep learning; remote sensing
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Santos, A.A.; Marcato Junior, J.; Araújo, M.S.; Di Martini, D.R.; Tetila, E.C.; Siqueira, H.L.; Aoki, C.; Eltner, A.; Matsubara, E.T.; Pistori, H.; Feitosa, R.Q.; Liesenberg, V.; Gonçalves, W.N. Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs. Sensors 2019, 19, 3595.

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