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Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery

Authors :
Osio, Anne Achieng
Lê, Hoàng-Ân
Ayugi, Samson
Onyango, Fred
Odwe, Peter
Lefèvre, Sébastien
Publication Year :
2022

Abstract

Deep-learning-based image classification and object detection has been applied successfully to tree monitoring. However, studies of tree crowns and fallen trees, especially on flood inundated areas, remain largely unexplored. Detection of degraded tree trunks on natural environments such as water, mudflats, and natural vegetated areas is challenging due to the mixed colour image backgrounds. In this paper, Unmanned Aerial Vehicles (UAVs), or drones, with embedded RGB cameras were used to capture the fallen Acacia Xanthophloea trees from six designated plots around Lake Nakuru, Kenya. Motivated by the need to detect fallen trees around the lake, two well-established deep neural networks, i.e. Faster Region-based Convolution Neural Network (Faster R-CNN) and Retina-Net were used for fallen tree detection. A total of 7,590 annotations of three classes on 256 x 256 image patches were used for this study. Experimental results show the relevance of deep learning in this context, with Retina-Net model achieving 38.9% precision and 57.9% recall.<br />Comment: Accepted for publication in the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (online from July 2022)

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2204.07096
Document Type :
Working Paper