A Novel Automated Tree Detection System using Faster R-CNN

Abstract

This paper introduces an approach to tree detection using mapped aerial images from drones. Tree detection is achieved by training a Faster Region-Based Convolutional Neural Network using Tensorflow Object Detection API. A Color-Based tree detector is also added to further filter out undesired detections such as tree shadows. Aerial images obtained from drones are mapped using Web Open Drone Map, an open-source drone mapping software. This paper aims to achieve a low-cost approach to disaster assessment using tree detection and drone images. The implemented system is able to detect trees and yielded an average detection rate accuracy of 85.63% and 95.14% for stitched maps and unstitched images respectively.