ALGORITMO PARA A DETECÇÃO DE ÁRVORES URBANAS A PARTIR DE IMAGENS 360

Abstract

Trees are indispensable to human life, they absorb carbon dioxide and release oxygen, protect ecosystems, reduce erosion and help to reduce the environment temperature. The manual identification of trees on public roads requires expense and time for recording and managing the data collected, since urban regions can be very large. We developed in this paper a method for the trees detection in urban areas from a 360 video. A YOLO neural network was trained to detect the trees from frames of the equirectangular video (360 images). We used Computer Vision techniques with the OpenCV library to develop algorithms to segment the regions that fit the detected trees in the rectilinear field of view (gnomonic projection), in order to verify if the trees are on the sidewalks. The results obtained showed around 80% success in detecting trees using YOLO, and an accuracy of 71% in the algorithm that checks if the trees are on the sidewalk.