Citation

Sey NEN, Amo-Boateng M, Domfeh MK, Kabo-Bah AT, Antwi-Agyei P. Spat. Inf. Res. 2023; 31(5): 501-513.

Copyright

(Copyright © 2023, Korean Spatial Information Society, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s41324-023-00518-0

PMID

unavailable

Abstract

The increasing popularity of drones has led to their adoption by electric utility companies to monitor intrusive vegetation near powerlines. The study proposes a deep learning-based detection framework compatible with drones for monitoring vegetation encroachment near powerlines which estimates vegetation health and detects powerlines. Aerial image pairs from a drone camera and a commercial-grade multispectral sensor were captured and processed into training and validation datasets which were used to train a Generative Adversarial Network (Pix2Pix model) and a Convolutional Neural Network (YoLov5 model). The Pix2Pix model generated satisfactory synthetic image translations from coloured images to Look-Up Table (LUT) maps whiles the YoLov5 obtained good performance for detecting powerlines in aerial images with precision, recall, mean Average Precision (mAP) @0.5, and mAP0.5:0.95 values are 0.82, 0.76, 0.79 and 0.56 respectively. The proposed vegetation detection framework was able to detect locations of powerlines and generate NDVI estimates represented as LUT maps directly from RGB images captured from aerial images which could serve as a preliminary and affordable alternative to relatively expensive multispectral sensors which are not readily available in developing countries for monitoring and managing the presence and health of trees and dense vegetation within powerline corridors.

Language: en

Keywords

Drones; Image-to-image translation; Pix2Pix; Powerlines; Vegetation encroachment; YoLov5