Deep learning-based framework for vegetation hazard monitoring near powerlines

Abstract

Abstract The increasing popularity in the use of drones has also led to their adoption by electric utility companies to monitor intrusive vegetation near powerlines due to their ability to provide reliable and cost-effective inspections, minimising downtime and improving the efficiency of the monitoring operations of such companies. Besides the lines themselves, the monitoring also involves surrounding objects, most specifically vegetation. Despite the importance of trees and shrubs in maintaining a healthy environment, the growth of vegetation around power transmission lines poses a threat to the public and utility infrastructure itself. The study proposes a deep learning-based detection framework compatible with UAVs for monitoring vegetation encroachment near powerlines which estimates vegetation health and detects powerlines. The framework leverages on computing capability of NVIDIA Jetson Nano to integrate the Pix2Pix model for estimation of vegetation indices and YoLov5 for detection of powerlines from RGB images captured from drones. YoLov5 obtained good performance for detecting powerlines in aerial images with precision, recall, mAP @0.5, and mAP@0.5:0.95 values are 0.821, 0.762, 0.798 and 0.563 respectively. The Pix2Pix model generated satisfactory synthetic image translations from RGB to LUT. 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.