Vegetation encroachment in power line corridors has multiple problems for modern energy-dependent societies. Failures due to the contact between power lines and vegetation can result in power outages and millions of dollars in losses. To address this problem, UAVs have emerged as a promising solution due to their ability to quickly and affordably monitor long corridors through autonomous flights or being remotely piloted. However, the extensive and manual task that requires analyzing every image acquired by the UAVs when searching for the existence of vegetation encroachment has led many authors to propose the use of Deep Learning to automate the detection process. Despite the advantages of using a combination of UAV imagery and Deep Learning, there is currently a lack of datasets that help to train Deep Learning models for this specific problem. This paper presents a dataset for the semantic segmentation of vegetation encroachment in power line corridors. RGB orthomosaics were obtained for a rural road area using a commercial UAV. The dataset is composed of pairs of tessellated RGB images, coming from the orthomosaic and corresponding multi-color masks representing three different classes: vegetation, power lines, and the background. A detailed description of the image acquisition process is provided, as well as the labeling task and the data augmentation techniques, among other relevant details to produce the dataset. Researchers would benefit from using the proposed dataset by developing and improving strategies for vegetation encroachment monitoring using UAVs and Deep Learning.

Vegetation encroachment in power line corridors remains a major challenge for modern energy-dependent societies, as it can cause power outages and lead to significant financial losses. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for monitoring infrastructure, owing to their ability to acquire high-resolution overhead images of these areas quickly and affordably. However, accurate segmentation of the vegetation encroachment in this imagery is a challenging task, due to the complexity of the scene and the high pixel imbalance between the power lines, the vegetation and the background classes. In this paper, we propose a deep learning-based approach to tackle this problem caused by the original and different geometry of the objects. Specifically, we use DeepLabV3, U-Net and a modified version of the U-Net architecture with VGG-16 weights to train two separate models. One of them segments the dominant classes, the vegetation from the background, achieving an IoU of 0.77. The other one segments power line corridors from the background, obtaining an IoU of 0.64. Finally, ensembling both models into one creates an “encroachment” zone, where power lines and vegetation are intersected. We train our models using the Vegetation Encroachment in Power Line Corridors dataset (VEPL), which includes RGB orthomosaics and multi-label masks for segmentation. Experimental results demonstrate that our approach outperforms individual networks and original prominent architectures when applied to this specific problem. This approach has the potential to significantly improve the efficiency and accuracy of vegetation encroachment monitoring using UAV, thus helping to ensure the reliability and sustainability of power supply.