TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines
This work addresses the need for accurate detection and segmentation to protect power-grid security and UAV safety, but it is incremental as it primarily introduces a new dataset.
The authors tackled the problem of detecting and segmenting transmission towers and power lines from aerial images by collecting and releasing the TTPLA dataset, which includes 1,100 images with 8,987 manually labeled instances, and reported baseline performance of state-of-the-art models on this dataset.
Accurate detection and segmentation of transmission towers~(TTs) and power lines~(PLs) from aerial images plays a key role in protecting power-grid security and low-altitude UAV safety. Meanwhile, aerial images of TTs and PLs pose a number of new challenges to the computer vision researchers who work on object detection and segmentation -- PLs are long and thin, and may show similar color as the background; TTs can be of various shapes and most likely made up of line structures of various sparsity; The background scene, lighting, and object sizes can vary significantly from one image to another. In this paper we collect and release a new TT/PL Aerial-image (TTPLA) dataset, consisting of 1,100 images with the resolution of 3,840$\times$2,160 pixels, as well as manually labeled 8,987 instances of TTs and PLs. We develop novel policies for collecting, annotating, and labeling the images in TTPLA. Different from other relevant datasets, TTPLA supports evaluation of instance segmentation, besides detection and semantic segmentation. To build a baseline for detection and segmentation tasks on TTPLA, we report the performance of several state-of-the-art deep learning models on our dataset. TTPLA dataset is publicly available at https://github.com/r3ab/ttpla_dataset