论文标题
TTPLA：用于检测和分割传输塔和电源线的航空图像数据集
TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines
论文作者
论文摘要
从空中图像中准确检测和分割传输塔〜（TTS）和电源线〜（PLS）〜（PLS）在保护电网安全性和低空无人机安全性方面起着关键作用。同时，TTS和PLS的空中图像对从事对象检测和分割的计算机视觉研究人员构成了许多新挑战 - PLS长而薄，并且可能显示出与背景相似的颜色； TT可以具有各种形状，很可能由各种稀疏性的线结构组成；背景场景，照明和物体大小可能因一个图像而异。在本文中，我们收集并发布了新的TT/PL航空图像（TTPLA）数据集，由1,100张图像组成，分辨率为3,840 $ \ times $ 2,160像素，并手动标记为8,987个TTS和PLS。我们制定了用于收集，注释和标记TTPLA中图像的新型政策。与其他相关数据集不同，TTPLA除了检测和语义分割外，还支持对实例分割的评估。为了在TTPLA上构建用于检测和细分任务的基线，我们报告了数据集上几种最先进的深度学习模型的性能。 TTPLA数据集可在https://github.com/r3ab/ttpla_dataset上公开获得
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
