Ground Truth of Powerline Dataset (Infrared-IR and Visible Light-VL)|电力线路检测数据集|红外/可见光图像数据集

Despite a relatively crowded literature about detection of power lines for aircraft safety, the works are mostly optimized with very few training images; some even use artificially generated images. The IR imaging case is even less utilized. The reason is clearly the tremendous workload to obtain real images. With this demand in mind, the authors cooperated with the Turkish Electricity Transmission Company (TEIAS) to obtain video captures from actual aircraft. Later, the authors made a thorough inspection over the video frames to isolate, capture and clean thousands of valuable images. In this content, 400 IR and 400 VL images are acquired and scaled to a size of 512x512. The IR folder contains IR images with power line, ground truths and overlay images of these images. The VL folder contains VL images with power line, ground truths and overlay images of these images. The IR and VL groups were deliberately constructed to contain both regular and especially confusing scenes. "TY" shows that there are no power lines in the images. "TV" shows that there are power lines in the images. The videos were captured from 21 different regions all over Turkey at different seasonal days. Due to varying background behavior, varying temperatures and weather conditions, and varying lighting conditions, the achieved positive set contains several difficult scenes where low contrast causes close to invisibility for power lines. The original video resolutions were 576x325 for IR and full HD for VL, however, the captured frames were scaled down to smaller sizes and the effect of resizing was tested for various image sizes. An image size of 512x512 is sufficient for a consistently accurate power line detection The program developed by Assistant Professor Cihan Topal from Anadolu University Electrical and Electronics Department was used to draw ground truths.

AI搜集汇总

背景与挑战

背景概述

该数据集是一个用于电力线检测的真实图像集合，包含400张红外图像和400张可见光图像，每张图像尺寸为512x512，并附带地面实况和叠加图像。数据集特别设计以涵盖常规和混淆场景，覆盖了21个土耳其不同地区在不同季节和天气条件下的拍摄，适用于计算机视觉和机器学习任务，旨在解决现有研究中训练图像不足的问题。