SEARCH AND RESCUE IMAGE DATASET FOR PERSON DETECTION - SARD

For the task of detecting casualties and persons in search and rescue scenarios in drone images and videos, our database called SARD was built. The actors in the footage have simulate exhausted and injured persons as well as "classic" types of movement of people in nature, such as running, walking, standing, sitting, or lying down. Since different types of terrain and backgrounds determine possible events and scenarios in captured images and videos, the shots include persons on macadam roads, in quarries, low and high grass, forest shade, and the like. The obtained dataset comprises 1,981 manually labeled images extracted from video frames.To increase the robustness of the SARD data, an extension of the SARD set, called Corr, was created that includes images that further simulate different weather conditions that may occur in actual search and rescue situations such as fog, snow, and ice. Also, blur images are included in the Corr set that occur in real conditions as a result of camera movement and aerial shooting in motion.

针对无人机影像与视频中的搜救场景人员及伤亡检测任务，我们构建了名为SARD的数据集。该数据集影像中的受试者均模拟为精疲力竭的受伤人员，同时涵盖了自然人在野外的各类典型动作，如奔跑、行走、站立、坐下及躺卧等。由于不同地形与背景会决定拍摄影像中可能出现的事件与场景，因此该数据集的拍摄场景涵盖了碎石路面、采石场、高低草丛、森林荫蔽等各类环境中的人员。本次构建的SARD数据集共包含1981张从视频帧中提取的人工标注影像。为提升SARD数据集的鲁棒性，我们额外构建了其扩展集Corr，该扩展集包含了进一步模拟真实搜救场景中可能出现的各类天气条件的影像，例如雾天、降雪及结冰场景。此外，Corr扩展集还涵盖了真实场景中因相机移动及动态航空拍摄所产生的模糊影像。

搜集汇总

背景与挑战

背景概述

SARD数据集是一个专为搜救场景设计的无人机图像数据集，包含1981张手动标记的图像，覆盖多种地形和背景。此外，数据集还提供了扩展集Corr，模拟了不同天气条件和模糊效果，以增强模型的鲁棒性。