Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally, the color content of the scenes, solar-zenith angle, and population density of different geographies influence the data diversity. These two factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground-view data, including the open-world foundational models. To pave the way for a transformative era of aerial detection, we present Multiview Aerial Visual RECognition or MAVREC, a video dataset where we record synchronized scenes from different perspectives -- ground camera and drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial-view dataset, and the fourth largest among all drone-based datasets across all modalities and tasks. Through our extensive benchmarking on MAVREC, we recognize that augmenting object detectors with ground-view images from the corresponding geographical location is a superior pre-training strategy for aerial detection. Building on this strategy, we benchmark MAVREC with a curriculum-based semi-supervised object detection approach that leverages labeled (ground and aerial) and unlabeled (only aerial) images to enhance the aerial detection. We publicly release the MAVREC dataset: https://mavrec.github.io.

翻译：尽管商用无人机已广泛普及，空中数据采集仍面临挑战，现有以亚洲和北美为中心的开源无人机数据集存在规模小、分辨率低且缺乏场景上下文多样性的问题。此外，不同地理区域的场景色彩内容、太阳天顶角及人口密度也影响着数据多样性。这两个因素共同导致主要基于地面视角数据（包括开放世界基础模型）训练的深度神经网络模型在空对地视觉感知中表现欠佳。为开创空中检测的变革时代，我们提出多视角空中视觉识别（MAVREC）视频数据集，该数据集从不同视角（地面摄像机和无人机搭载摄像机）同步记录场景。MAVREC包含约2.5小时行业标准的2.7K分辨率视频序列，超过50万帧图像及110万个标注边界框，使其成为规模最大的地面-空中联合视角数据集，并在所有模态与任务的无人机数据集中位居第四。通过在MAVREC上的全面基准测试，我们发现利用对应地理位置的地面视角图像增强目标检测器，是空中检测更优的预训练策略。基于此，我们采用课程式半监督目标检测方法对MAVREC进行基准测试，该方法结合标注数据（地面与空中）和无标注数据（仅空中）来提升空中检测性能。我们已公开MAVREC数据集：https://mavrec.github.io。