Mat-Desa Shahbe, Mohd-Isa Wan-Noorshahida, Gomez-Krämer Petra, Roslee M, Hashim Noramiza, Abdullah Junaidi, Ali Aziah, Che-Embi Zarina, Ibrahim Amalina
Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya, Selangor 63100, Malaysia.
L3i Laboratory, La Rochelle University, Avenue Michel Crépeau, La Rochelle 17042, France.
Data Brief. 2025 Mar 20;60:111482. doi: 10.1016/j.dib.2025.111482. eCollection 2025 Jun.
Detecting small objects in aerial images poses several challenges, including issues with resolution limitations, scale variability, background clutter, and object occlusion. Annotated datasets for small objects in aerial images are often scarce, complicating the training and validation of detection models. This article introduces a new dataset specifically designed for small object detection in low-altitude aerial images. It addresses the challenges posed by shadows, including their impact on object visibility, by including images that capture small objects obscured with shadows. The dataset also features ground-truth shadow maps to support research in shadow detection. This dataset offers potential for future research and serves as a resource for transfer learning.
检测航空图像中的小物体存在诸多挑战，包括分辨率限制、尺度变化、背景杂乱以及物体遮挡等问题。用于航空图像中小物体的标注数据集通常很稀缺，这使得检测模型的训练和验证变得复杂。本文介绍了一个专门为低空航空图像中的小物体检测而设计的新数据集。它通过纳入捕捉被阴影遮挡的小物体的图像，解决了阴影带来的挑战，包括阴影对物体可见性的影响。该数据集还具有地面真值阴影图，以支持阴影检测研究。这个数据集为未来的研究提供了潜力，并可作为迁移学习的资源。
