Object counting is a hot topic in computer vision, which aims to estimate the number of objects in a given image. However, most methods only count objects of a single category for an image, which cannot be applied to scenes that need to count objects with multiple categories simultaneously, especially in aerial scenes. To this end, this paper introduces a Multi-category Object Counting (MOC) task to estimate the numbers of different objects (cars, buildings, ships, etc.) in an aerial image. Considering the absence of a dataset for this task, a large-scale Dataset (NWPU-MOC) is collected, consisting of 3,416 scenes with a resolution of 1024 $\times$ 1024 pixels, and well-annotated using 14 fine-grained object categories. Besides, each scene contains RGB and Near Infrared (NIR) images, of which the NIR spectrum can provide richer characterization information compared with only the RGB spectrum. Based on NWPU-MOC, the paper presents a multi-spectrum, multi-category object counting framework, which employs a dual-attention module to fuse the features of RGB and NIR and subsequently regress multi-channel density maps corresponding to each object category. In addition, to modeling the dependency between different channels in the density map with each object category, a spatial contrast loss is designed as a penalty for overlapping predictions at the same spatial position. Experimental results demonstrate that the proposed method achieves state-of-the-art performance compared with some mainstream counting algorithms. The dataset, code and models are publicly available at https://github.com/lyongo/NWPU-MOC.

翻译：目标计数是计算机视觉的热点问题，旨在估算给定图像中目标的数量。然而，大多数方法仅针对图像中单一类别的目标进行计数，难以应用于需要同时统计多类别目标的场景，尤其是航空场景。为此，本文提出多类别目标计数（MOC）任务，用于估算航空图像中不同目标（如汽车、建筑物、船舶等）的数量。鉴于该任务缺乏相关数据集，我们构建了一个大规模数据集（NWPU-MOC），包含3416个场景，分辨率为1024×1024像素，并使用14个细粒度目标类别进行精细标注。此外，每个场景包含红绿蓝（RGB）和近红外（NIR）图像，其中近红外光谱相较仅使用RGB光谱能提供更丰富的表征信息。基于NWPU-MOC，本文提出了一种多光谱、多类别目标计数框架，该框架采用双注意力模块融合RGB和近红外特征，进而回归每个目标类别对应的多通道密度图。同时，为建模密度图中不同通道与各目标类别之间的依赖关系，设计了空间对比损失函数，用于惩罚同一空间位置上的重叠预测。实验结果表明，与主流计数算法相比，所提方法达到了最先进的性能。数据集、代码及模型已公开于https://github.com/lyongo/NWPU-MOC。