NWPU-MOC: A Benchmark for Fine-Grained Multicategory Object Counting in Aerial Images

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 article introduces a multicategory 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 3416 scenes with a resolution of
$1024\times1024$
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 article presents a multispectrum, MOC framework, which employs a dual-attention module to fuse the features of RGB and NIR and subsequently regress multichannel density maps corresponding to each object category. In addition to modeling the dependence 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
.