NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in
  Aerial Images
Junyu Gao Liangliang Zhao Xuelong Li
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.