Coarse-grained Density Map Guided Object Detection in Aerial Images

Object detection in aerial images is challenging for at least two reasons: (1) most objects are small scale relative to high resolution aerial images; and (2) the object position distribution is nonuniform, making the detection inefficient. In this paper, a novel network, the coarse-grained density map network (CDMNet), is proposed to address these problems. Specifically, we format density maps into coarsegrained form and design a lightweight dual task density estimation network. The coarse-grained density map can not only describe the distribution of objects, but also cluster objects, quantify scale and reduce computing. In addition, we propose a cluster region generation algorithm guided by density maps to crop input images into multiple subregions, denoted clusters, where the objects are adjusted in a reasonable scale. Besides, we improved mosaic data augmentation to relieve foreground-background and category imbalance problems during detector training. Evaluated on two popular aerial datasets, VisDrone29 and UAVDT6, CDMNet has achieved significant accuracy improvement compared with previous state-of-the-art methods.