HRCTNet: a hybrid network with high-resolution representation for object detection in UAV image

Abstract Object detection in unmanned aerial vehicle (UAV) images has attracted the increasing attention of researchers in recent years. However, it is challenging for small object detection using conventional detection methods because less location and semantic information are extracted from the feature maps of UAV images. To remedy this problem, three new feature extraction modules are proposed in this paper to refine the feature maps for small objects in UAV images. Namely, Small-Kernel-Block (SKBlock), Large-Kernel-Block (LKBlock), and Conv-Trans-Block (CTBlock), respectively. Based on these three modules, a novel backbone called High-Resolution Conv-Trans Network (HRCTNet) is proposed. Additionally, an activation function Acon is deployed in our network to reduce the possibility of dying ReLU and remove redundant features. Based on the characteristics of extreme imbalanced labels in UAV image datasets, a loss function Ployloss is adopted to train HRCTNet. To verify the effectiveness of the proposed HRCTNet, corresponding experiments have been conducted on several datasets. On VisDrone dataset, HRCTNet achieves 49.5% on AP50 and 29.1% on AP, respectively. As on COCO dataset, with limited FLOPs, HRCTNet achieves 37.9% on AP and 24.1% on APS. The experimental results demonstrate that HRCTNet outperforms the existing methods for object detection in UAV images