TBNet: A texture and boundary-aware network for small weak object detection in remote-sensing imagery

Object detection is of great importance for remote sensing image interpretation work and has received significant attention. However, small weak object detection has always been a challenge. The main reason is that the critical information of these objects, such as textures and boundaries, is suppressed by the background and cannot effectively express their own characteristics. To address this issue, we introduce a novel texture and boundary-aware network (TBNet) in this paper. Firstly, we propose a texture-aware enhancement module (TAEM) to explore the texture details within the images. TAEM captures pixel correlations to perceive the distribution of texture in the objects. Secondly, a boundary-aware fusion module (BAFM) is introduced to emphasize spatial positions. BAFM can extract the edge information to guide the prediction of small weak objects. Finally, a task-decoupled RCNN (TD-RCNN) is designed to separate classification and regression tasks. TD-RCNN achieves fine-grained detection, avoiding compromises between subtasks. Comprehensive experiments on four public datasets, DIOR NWPU VHR-10, RSOD, and AI-TOD, demonstrate that TBNet achieves state-of-the-art performance compared to competitors. The model is also evaluated on UAVOD-10, which collects numerous small weak objects. TBNet achieves state-of-the-art results while significantly outperforming competitors, proving its ability to detect small weak objects. • TBNet is designed to detect small and weak objects in complex remote sensing images. • The network enhances object representations by exploring texture and boundary features. • TD-RCNN avoids feature coupling from shared classification and localization. • Results show the model detects remote-sensing objects effectively, especially weak ones.