Single-Shot Balanced Detector for Geospatial Object Detection
Yanfeng Liu, Qiang Li, Yuan Yuan, Qi Wang
Abstract
Geospatial object detection is an essential task in remote sensing community. One-stage methods based on deep learning have faster running speed but cannot reach higher detection accuracy than two-stage methods. In this paper, to achieve excellent speed/accuracy trade-off for geospatial object detection, a single-shot balanced detector is presented. First, a balanced feature pyramid network (BFPN) is designed, which can balance semantic information and spatial information between high-level and shallow-level features adaptively. Second, we propose a task-interactive head (TIH). It can reduce the task misalignment between classification and regression. Extensive experiments show that the improved detector obtains significant detection accuracy with considerable speed on two benchmark datasets.