Multi-Scale Cascade Guided Object Detection in Aerial Images.

Object detection in aerial images has received increasing attention during the last few years. Scale variation is one of the main challenges in large scene aerial images. Existing object detection pipelines usually detect objects of different scale objects at multiple scale layers. However, the conventional detection approaches with multi-scale density predictions could cause duplicate detections of the same object. In this paper, we proposed a multi-scale cascade guided detection framework (MCGNet) to address these issues by guiding the different scales in detector focus on different scale objects. In particular, we proposed a multi-scale cascade module to predict the different scale objects with an explicit constraint in the loss function. Experiments on benchmark DOTA show promising performance of MCGNet compared with other detectors. Code will be released at https://github.com/jason-su/MCGNET.