Coupled Global–Local object detection for large VHR aerial images

Object detection in large aerial images generally requires splitting each image into local images in the preprocessing step, and even the state-of-the-art models currently use this preprocessing method. However, image splitting often leads to deficiencies in contextual information and incomplete detection of oversized objects. At present, many object detection methods are designed to deal with large images. However, they require complex additional structures or training steps, and their applicability is limited. To address these problems, we propose the Coupled Global–Local (CGL) network, which can be easily embedded in frequently used detection models, to efficiently capture more information. Specifically, we employ a multiscale feature fusion module to share information between the global and local branches. Furthermore, a new convolution method is proposed to adaptively adjust the receptive field for better feature extraction. In addition, we find that detection results from global branches in the existing global–local architecture hinder the performance improvement on details when the detection results from different-resolution branches are fused. Therefore, on the global branch, a proposal filter and a nonlocal suppression (NLS) algorithm are developed to prevent small positive proposals and remove unqualified detection boxes easily and efficiently, respectively. We conduct extensive experiments on the DOTA-1.0, DOTA-1.5, and DOTA-2.0 data sets. The results demonstrate that CGL can significantly improve the detection performance of various baseline models for large very-high-resolution (VHR) aerial images without bells and whistles.