DCL-Net: Augmenting the Capability of Classification and Localization for Remote Sensing Object Detection

Abstract

Deep learning-based remote sensing object detectors are usually composed of two branches: classification and localization. Recently proposed object detectors often follow the pipeline that classification and localization branches share the same feature maps, which leads to a strong coupling relationship between them. However, when tackling remote sensing images, this strong coupling relationship may impair the performance of the detectors because the top-view perspective of remote sensing images may result in conflicts between classification and location branches. To address this issue, we propose a decoupled classification localization network (DCL-Net) by considering the different characteristics between the two branches. Two modules are developed to suppress the strong coupling: receptive field aggregation module (RFAM) and bottom-up path aggregation module (PAM). For the classification branch, RFAM can learn the relationship between objects and context information by simulating the human receptive field and improve the robustness of the classification branch to rotational distortions. For the localization branch, PAM can enhance the entire feature hierarchy by transferring the rich detailed information of low-level features, which helps the detector to achieve precise bounding box regression. Compared with existing methods, the major contribution of DCL-Net is that the independence of the classification and localization branches can be significantly enhanced, which may be beneficial to the detection accuracy for the objects in remote sensing images. Experiments on public data sets validate the effectiveness of our detector.