Object detection in aerial images is a fundamental research topic in the geoscience and remote sensing domain. However, the advanced approaches on this topic mainly focus on designing the elaborate backbones or head networks but ignore neck networks. In this letter, we first underline the importance of the neck network in object detection from the perspective of information bottleneck. Then, to alleviate the information deficiency problem in the current approaches, we propose a global semantic network (GSNet), which acts as a bridge from the backbone network to the head network in a bidirectional global pattern. Compared to the existing approaches, our model can capture the rich and enhanced image features with less computational costs. Besides, we further propose a feature fusion refinement module (FRM) for different levels of features, which are suffering from the problem of semantic gap in feature fusion. To demonstrate the effectiveness and efficiency of our approach, experiments are carried out on two challenging and representative aerial image datasets (i.e., DOTA and HRSC2016). Experimental results in terms of accuracy and complexity validate the superiority of our method. The code has been open-sourced at GSNet.
翻译:航空图像中的天体探测是地球科学和遥感领域的一个基本研究课题。然而,关于这一专题的先进方法主要侧重于设计精密的骨干或头网,但忽视颈部网络。在本信,我们首先强调从信息瓶颈的角度研究物体探测目标时颈部网络的重要性。然后,为了减轻当前方法中的信息不足问题,我们提议建立一个全球语义网络(GSNet),作为从主干网到双向全球主网络的桥梁。与现有方法相比,我们的模型可以以较低的计算成本捕获丰富和增强的图像特征。此外,我们进一步提议为不同特征的不同层面提供一个特征组合精细模块(FRM),这些特征因特征融合存在语义差异问题而受到影响。为了证明我们的方法的有效性和效率,我们正在对两个具有挑战性和代表性的航空图像数据集(即DOTA和HRSC2016)进行实验。在精确性和复杂性方面,实验结果可以验证我们的方法的优越性。在GSNet上,代码是开源的。