Semantic Information Feature Aggregation Network for Object Detection in Remote Sensing Images

Object detection is a crucial but challenging task in remote sensing images. Thanks to the emergence of convolutional neural networks (CNNs), object detection has made significant progress. However, there are still two significant issues that must be addressed: 1) since small targets are distributed at any angle, the features extracted by traditional convolution are incomplete and 2) the objects in remote sensing images are small and dense, resulting in missed detections and false detections during the detection process. In this letter, we innovatively propose to obtain more semantic information to help remote sensing detection tasks solve these two problems. To achieve this goal, we design two novel modules: an adaptive feature extraction module (AFEM) and a tridirectional feature fusion module (TRFFM) to improve detection capabilities in small target-dense scenarios. More specifically, AFEM combines local features with global features to adaptively fit the receptive field of rotating targets. TRFFM establishes multiple paths between different layers of the feature pyramid and uses a weighted special fusion mechanism to obtain higher quality feature maps. Extensive experiments on two challenging remote datasets, optical remote sensing images (DIOR) and VisDrone2019, results reached 75.4% AP50 and 33.2% AP, respectively, which verified the superiority of our method in terms of accuracy and adaptability. The code has been open-sourced at https://github.com/GGD777/SIFANet.