Remote Sensing Small Object Detection Based on Multi-Contextual Information Aggregation

Due to wide field of view and background confusion, remote sensing objects are small and densely packed, commonly used detection methods detecting small object are not satisfactory. In this article, we propose method multi-contextual information aggregation YOLO (MCIA-YOLO), combining three novel modules to effectively aggregate multi-contextual information across channels, depths and pixels. Firstly, the channel-spatial information aggregation (CSA) module assembles spatial global features pursuant to channel contextual information, increasing the density of key information. Secondly, the shallow-deep information sparse aggregation (SDSA) module applies sparse cross self-attention mechanism. By sparsely correlating long-range dependency information across different regions, the representation capability of small target is enhanced while removing redundant information. Thirdly, to enrich local multi-scale features and better identify dense targets, multi-scale weighted aggregation (MWA) module convolves multi-receptive field information and performs weighted fusion. Our method demonstrates satisfactory performance on dataset VisDrone2019, UAVDT and NWPU VHR-10, especially in small objects detection, surpassing several state-of-the-art methods.