A Tiny Object Detection Method Based on Explicit Semantic Guidance for Remote Sensing Images

In the field of remote sensing, the detection of tiny objects has always been an interesting and highly regarded issue. Although many researchers have dedicated their efforts to studying this problem, it still presents numerous challenges due to the complexity of the environment in which tiny objects are presented in remote sensing images. To this end, we propose a remote sensing image tiny objects detection method based on explicit semantic guidance, with a specific focus on regions containing tiny objects. Specifically, we incorporate supervision of the tiny object regions during the training process. This supervision allowed us to extract tiny object regions, thereby forming an explicit attention map. This explicit attention map is employed to semantically modulate the feature map for detecting tiny objects, thus enhancing the regions containing tiny objects while suppressing the background. Extensive experiments are conducted on the AI-TODv2 dataset and the proposed method can achieve an AP of 24.6%. The experimental results demonstrate the effectiveness of the proposed tiny object detection method based on explicit semantic guidance. The code will be released soon on the site of https://github.com/dyl96/ESG_TODNet.