Adaptive Downsampling and Scale Enhanced Detection Head for Tiny Object Detection in Remote Sensing Image

Abstract

In recent years, the detection for tiny objects in remote sensing images has become a hot research topic. Tiny objects contain a limited number of pixels and are easily confused with the background, which leads to low detection accuracy. To the end, this letter proposes a tiny object detection method based on adaptive downsampling and scale enhanced detection head (SEDH) to improve the accuracy of detection without increasing the model parameters. First, the dynamic feature extraction module (DFEM) is proposed. The module can obtain the context information of tiny objects. Second, the adaptive downsampling module (ADM) is designed to capture local details of tiny objects. Finally, the scale enhanced detection head is constructed which improves the sensitivity to tiny objects, while reducing the number of parameters of the model. To verify the effectiveness of the proposed method, a series of experiments are conducted on the challenging AI-TOD dataset. The experimental results demonstrate that the proposed method effectively trade-offs the relationship between detection accuracy and the number of model parameters.