Exploring Lightweight Structures for Tiny Object Detection in Remote Sensing Images

Detecting tiny objects in remote sensing images has been an intriguing yet challenging topic in the remote sensing image processing. While significant progress has been made in many studies, most existing methods focus on improving the accuracy of tiny object detection without particular consideration for computational complexity, which restricts their applicability in resource-limited condi-tions. Therefore, this paper aims to design a lightweight de-tection algorithm tailored for tiny objects in remote sensing images. First, we investigate the impact of the complexity of different components in deep learning-based object detec-tion models on the accuracy of tiny object detection, includ-ing the backbone and detection head. Then, a dedicated backbone for tiny object detection is proposed, achieving competitive detection accuracy while remaining lightweight. Moreover, we propose a lightweight detection head that in-corporates deformable convolution and optimize the chan-nel dimension. Finally, we combine the above methods to introduce a lightweight network, LTDNet, for tiny object detection in remote sensing images. Benefiting from the dedicated designs for the backbone and detection head spe-cifically for tiny objects, the proposed method can achieve competitive detection accuracy with very low parameters and computational complexity. Extensive experiments are conducted on the AI-TODv2 and LEVIR-Ship datasets, and the results demonstrate the effectiveness of our proposed method. Specifically, the proposed method achieves 54.6% AP50 on the AI-TODv2 dataset with only 4.85M parameters and 38.19G FLOPs. The code will be released soon on the site of https://github.com/dyl96/LTDNet.