SWIN-TOD: Smooth Wasserstein Distance and Instance-Level Neighboring Enhancement for Remote Sensing Tiny Object Detection

The advancement of deep neural network has propelled the widespread application of remote sensing target detection. However, compared to natural scenes, remote sensing targets possess inherent characteristics such as weak features and small scale, leading to a significant performance gap in traditional detection methods. To address these challenges, we undertake a systematic analysis of existing approaches, focusing on two key aspects: inadequate extraction of discriminative features and inappropriate regression measurement metrics. To tackle the first issue, an instance-level neighboring enhancement network (INEN) is proposed, enhancing the network’s feature extraction capability through inter-object feature aggregation. To address the second issue, a novel metric, smooth Wasserstein loss (SWL), is devised. Building upon these principles, a new tiny object detection (TOD) network for remote sensing images is developed. Extensive experiments on AI-TOD v1/v2 and DOTA v2 remote sensing tiny target detection datasets demonstrate that our approach achieves state-of-the-art (SOTA) performance. Codes are available at https://github.com/sevenwgb/SWIN-TOD.