Dynamic Adaptive Label Assignment for Tiny Object Detection in Remote Sensing Images

ABSTRACT With the development of unmanned aerial vehicle and satellite technology, the application of tiny object detection in remote sensing images is becoming increasingly widespread. Although significant progress has been made in the accuracy and speed of object detection in recent years, performance declines sharply when general object detectors are applied to tiny objects; one of the main reasons is unsuitable label assignment strategy. Traditional label assignment strategies often rely on fixed thresholds, leading to mismatches between the number of positive samples and object areas. Additionally, most improved methods require setting one or more hyperparameters. In this paper, we propose a dynamic adaptive label assignment strategy (DALA) comprising three modules. First, we calculate the similarity distance to comprehensively evaluate the matching degree between anchors and each ground truth. Then, we use the ratio‐based label assignment strategy to select an appropriate number of positive samples for each object. Finally, we introduce dynamic weighting loss during training to ensure the model pays more attention to tiny objects. Our three modules automatically adapt to different datasets and detectors without any manual hyperparameter settings. Extensive experiments on four widely used datasets demonstrate the excellent performance of our proposed method. Our code will be released soon.