Joint Optimization Loss Function for Tiny Object Detection in Remote Sensing Images

Tiny object detection remains a formidable challenge in the field of computer vision. There are many factors that influence tiny object detection performance. In this paper, we focus primarily on the following two aspects. First, due to diminutive size and inappropriate label assignment strategy, tiny objects yield significantly fewer positive samples than larger objects, resulting in weakened supervisory signals during backpropagation and model training. Second, most existing detectors directly combine the classification loss and bounding box regression loss during training. Some improvement methods focus exclusively on either classification or localization, leading to potential discrepancies in which predictions exhibit precise localization but incorrect classifications or accurate classifications with imprecise localization. To address these issues, we propose a novel Joint Optimization Loss (JOL) that dynamically assigns optimal weights to each training sample, enabling joint optimization of both the classification and regression losses. Notably, JOL integrates seamlessly with most mainstream detectors and loss functions without requiring alterations to network architectures. Extensive experiments conducted on five benchmark datasets demonstrate the superior performance of our approach, achieving AP improvements of 1.7 and 1.5 points on the AI-TOD and SODA-D datasets, respectively, compared to the state-of-the-art method.