Robust Aircraft Detection in Imbalanced and Similar Classes With a Multi-Perspectives Aircraft Dataset

Aircraft detection holds significant importance in both civilian and military fields, such as air transportation control and battlefield situational awareness. Deep learning-based methods for aircraft detection can obtain promising detection performance with sufficient and labeled samples. However, current public aircraft datasets are mainly from top-down perspective images, lacking multi-perspectives samples, which limits their applicability. Therefore, we construct a multi-perspectives aircraft dataset (MAD), encompassing 10 distinct classes, and making up of 13,205 images and 18,908 instances. Moreover, the imbalanced and similar classes are common issues in aircraft detection. We design an adaptive threshold focal loss (ATFL) function to alleviate the class imbalance through dynamically adjusting the loss weights between different classes. Meanwhile, the dynamic visual center (DVC) module which can effectively capture both local and global information of target is proposed to distinguish the aircraft classes that share similar feature. Finally, we evaluated several state-of-the-art methods on the constructed MAD dataset as baselines for future research.The experimental results verify that the recognition performance of the current state-of-the-art model can be improved after the application of ATFL and DVC. The dataset and code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YangBo0411/aircraft-detection</uri>.