  Fine-Grained Object Detection (FGOD) is a critical task in high-resolution
aerial image analysis. This letter introduces Orthogonal Mapping (OM), a simple
yet effective method aimed at addressing the challenge of semantic confusion
inherent in FGOD. OM introduces orthogonal constraints in the feature space by
decoupling features from the last layer of the classification branch with a
class-wise orthogonal vector basis. This effectively mitigates semantic
confusion and enhances classification accuracy. Moreover, OM can be seamlessly
integrated into mainstream object detectors. Extensive experiments conducted on
three FGOD datasets (FAIR1M, ShipRSImageNet, and MAR20) demonstrate the
effectiveness and superiority of the proposed approach. Notably, with just one
line of code, OM achieves a 4.08% improvement in mean Average Precision (mAP)
over FCOS on the ShipRSImageNet dataset. Codes are released at
https://github.com/ZhuHaoranEIS/Orthogonal-FGOD.
