Enhancing Fine-grained Object Detection in Aerial Images via Orthogonal Mapping
This addresses the problem of distinguishing similar objects in aerial imagery for applications like surveillance or mapping, but it is incremental as it builds on existing object detectors.
The paper tackles semantic confusion in fine-grained object detection for aerial images by introducing Orthogonal Mapping, which improves classification accuracy by applying orthogonal constraints in feature space, achieving a 4.08% mAP gain on the ShipRSImageNet dataset.
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.