LC-YOLO: long-tailed infrared UAV detection based on local contrast

Abstract

Infrared UAV detection and fine-grained recognition are pivotal for low-altitude security but suffer from two primary issues: the lack of texture in infrared imagery, which hinders fine-grained classification, and extreme long-tailed data distributions, which leads to poor performance on rare classes. We propose LC-YOLO, a real-time framework integrating local contrast enhancement and category balancing. To resolve texture deficiency, we design a Multi-scale Local Contrast Module (MLCM) that utilizes dilated convolutions to mimic the Human Visual System, significantly enhancing rotor edge features for better fine-grained discrimination. To mitigate data imbalance, we introduce a Category-Specific Mosaic (CS-Mosaic) strategy that enforces tail-class oversampling during data loading, preventing model overfitting to head classes at the source. Experiments on a multi-source heterogeneous dataset demonstrate that LC-YOLO substantially improves overall mAP and tail-class recall (e.g., single-rotors and fixed-wings) while maintaining real-time efficiency.