A DCM-DETR object detector for drone images in harsh environments

Under adverse weather conditions, especially haze, small target detection in Unmanned Aerial Vehicle (UAV) images faces significant challenges due to low contrast, blurred features, and scale changes. To address these issues, this paper proposes DCM-DETR (Dynamic Contrast Module), an enhanced version of Real-Time Detection Transformer (RT-DETR) tailored for drone-based robust detection in complex environments. The framework integrates three new modules: a dynamic convolution fusion module, which uses adaptive convolution kernel and multi-scale feature mixing to improve angle and scale invariance; contrast wavelet feature aggregation, which is used to decouple high-frequency details from low-frequency structural features to enhance anti-haze capability; and frequency-spatial attention and multi-scale progressive channel attention combined with frequency domain filtering and spatial attention to reduce noise and improve feature resolution. Numerous experiments on the HazyDet and VisDrone-2019 datasets show that DCM-DETR outperforms the most advanced detectors, achieving an AP50 score of 74.3% and 40.8%, respectively, while maintaining a real-time inference speed of 128.8 FPS. The model’s efficiency (53.5 GFLOPs) and balance parameter design (22.1M) further validate its practicality for deployment on resource-constrained UAV platforms. This work advances the application of RT-DETR-based models in UAVs, providing a powerful solution for extreme weather scenarios.