RT-DETR++ for UAV Object Detection
This work addresses object detection for UAV applications, offering an incremental improvement in feature encoding design for real-time systems.
The paper tackles object detection in UAV imagery, which faces challenges like small, densely packed objects and scale variations, by introducing RT-DETR++ with an enhanced encoder that achieves superior performance for small and densely packed objects while maintaining real-time speed without increased computational complexity.
Object detection in unmanned aerial vehicle (UAV) imagery presents significant challenges. Issues such as densely packed small objects, scale variations, and occlusion are commonplace. This paper introduces RT-DETR++, which enhances the encoder component of the RT-DETR model. Our improvements focus on two key aspects. First, we introduce a channel-gated attention-based upsampling/downsampling (AU/AD) mechanism. This dual-path system minimizes errors and preserves details during feature layer propagation. Second, we incorporate CSP-PAC during feature fusion. This technique employs parallel hollow convolutions to process local and contextual information within the same layer, facilitating the integration of multi-scale features. Evaluation demonstrates that our novel neck design achieves superior performance in detecting small and densely packed objects. The model maintains sufficient speed for real-time detection without increasing computational complexity. This study provides an effective approach for feature encoding design in real-time detection systems.