Slim-DETR: Real-Time Tiny Object Detection with Efficient Interaction and Gaussian Query

Tiny object detection is critical for applications ranging from drone-based scene analysis to remote sensing. However, existing DETR frameworks suffer from inefficient encoder architectures and a severe query-object misalignment for tiny targets, hindering their deployment in real-time scenarios. To overcome these limitations, we propose SlimDETR, a novel framework tailored for real-time tiny object detection. Central to our approach is an Efficient Interaction (EI) encoder, which employs a gather-and-inject mechanism to aggregate global semantics. Uniquely, the EI encoder restricts semantic attention interactions exclusively to extracted foreground tokens, thereby significantly alleviating the computational overhead of conventional Transformer-based encoders. Furthermore, we introduce a Gaussian Target Guided (GTG) query selection strategy that leverages the Gaussian spatial distribution of targets to dynamically concentrate high-quality queries on regions containing tiny objects. Extensive experiments demonstrate that Slim-DETR achieves state-of-the-art performance, yielding 33.9% AP on VisDrone and 33.6% AP on AI-TOD-V2. Notably, it maintains real-time inference speeds across platforms, achieving a latency of 24.4 ms on an NVIDIA RTX 3090 GPU and 25.1 ms (40 FPS) on the embedded Jetson AGX Orin.