# RT-DETR++ for UAV Object Detection

Source: [RT-DETR++ for UAV Object Detection](<https://www.sota2.com/research/paper/68fa7b4b1e209131cea879d5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:37:33.682Z

Authors: Yuan Shufang

Published: 2025-09-11T05:33:52.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.09157>)

[PDF](<https://arxiv.org/pdf/2509.09157.pdf>)