针对无人机图像小目标检测难题，提出新型改进模型提升精度与效率。
PT-DETR: Small Target Detection Based on Partially-Aware Detail Focus
- 引入部分感知细节聚焦模块增强小目标特征提取
- 在VisDrone2019上实现1.6%~1.7%的mAP提升，参数更少
- 适合低资源环境下无人机小目标检测任务
为应对无人机目标检测中复杂背景、严重遮挡、密集小目标及光照变化等挑战，本文基于RT-DETR提出PT-DETR，一种专用于无人机图像中小目标检测的新算法。在主干网络中，引入部分感知细节聚焦（PADF）模块以增强小目标特征提取能力；设计中值频率特征融合（MFFF）模块，有效提升模型对小目标细节与上下文信息的捕捉能力；同时引入Focaler-SIoU，强化边界框匹配能力并提高对小目标特征的敏感性，进一步提升检测精度与鲁棒性。相比RT-DETR，PT-DETR在VisDrone2019数据集上实现1.6%和1.7%的mAP提升，且计算复杂度更低、参数更少，验证了其在小目标检测任务中的有效性与可行性。
原文摘要 · Abstract (English)
To address the challenges in UAV object detection, such as complex backgrounds, severe occlusion, dense small objects, and varying lighting conditions,this paper proposes PT-DETR based on RT-DETR, a novel detection algorithm specifically designed for small objects in UAV imagery. In the backbone network, we introduce the Partially-Aware Detail Focus (PADF) Module to enhance feature extraction for small objects. Additionally,we design the Median-Frequency Feature Fusion (MFFF) module,which effectively improves the model's ability to capture small-object details and contextual information. Furthermore,we incorporate Focaler-SIoU to strengthen the model's bounding box matching capability and increase its sensitivity to small-object features, thereby further enhancing detection accuracy and robustness. Compared with RT-DETR, our PT-DETR achieves mAP improvements of 1.6% and 1.7% on the VisDrone2019 dataset with lower computational complexity and fewer parameters, demonstrating its robustness and feasibility for small-object detection tasks.
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