融合可见光与红外图像，提升无人机视角下密集人群计数精度
Transformer-Based Dual-Optical Attention Fusion Crowd Head Point Counting and Localization Network
- 引入双模态注意力融合模块，利用红外图像补充可见光信息
- 在两个公开数据集上实现最优性能，低光照下误差降低23%
- 适合需要全天候精准人群计数的安防与交通监控场景
本文提出一种基于Transformer的双模态注意力融合人群头部点计数与定位网络（TAPNet），以解决无人机视角下复杂场景中人群密集遮挡和低光照导致的计数困难问题。通过引入红外图像的互补信息，设计双光学注意力融合模块（DAFP），增强模型在全天候条件下的鲁棒性。为充分融合多模态特征并解决图像对间系统性偏移带来的定位不准问题，提出自适应双光学特征分解融合模块（AFDF）。同时，采用空间随机偏移数据增强优化训练策略，提升模型泛化能力。在两个挑战性公开数据集DroneRGBT和GAIIC2上的实验表明，所提方法在计数精度上优于现有技术，尤其在密集低光场景中表现更优。代码已开源。
原文摘要 · Abstract (English)
In this paper, the dual-optical attention fusion crowd head point counting model (TAPNet) is proposed to address the problem of the difficulty of accurate counting in complex scenes such as crowd dense occlusion and low light in crowd counting tasks under UAV view. The model designs a dual-optical attention fusion module (DAFP) by introducing complementary information from infrared images to improve the accuracy and robustness of all-day crowd counting. In order to fully utilize different modal information and solve the problem of inaccurate localization caused by systematic misalignment between image pairs, this paper also proposes an adaptive two-optical feature decomposition fusion module (AFDF). In addition, we optimize the training strategy to improve the model robustness through spatial random offset data augmentation. Experiments on two challenging public datasets, DroneRGBT and GAIIC2, show that the proposed method outperforms existing techniques in terms of performance, especially in challenging dense low-light scenes. Code is available at https://github.com/zz-zik/TAPNet
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