Pedestrian detection plays a critical role in computer vision as it contributes to ensuring traffic safety. Existing methods that rely solely on RGB images suffer from performance degradation under low-light conditions due to the lack of useful information. To address this issue, recent multispectral detection approaches have combined thermal images to provide complementary information and have obtained enhanced performances. Nevertheless, few approaches focus on the negative effects of false positives caused by noisy fused feature maps. Different from them, we comprehensively analyze the impacts of false positives on the detection performance and find that enhancing feature contrast can significantly reduce these false positives. In this paper, we propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet. Our fusion strategy highlights the pedestrian-related features while suppressing unrelated ones, resulting in more discriminative fused features. TFDet achieves state-of-the-art performance on both KAIST and LLVIP benchmarks, with an efficiency comparable to the previous state-of-the-art counterpart. Importantly, TFDet performs remarkably well even under low-light conditions, which is a significant advancement for ensuring road safety. The code will be made publicly available at \url{https://github.com/XueZ-phd/TFDet.git}.

翻译：行人检测在计算机视觉中扮演着关键角色，因其有助于保障交通安全。现有仅依赖RGB图像的方法在低光照条件下会因缺乏有效信息而导致性能下降。为解决此问题，近期多光谱检测方法结合热成像图像以提供互补信息，取得了更优性能。然而，少有研究关注噪声融合特征图所引发的虚警负面影响。与之不同，我们全面分析了虚警对检测性能的影响，发现增强特征对比度可显著减少此类虚警。本文提出一种名为TFDet的新型多光谱行人检测目标感知融合策略。该融合策略在突出行人相关特征的同时抑制无关特征，从而得到更具判别性的融合特征。TFDet在KAIST和LLVIP基准测试中均达到最先进性能，且效率与先前最优方法相当。重要的是，即使在低光照条件下，TFDet仍表现卓越，这是保障道路安全的重要进步。代码将开源至\url{https://github.com/XueZ-phd/TFDet.git}。