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 and suppresses unrelated ones, generating 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 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，因为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因缺乏有效信息而性能下降。为解决该问题，近年来的多光谱检测方法融合热成像图像以提供互补信息，并获得了增强的性能。然而，很少有研究关注由噪声融合特征图引起的误检的负面影响。与此不同，我们全面分析了误检对检测性能的影响，并发现增强特征对比度可显著减少这类误检。本文提出一种新颖的面向多光谱行人检测的目标感知融合策略，命名为TFDet。我们的融合策略突出行人相关特征并抑制无关特征，从而生成更具判别性的融合特征。TFDet在KAIST和LLVIP基准测试上均实现了最优性能，且效率与此前最先进方法相当。重要的是，TFDet即使在低光照条件下也表现优异，这对确保道路安全具有重要意义。代码将在https://github.com/XueZ-phd/TFDet.git 公开。