Infrared imagery can help in low-visibility situations such as fog and low-light scenarios, but it is prone to thermal noise and requires further processing and correction. This work studies the effect of different infrared processing pipelines on the performance of a pedestrian detection in an urban environment, similar to autonomous driving scenarios. Detection on infrared images is shown to outperform that on visible images, but the infrared correction pipeline is crucial since the models cannot extract information from raw infrared images. Two thermal correction pipelines are studied, the shutter and the shutterless pipes. Experiments show that some correction algorithms like spatial denoising are detrimental to performance even if they increase visual quality for a human observer. Other algorithms like destriping and, to a lesser extent, temporal denoising, increase computational time, but have some role to play in increasing detection accuracy. As it stands, the optimal trade-off for speed and accuracy is simply to use the shutterless pipe with a tonemapping algorithm only, for autonomous driving applications within varied environments.

翻译：红外图像可在雾天与低光照等低能见度条件下提供辅助，但其易受热噪声影响，需进一步处理与校正。本研究探究了不同红外处理流程对城市环境（类自动驾驶场景）中行人检测性能的影响。实验表明红外图像检测性能优于可见光图像，但红外校正流程至关重要，因为模型无法从原始红外图像中提取有效信息。本文研究了两种热校正流程：快门式与非快门式管道。实验证明，部分校正算法（如空间去噪）虽能提升人眼视觉质量，却会损害检测性能；而其他算法（如去条纹算法及在较小程度上的时序去噪）虽会增加计算耗时，但对提升检测精度具有积极作用。当前研究表明，针对多变环境下的自动驾驶应用，兼顾速度与精度的最优方案是仅采用色调映射算法的非快门式管道。