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Thermal infrared imaging is attracting much attention due to its strength against illuminance variation. However, because of the spectral difference between thermal infrared images and RGB images, the existing research on self-supervised monocular depth estimation has performance limitations. Therefore, in this study, we propose a novel Self-Guided Framework using a Pseudolabel predicted from RGB images. Our proposed framework, which solves the problem of appearance matching loss in the existing framework, transfers the high accuracy of Pseudolabel to the thermal depth estimation network by comparing low- and high-level pixels. Furthermore, we propose Patch-NetVLAD Loss, which strengthens local detail and global context information in the depth map from thermal infrared imaging by comparing locally global patch-level descriptors. Finally, we introduce an Image Matching Loss to estimate a more accurate depth map in a thermal depth network by enhancing the performance of the Pseudolabel. We demonstrate that the proposed framework shows significant performance improvement even when applied to various depth networks in the KAIST Multispectral Dataset.

In this paper, multispectral pedestrian detection is mainly discussed, which can contribute to assigning human-aware properties to automated forklifts to prevent accidents, such as collisions, at an early stage. Since there was no multispectral pedestrian detection dataset in an intralogistics domain, we collected a dataset; the dataset employs a method that aligns image pairs with different domains, i.e. RGB and thermal, without the use of a cumbersome device such as a beam splitter, but rather by exploiting the disparity between RGB sensors and camera geometry. In addition, we propose a multispectral pedestrian detector called SSD 2.5D that can not only detect pedestrians but also estimate the distance between an automated forklift and workers. In extensive experiments, the performance of detection and centroid localization is validated with respect to evaluation metrics used in the driving car domain but with distinct categories, such as hazardous zone and warning zone, to make it more applicable to the intralogistics domain.

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