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In the autonomous driving industry, there is a growing trend to employ long-wave infrared (LWIR)-based uncooled thermal-imaging cameras, capable of robustly collecting data even in extreme environments. Consequently, both industry and academia are actively researching contrast-enhancement techniques to improve the quality of LWIR-based thermal-imaging cameras. However, most research results only showcase experimental outcomes using mass-produced products that already incorporate contrast-enhancement techniques. Put differently, there is a lack of experimental data on contrast enhancement post-non-uniformity (NUC) and temperature compensation (TC) processes, which generate the images seen in the final products. To bridge this gap, we propose a histogram equalization (HE)-based contrast enhancement method that incorporates a region-based clipping technique. Furthermore, we present experimental results on the images obtained after applying NUC and TC processes. We simultaneously conducted visual and qualitative performance evaluations on images acquired after NUC and TC processes. In the visual evaluation, it was confirmed that the proposed method improves image clarity and contrast ratio compared to conventional HE-based methods, even in challenging driving scenarios such as tunnels. In the qualitative evaluation, the proposed method demonstrated upper-middle-class rankings in both image quality and processing speed metrics. Therefore, our proposed method proves to be effective for the essential contrast enhancement process in LWIR-based uncooled thermal-imaging cameras intended for autonomous driving platforms. [ABSTRACT FROM AUTHOR]

Beyond single‐source domain adaption (DA) for object detection, multi‐source domain adaptation for object detection is another challenge because the authors should solve the multiple domain shifts between the source and target domains as well as between multiple source domains. In this letter, the authors propose a novel multi‐source domain adaptation via weighted alignment for object detection where the authors adopt a teacher–student framework. The authors first propose the weighted multiple binary discriminator (MBD) to align the multiple domains considering individual domain shifts. The authors also design the weighted class balance loss (CBL) that aligns the different weights to efficiently learn the detection model even if the number of objects is not balanced in an image under the teacher–student learning scheme. The authors empirically prove the superiority of our method on widely used benchmarks such as Cityscapes, KITTI, and BDD100k datasets. [ABSTRACT FROM AUTHOR]