Title: Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection Show Chinese title

Title: 合作学生：在夜间目标检测中的无监督域适应

Abstract: Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions; however, its performance degrades notably in low-visibility scenarios, especially at night, posing challenges not only for its adaptability in low signal-to-noise ratio (SNR) conditions but also for the reliability and efficiency of automated vehicles. To address this problem, we propose a \textbf{Co}operative \textbf{S}tudents (\textbf{CoS}) framework that innovatively employs global-local transformations (GLT) and a proxy-based target consistency (PTC) mechanism to capture the spatial consistency in day- and night-time scenarios effectively, and thus bridge the significant domain shift across contexts. Building upon this, we further devise an adaptive IoU-informed thresholding (AIT) module to gradually avoid overlooking potential true positives and enrich the latent information in the target domain. Comprehensive experiments show that CoS essentially enhanced UDA performance in low-visibility conditions and surpasses current state-of-the-art techniques, achieving an increase in mAP of 3.0\%, 1.9\%, and 2.5\% on BDD100K, SHIFT, and ACDC datasets, respectively. Code is available at https://github.com/jichengyuan/Cooperitive_Students. Show Chinese abstract

Abstract: 无监督域适应（UDA）在光照良好的条件下目标检测方面已取得显著进展；然而，在低能见度场景中，尤其是夜间，其性能明显下降，这不仅对其在低信噪比（SNR）条件下的适应性构成挑战，也对自动驾驶车辆的可靠性和效率提出了挑战。 为了解决这个问题，我们提出了一种\textbf{共同}操作\textbf{S}学生（\textbf{共同性}）框架，该框架创新性地采用全局-局部变换（GLT）和基于代理的目标一致性（PTC）机制，以有效捕捉昼夜场景中的空间一致性，从而弥合不同上下文之间的显著域偏移。 在此基础上，我们进一步设计了一个自适应IoU信息阈值化（AIT）模块，以逐步避免忽略潜在的真正阳性，并丰富目标域中的潜在信息。 全面的实验表明，CoS在低能见度条件下显著提升了UDA性能，并超越了当前最先进的技术，在BDD100K、SHIFT和ACDC数据集上分别实现了mAP提升3.0%、1.9%和2.5%。 代码可在https://github.com/jichengyuan/Cooperitive_Students获取。