关键词: 无监督学习, 目标检测, 夜间域适应, 光度对齐, CNN-Transformer模型

Abstract: Nighttime object detection faces numerous challenges compared to daytime scenarios, primarily due to low-light conditions and the scarcity of high-quality annotated data, which hinder feature extraction and degrade detection accuracy. To this end, a domain-adaptive object detection method for nighttime images is proposed. First, a nighttime domain-adapted luminance alignment module is designed to convert a labeled daytime source domain image into a corresponding nighttime target domain image. Bridging the gap between source and target domains through luminance alignment thus solves the problem of difficulty in obtaining accurate nighttime target annotations under low-light conditions; Second, a hybrid CNN-Transformer model is used as a detector. CSwin Transformer is used as a backbone network to extract multi-level image features and input these features into a feature pyramid network to facilitate multi-scale object detection. Finally, the Outlook attention module is introduced to address the loss of image details caused by insufficient illumination, enhancing the model's robustness under varying lighting conditions, shadows, and other complex environments. The results demonstrate that the proposed method achieved the mean Average Precision (mAP) of 50.0% on the BDD100K dataset, improving by 4.2 percentage points compared to the 2PCNet method. The mAP@0.5 reached 45.4% on the SODA10M dataset, improving by 0.9 percentage points compared to the SFA method.

Key words: unsupervised learning, object detection, nighttime domain adaptation, photometric alignment, CNN-Transformer model