Nighttime object detection faces numerous challenges compared to daytime object detection， due to low-light conditions and the scarcity of high-quality labeled data， which hinder feature extraction and degrade detection accuracy. Therefore， a domain-adaptive object detection method for nighttime images was proposed. Firstly， a nighttime domain-adaptive photometric alignment module was designed to convert a labeled daytime source domain image into a corresponding nighttime target domain image， that is bridging the gap between source and target domains through photometric alignment， thereby solving the problem of difficulty in obtaining accurate nighttime object labels under low-light conditions. Secondly， a hybrid CNN-Transformer model was used as a detector， in which using CSwin Transformer was used as a backbone network to extract multi-level image features and these features were input into a feature pyramid network， thus enhancing the model's capability for multi-scale object detection. Finally， the Outlook attention module was introduced to address the loss of image details caused by insufficient lighting， thereby enhancing the model's robustness under varying lighting conditions， shadows， and other complex environments. Experimental results demonstrate that the proposed method achieves the mean Average Precision （mAP） @0.5 of 50.0% on the public dataset BDD100K， which is improved by 4.2 percentage points compared to 2PCNet （two-Phase Consistency Network） method； and the mAP@0.5 reached 45.4% on the public dataset SODA10M， which is improved by 0.9 percentage points compared to SFA （Sequence Feature Alignment） method.