In order to solve the problem that visible light images suffer from performance degradation in low-light conditions, this paper proposes a semi-supervised domainadapted pedestrian detection algorithm. Firstly, the semi-supervised detection network is built by combining the mean teacher model and the YOLOv8 detector. Secondly, pseudo-images are generated for pseudo-cross-training using a combination of the image fusion algorithm and the style migration algorithm to reduce the problem of domain difference between images. Finally, the hybrid attention mechanism based on Transform is introduced into the backbone feature extraction network, which further improves the detection accuracy while enhancing the image resolution. The experimental results show that the detection accuracy of the algorithm reaches 89.3% and 66.8% on the LLVIP dataset and KAIST dataset, respectively, which is 7.6% and 19.8% higher compared to the SSDA-YOLO algorithm, 4% and 8.7% higher compared to the Efficient Teacher algorithm, and 1.8% and 17.9% compared to the fully supervised algorithm ICAFusion. Compared with previous algorithms, this algorithm has higher detection accuracy.