Abstract: Pedestrian detection is widely used in the fields of intelligent transportation and autonomous driving. However, in low light scenarios, pedestrian detection has problems such as missed detection and false detection, resulting in reduced detection accuracy. Therefore, a low light pedestrian detection algorithm GSG-YOLOv8 based on YOLOv8 improvement is proposed. Firstly, the GCNet module is added to the backbone network to enhance the model's ability to extract contextual information from images. Then, SPDConv and Conv are integrated into the backbone network to enhance the model's ability to extract local features and improve the effectiveness of small object detection. Finally, GAM attention mechanism is added to the neck network to adaptively adjust the correlation between the target and background, and reduce the interference of background information. Compared to the baseline YOLOv8n algorithm, the improved algorithm performs mAP@0.5 And mAP@0.5 ~0.95 increased by 3.4 and 4.6 respectively. Compared to other mainstream algorithms, the improved algorithm has lower system overhead and higher object detection accuracy. The experimental results show that the GSG-YOLOv8 algorithm overcomes the influence of low light and improves the accuracy of pedestrian detection under low light conditions.