Nighttime assisted driving suffers from image quality degradation due to insufficient light, which causes vehicle detection to face problems such as low visibility, reduced contrast, and increased noise. To address these issues, an improved model, you only look once version 8 nano-SPDConv-triplet attention-HS-FPN (YOLOv8n-STH), is proposed. The model is based on the YOLOv8n and targets problems such as difficult feature extraction and small targets in nighttime images. The space to depth (SPD) module is added in front of part of the faster implementation of CSP bottleneck with 2 convolutions (C2f) structure in the backbone network, and part of the convolutional pooling layers is replaced with SPDConv. Meanwhile, a lightweight triplet attention mechanism is introduced into the C2f structure to enable more accurate distinction between targets and background. Finally, a multi-scale selection feature fusion module is employed to allow the model to efficiently filter more effective feature information. Experimental results on two datasets show that YOLOv8n-STH improves the accuracy rate by 2.1% and reduces the model size by 24.2% compared to the YOLOv8n model, making it suitable for deployment in resource-limited environments.