Aiming at the problems of existing methods in dense, occluded and small object detection, this paper proposes a QARep-YOLOv8n algorithm for vehicle detection in urban road scenes. First, this paper adopts a Haar wavelet downsampling module to alleviate the problem of feature information loss caused by traditional stepwise convolution or pooling; secondly, this paper proposes a batch-normalization attention module and QARepC2f module to improve the feature extraction ability of YOLOv8; finally, this paper uses NWD bounding box loss and Slide classification loss to improve the detection effect of small and occluded objects. Extensive ablation experiments and validation experiments on four mainstream vehicle detection benchmark datasets shows that QARep-YOLOv8n improves mAP by 3.3%, 3.2%, 2.7% and 1.5%, respectively, compared with YOLOv8n. In addition, the proposed method has more significant detection effects on small and occluded objects.