This study develops a lightweight roadside object detection algorithm called MQ-YOLO. The algorithm is based on multiscale sequence fusion. It addresses the challenges of low detection accuracy for small and occluded targets and the large number of model parameters in urban traffic roadside object detection tasks. We design a D-C2f module based on multi-branch feature extraction to enhance feature representation while maintaining speed. To strengthen the integration of information from multiscale sequences and enhance feature extraction for small targets, the plural-scale sequence fusion (PSF) module is designed to reconstruct the feature fusion layer. Multiple attention mechanisms are incorporated into the detection head for greater focus on the salient semantic information of occluded targets. To enhance the detection performance of the model, a loss function based on the normalized Wasserstein distance is introduced. Experimental results on the DAIR-V2X-I dataset demonstrate that MQ-YOLO achieves improved mAP@50 and mAP@(50?95) by 3.9 percentage point and 6.0 percentage point compared to the valuses obtained with baseline YOLOv8n with 3.96 Mbit parameters. Experiments on the DAIR-V2X-SPD-I dataset show that the model has good generalizability. During roadside deployment, the model reaches detection speeds of 62.5 frame/s, meeting current roadside object detection requirement for edge deployment in urban traffic.