• Laser & Optoelectronics Progress
  • Vol. 62, Issue 2, 0237005 (2025)
Ruoying Liu1、2、3、*, Miaohua Huang1、2、3, Liangzi Wang1、2、3, Yongkang Hu1、2、3, and Ye Tao1、2、3
Author Affiliations
  • 1Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, Hubei , China
  • 2Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, Hubei , China
  • 3Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan 430070, Hubei , China
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    DOI: 10.3788/LOP241187 Cite this Article Set citation alerts
    Ruoying Liu, Miaohua Huang, Liangzi Wang, Yongkang Hu, Ye Tao. Roadside Object Detection Algorithm Based on Multiscale Sequence Fusion[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237005 Copy Citation Text show less

    Abstract

    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.
    Ruoying Liu, Miaohua Huang, Liangzi Wang, Yongkang Hu, Ye Tao. Roadside Object Detection Algorithm Based on Multiscale Sequence Fusion[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237005
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