In this paper, we propose the concept of global guidance, design a global guidance structure based on a dual-scale global feature enhancement module, and construct a multi-task network (RTMDet-MGG) for road scene instance segmentation and drivable area segmentation in order to overcome the limitation of current multi-task networks in sharing features to different task branches. The proposed global guidance structure enhances the connection between different task branches in the multi-task network by sharing the enhanced global feature with different task branches through various fusion methods, thereby enhancing the multi-task network’s overall performance. With an input resolution of $\mathbf {640}\times \mathbf {360}$ on the BDD100K dataset, RTMDet-MGG achieves an accuracy of 18.7% mAP (mean average precision) on the instance mask and 82.9% mIoU (mean intersection over union) on the drivable area, with an inference speed of 32.1 FPS (frames per second), which satisfies the requirements of real-time tasks. In addition, the algorithm has excellent scene generalization capabilities, and the mIoU of drivable area segmentation on our custom-built dataset for unstructured road drivable area segmentation reaches 93.3%.


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    Titel :

    RTMDet-MGG: A Multi-Task Model With Global Guidance


    Beteiligte:
    Wang, Hai (Autor:in) / Qin, Qirui (Autor:in) / Chen, Long (Autor:in) / Li, Yicheng (Autor:in) / Cai, Yingfeng (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2024


    Format / Umfang :

    2401885 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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