On the basis of YOLO, combined with multi-task learning to realize the tasks of traffic object detection, drivable area segmentation and lane line segmentation in unmanned driving, the unmanned panoramic perception algorithm based on DAFPN-YOLO is proposed. Then, dynamic attention is used to achieve scale perception, space perception and task perception to improve the model’s performance in three tasks: traffic object detection, driveable area segmentation and lane line segmentation. The multi-task loss function is adjusted, and FocalLoss is introduced to improve the model’s performance in the face of category-unbalanced data. The accuracy of vehicle perception algorithm for driverless cars is improved.


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    Titel :

    Improving Unmanned Panoramic Perception Algorithm of DAFPN-YOLO


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Yingmin (Herausgeber:in) / Zhang, Weicun (Herausgeber:in) / Fu, Yongling (Herausgeber:in) / Wang, Jiqiang (Herausgeber:in) / Wu, Xiru (Autor:in) / Lin, Yurui (Autor:in) / Liu, Chao (Autor:in)

    Kongress:

    Chinese Intelligent Systems Conference ; 2023 ; Ningbo, China October 14, 2023 - October 15, 2023



    Erscheinungsdatum :

    08.10.2023


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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