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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    Title :

    Improving Unmanned Panoramic Perception Algorithm of DAFPN-YOLO


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Yingmin (editor) / Zhang, Weicun (editor) / Fu, Yongling (editor) / Wang, Jiqiang (editor) / Wu, Xiru (author) / Lin, Yurui (author) / Liu, Chao (author)

    Conference:

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



    Publication date :

    2023-10-08


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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