Semantic segmentation (SS) partitions an image into several coherent semantically meaningful parts and classifies each part into one of the pre-determined classes. In this paper, we argue that the existing SS methods cannot be reliably applied to autonomous driving system as they ignore the different importance levels of distinct classes for safe driving. For example, pedestrian, car, and bicyclist in the scene are much more important than sky and building when driving a car, so their segmentations should be as accurate as possible. To incorporate the importance information possessed by various object classes, this paper designs an “importance-aware loss” (IAL) that specifically emphasizes the critical objects for autonomous driving. The IAL operates under a hierarchical structure and the classes with different importance are located in different levels so that they are assigned distinct weights. Furthermore, we derive the forward and backward propagation rules for IAL and apply them to four typical deep neural networks for realizing SS in an intelligent driving system. The experiments on CamVid and Cityscapes data sets reveal that, by employing the proposed loss function, the existing deep learning models, including FCN, SegNet, ENet, and ERFNet, are able to consistently obtain the improved segmentation results on the pre-defined important classes for safe driving.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Importance-Aware Semantic Segmentation for Autonomous Vehicles


    Contributors:
    Chen, Bike (author) / Gong, Chen (author) / Yang, Jian (author)


    Publication date :

    2019-01-01


    Size :

    4088482 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Hyperbolic Uncertainty Aware Semantic Segmentation

    Chen, Bike / Peng, Wei / Cao, Xiaofeng et al. | IEEE | 2024



    Expert-driven Rule-based Refinement of Semantic Segmentation Maps for Autonomous Vehicles

    Manibardo, Eric L. / Lana, Ibai / Del Ser, Javier et al. | IEEE | 2023


    Semantic information sharing in autonomous vehicles

    FERRIN JEFF / BYBEE TAYLOR / BUNDERSON NATE | European Patent Office | 2022

    Free access

    CONTEXT AWARE STOPPING FOR AUTONOMOUS VEHICLES

    DYER JOHN WESLEY / TORRES LUIS / EPSTEIN MICHAEL et al. | European Patent Office | 2021

    Free access