Object detection is becoming increasingly significant for autonomous-driving system. However, poor accuracy or low inference performance limits current object detectors in applying to autonomous driving. In this work, a fast and accurate object detector termed as SA-YOLOv3, is proposed by introducing dilated convolution and self-attention module (SAM) into the architecture of YOLOv3. Furthermore, loss function based on GIoU and focal loss is reconstructed to further optimize detection performance. With an input size of $512\times 512$ , our proposed SA-YOLOv3 improves YOLOv3 by 2.58 mAP and 2.63 mAP on KITTI and BDD100K benchmarks, with real-time inference (more than 40 FPS). When compared with other state-of-the-art detectors, it reports better trade-off in terms of detection accuracy and speed, indicating the suitability for autonomous-driving application. To our best knowledge, it is the first method that incorporates YOLOv3 with attention mechanism, and we expect this work would guide for autonomous-driving research in the future.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    SA-YOLOv3: An Efficient and Accurate Object Detector Using Self-Attention Mechanism for Autonomous Driving


    Beteiligte:
    Tian, Daxin (Autor:in) / Lin, Chunmian (Autor:in) / Zhou, Jianshan (Autor:in) / Duan, Xuting (Autor:in) / Cao, Yue (Autor:in) / Zhao, Dezong (Autor:in) / Cao, Dongpu (Autor:in)


    Erscheinungsdatum :

    2022-05-01


    Format / Umfang :

    5072605 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A STUDY ON AUTONOMOUS DRIVING ADAPTIVE SIMULATION SYSTEM USING DEEP LEARNING MODEL YOLOV3

    Donzia, Symphorien Karl Yoki / Geum, Young-Pil / Kim, Haeng-Kon | BASE | 2021

    Freier Zugriff

    A STUDY ON AUTONOMOUS DRIVING ADAPTIVE SIMULATION SYSTEM USING DEEP LEARNING MODEL YOLOV3

    Donzia, Symphorien Karl Yoki / Geum, Young-Pil / Kim, Haeng-Kon | BASE | 2021

    Freier Zugriff

    Traffic Object Detection and Distance Estimation Using YOLOv3

    PANTHATI, JAGADEESH | British Library Conference Proceedings | 2022


    Traffic Object Detection and Distance Estimation Using YOLOv3

    PANTHATI, JAGADEESH | SAE Technical Papers | 2022


    Multiple object tracking using a dual-attention network for autonomous driving

    Gao, Ming / Jin, Lisheng / Jiang, Yuying et al. | IET | 2020

    Freier Zugriff