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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Tian, Daxin (author) / Lin, Chunmian (author) / Zhou, Jianshan (author) / Duan, Xuting (author) / Cao, Yue (author) / Zhao, Dezong (author) / Cao, Dongpu (author)


    Publication date :

    2022-05-01


    Size :

    5072605 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    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

    Free access

    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

    Free access

    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

    Free access