Considering typical remote sensing object detection problems, this paper proposes an improved remote sensing object detection model based on the YOLOv3 algorithm. This model introduces the SE attention module to get richer features and uses K-means to extract the more suitable anchor for remote sensing images. The experiments are carried out under the DOTA data set, and the results show that, under the premise of unchanged detection speed, the accuracy of detection is improved, and the improved algorithm has a better effect on detecting the small and dense objects.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Remote Sensing Object Detection Based on Improved YOLOv3


    Beteiligte:
    Dong, Wenlong (Autor:in) / Nie, Shiyang (Autor:in) / Wang, Yibo (Autor:in)


    Erscheinungsdatum :

    2022-10-12


    Format / Umfang :

    1468673 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Vehicle detection method based on improved YOLOv3

    Qi, Cheng / Shen, Xizhong | IEEE | 2022


    Vehicle Detection Based on Improved Yolov3 Algorithm

    Zhao, Shuai / You, Fucheng | IEEE | 2020


    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


    Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image

    Dehai Chen / Shiru Sun / Zhijun Lei et al. | DOAJ | 2021

    Freier Zugriff