Automotive manufacturers are investing in smart cars, driverless processes, and pre-collision technologies to deliver safe and fuel-efficient mobility solutions. This paper aims to develop an advanced object detection model which incorporate more sophisticated techniques or architectures to identify commonly found objects on roads using one-stage (Single-Shot Detector) and two-stage object detectors (Faster R-CNN). The models are trained using TensorFlow and the Open Image Dataset V7, with ResNet50 architecture as the backbone. The Faster R-CNN models outperformed the SSD models, achieving a mean average precision of 92.5% at IoU 0.50. This paper presents the theory, methodology, and performance results of the work, which focuses on five classes of objects commonly found on roads (bus, car, motorcycle, stop sign, truck). The detection model exhibited an overall good performance in terms of precision and recall in object classification and localization, as evaluated from TensorBoard. The average precision value is 0.925, indicating accurate object classification. The recall value is 0.727, demonstrating successful object localization.


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    Titel :

    Object Detection in Autonomous Vehicles: A Performance Analysis


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Tan, Andrew (Herausgeber:in) / Zhu, Fan (Herausgeber:in) / Jiang, Haochuan (Herausgeber:in) / Mostafa, Kazi (Herausgeber:in) / Yap, Eng Hwa (Herausgeber:in) / Chen, Leo (Herausgeber:in) / Olule, Lillian J. A. (Herausgeber:in) / Myung, Hyun (Herausgeber:in) / Lim, Yuxiang (Autor:in) / Tiang, Sew Sun (Autor:in)

    Kongress:

    International Conference on Mechatronics and Intelligent Robotics ; 2023 ; Suzhou, China August 22, 2023 - August 23, 2023



    Erscheinungsdatum :

    27.02.2024


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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