Driven by the continuous advancements in the field of Advanced Driver Assistance Systems (ADAS), object detection and classification are crucial functionalities receiving significant attention in recent research. Mainly, Indian scenarios are highly dynamic due to the diverse and challenging driving conditions, including congested urban traffic, unpredictable road infrastructure, and varying weather patterns. This paper presents a unique method for classifying two and three-wheelers on the challenging Fine-Grained Vehicle Detection (FGVD) dataset from India. In the first stage, we employ the yolov7-tiny model to detect the objects in the image. In the second stage, we represent the detected object images in the graph domain and utilize a GNN architecture to classify objects within the image. A Spatial Graph Convolutional Network (SGCN) is employed in the second stage to classify the detected objects based on their spatial context. This approach leverages the strengths of SGCNs for classification, potentially leading to improved performance on the two, and three-wheelers of the FGVD dataset. We evaluate our method on the FGVD dataset and demonstrate its effectiveness in achieving object detection and classification.


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    Titel :

    Graph-Based Two-Three Wheeler Classification in Unconstrained Indian Roads


    Beteiligte:


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    859882 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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