Intelligent vehicles detection (IVD) provides information to manage traffic efficiently, drive autonomous vehicles and feed data to intelligent traffic management systems (ITMS). IVD is a challenging task for the close proximity vehicles in lane-less traffic and heterogeneous systems. Most vehicle detection models are complex and limited to multi-scale feature extraction due to the involvement of existing feature extraction backbones. Also, they do not include heterogeneous traffic vehicles, usually present in developing countries. Therefore, this paper proposes a multi-class vehicle detection (MCVD) model to detect vehicles in heterogeneous traffic using a realistic traffic dataset from a developing country. MCVD is a deep learning (DL) model that consists of a convolutional neural network backbone called VDnet, a light fusion bi-directional feature pyramid network (LFBFPN) and a modified vehicle detection head (MVDH). VDnet extracts multi-scale features from the traffic input images using feature reuse methods. LFBFPN combines these features bi-directionally and provides robust feature maps. Finally, MVDH is applied to detect multi-class vehicles and classify them into respective categories. The proposed model achieves 91.45% mean average precision (mAP) on the heterogeneous traffic labeled dataset (HTLD). The proposed MCVD is tested over Nvidia Jetson TX2 edge computing boards to verify the real-time performance. It achieves 17 frames per second (FPS) on TX2. The performance evaluation results indicate that the proposed MCVD model is fast, accurate and better than the existing works.


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    Titel :

    Multi-Class Vehicle Detection Using VDnet in Heterogeneous Traffic


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    3114931 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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