Abstract
Deep convolutional neural networks (CNNs) have shown tremendous success in the detection of objects and vehicles in recent years. However, when using CNNs to identify real-time vehicle detection in a moving context remains difficult. Many obscured and truncated cars, as well as huge vehicle scale fluctuations in traffic photos, provide these issues. To improve the performance of detection findings, we used multiscale feature maps from CNN or input pictures with numerous resolutions to adapt the base network to match different scales. This research presents an enhanced framework depending on Faster R-CNN for rapid vehicle recognition which presents better accuracy and fast processing time. Research results on our custom dataset indicate that our recommended methodology performed better in terms of detection efficiency and processing time, especially in comparison to the earlier age of Faster R-CNN models.
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