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A Novel Algorithm for Reducing the Vehicle Density in Traffic Scenario by Using YOLOv7 Algorithm
Abstract
Large megalopolises are experiencing problems with corporate administration due to their expanding populations. The metro political road network regulation also has to be continuously observed, expanded, and modernized. We provide a sophisticated car tracking system with tape recording for surveillance. The suggested system combines neural networks and image-based dogging. To track automobiles, use the You Only Look Once (YOLOv7) method. We used several datasets to train the suggested algorithm. By adopting a Mobile Nets configuration, the YOLOv7's skeleton is altered. Also, its anchor boxes are changed so that they may be trained to recognize vehicle items. In meantime, further post-processing techniques are used to confirm the bounding box that has been found. It was confirmed after extensive testing and analysis that using the suggested technique in a vehicle spotting system is a promising idea. YOLOv7 and the CNN algorithm for bounding box and class prediction. It is explained that the suggested system can locate, track, and count the cars accurately in a variety of situations.
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