Abstract
In this paper, a new vehicle counting and traffic flow monitoring system is designed based on deep learning and image recognition. For accurate recognition of vehicles, Mask R-CNN model has been adopted and improved. Vehicle dataset is set up to obtain the corresponding model weight as recognition backbone in software. In addition, two counting methods, regional counting method and tracking counting method, have been analyzed and combined for effective counting. The experimental results show that the recognition rate of the proposed system is almost 100% and the counting rate is about 93.5%. According to the counting results, the planning requirement of vehicle path optimization is realized.
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