With the development of neural networks, detection accuracy and speed constantly improved. However, the detection effect is still insufficient in some special scenarios such as traffic environment. Therefore, we combine neural network with prior knowledge to improve its performance in vehicle detection. In this paper, we propose two effective prior: proportion and area prior to enhance the vehicle detection ability of neural network in traffic environment. The proportion and area prior is the statistical data of the vehicle at different angles and distances from the camera. In the traffic monitoring video, the proportion of most vehicles is mainly divided into several values. The area of all vehicles is also included between the thresholds. Experimental results demonstrate the effect of prior. Detection effect for vehicle in traffic environment of sample network in this paper increase by 6.56%.


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    Title :

    Vehicle Detection Based on Area and Proportion Prior with Faster-RCNN


    Additional title:

    Smart Innovation, Systems and Technologies


    Contributors:


    Publication date :

    2020-07-17


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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