Abstract
The systems based on image processing for vehicle type recognition is becoming more and fiercer. It plays an important role in traffic safety. In order to improve the problems that traditional Convolutional Neural Network has low accuracy of feature extraction from the low-resolution image, a novel model based on Deep Convolutional Neural Network (DCNN) was proposed. In this paper, our work mainly contains two aspects both extraction of feature dimension and recognition of vehicle image. Firstly, the learning way was introduced, and the raw image of vehicle subsampled with several different sizes was operated with the filter corresponding each channel in a way of convolution to extract the feature dimension of image. Secondly, the features dimension obtained from every channel were merged by a full connected layer. Eventually, features used to recognize the type of vehicle is got. The experiment shows that the architecture of DCNN model has a efficient performance on the recognition of vehicle image. Compared with the traditional algorithm of CNN, the results of experiment show that the mode of DCNN can achieve 97.6% accuracy and a higher precision is got.
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Shi, L., Wang, Y., Cao, Y., Wei, L. (2017). Vehicle Type Recognition Based on Deep Convolution Neural Network. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_42
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DOI: https://doi.org/10.1007/978-981-10-6388-6_42
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