Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (1): 303-308.doi: 10.11896／j.issn.1002-137X.2019.01.047

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Study on Bayonet Recognition Engine Based on Cascade Multitask Deep Learning

HE Xia, TANG Yi-ping, YUAN Gong-ping, CHEN Peng, WANG Li-ran   

  1. (School of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2017-10-16 Online:2019-01-15 Published:2019-02-25

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Abstract: Aiming at the complexity of environment,the diversity of requirements,the relevance of tasks and the real-time of identification in the process of converting the unstructured video data of bayonet into the intelligent structured information,this paper proposed a method of bayonet recognition engine based on cascade multitask deep learning.This method makes full use of the relationship between segmentation and detection recognition tasks to achieve high-precision,efficient,synchronous and real-time recognition of a variety of basic information of bayonet vehicles (motorcycle types,brands,series,colors and license plates etc.).First,the deep convolutional neural network is used to automatically extract the depth feature and the logical regression is performed on the feature map to extract the interested region from the complex background (including multi-vehicle object).And then the multitask deep learning network is used to achieve multilevel multitask recognition for the extracted vehicle objects.Experimental results show that the proposed method is superior to the traditional computer vision method and the existing recognition engine technology based on deep learning in terms of recognition accuracy and efficiency,and the accuracy of recognizing and detecting the motorcycle types,brands,series and license plates is more than 99% respectively,and the detection efficiency is increased by 1.6times.

Key words: Bayonet recognition engine, Cascade network, Convolutional neural network, Deep learning, Multitask deep learning

CLC Number: 

  • TP391.4
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