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Vehicle model recognition method and system based on improved deep learning

A technology of deep learning and vehicle identification, applied in the field of vehicle identification methods and systems based on improved deep learning, to achieve the effects of suppressing invalid features, improving recognition accuracy, and high recognition accuracy

Active Publication Date: 2020-08-11
YANSHAN UNIV
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AI Technical Summary

Problems solved by technology

Although deep learning has been widely used in image recognition in recent years, the current convolutional neural network mostly improves the network from a single aspect of deepening the depth of the network and widening the feature channel of the network.

Method used

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  • Vehicle model recognition method and system based on improved deep learning
  • Vehicle model recognition method and system based on improved deep learning
  • Vehicle model recognition method and system based on improved deep learning

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Embodiment Construction

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0046] The purpose of the present invention is to provide a car model recognition method and system based on improved deep learning, which adopts a composite model expansion method and a deep learning image recognition and classification method that includes an attention mechanism, effectively extracts image features, and reduces the external environment for image feature extraction. The impact can improve the accuracy of vehicle identification.

[0047] In or...

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Abstract

The invention relates to a vehicle model recognition method and system based on improved deep learning. The method comprises the steps that a traffic gate vehicle data set is cut, sorted and classified to obtain classified data, and the classified data comprises a training set, a verification set and a test set; training the improved deep learning Effective Net network according to the training set to obtain a trained deep learning network, the training of the deep learning Effective Net network being based on adjustment of a balance relationship among the network depth, the network width andthe resolution; optimizing the trained deep learning network according to the verification set to obtain an optimal deep learning network; and inputting the test set into the optimal deep learning network for recognition, and performing classification by adopting a softmax classifier to obtain a network recognition result. According to the invention, the vehicle model recognition precision can beimproved.

Description

technical field [0001] The invention relates to the field of vehicle identification and classification, in particular to a vehicle identification method and system based on improved deep learning. Background technique [0002] With the development and application of pattern recognition technology in the field of intelligent transportation, vehicle type recognition has become a very important part of the intelligent transportation system. With the development of social economy and science and technology, the traffic field is facing more and more vehicles and various frequent traffic problems. Vehicle type identification can count the number of different types of vehicles, improve the work efficiency in traffic flow monitoring, and can be used in the investigation The system identifies camouflaged vehicles to improve the efficiency of solving crimes. It can also combine the registered information of vehicles to quickly identify vehicles, thereby reducing traffic congestion cau...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/34G06N3/04G06N3/08
CPCG06N3/08G06V10/267G06V2201/08G06N3/045G06F18/2415
Inventor 张秀玲魏其珺康学楠万庭波
Owner YANSHAN UNIV
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