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Multi-task vehicle component identification model, method and system based on deep learning

A vehicle component and recognition model technology, applied in the field of pattern recognition, can solve problems such as troublesome data acquisition, difficult convergence, complex network training, etc., and achieve the effect of easy data acquisition, improved vehicle component recognition rate, and high recognition efficiency

Inactive Publication Date: 2018-10-12
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

[0010] In view of the above defects or improvement needs of the prior art, the present invention provides a multi-task vehicle part recognition model, method and system based on deep learning, thereby solving the problems of complex network training and difficult convergence as the network deepens in the prior art. Troublesome technical issues with data acquisition

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[0044]In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0045] Softmax: The Softmax classifier is the generalization of the logistic regression classifier (two classes) to multiple classes. It is actually a normalized exponential function. First calculate the Softmax normalized probability, the Softmax function formula is as follows:

[0046] x i =x i -max(x 1 ,...,x n )

[0047]

[0048] x i Represents the score on category j, i represents ...

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Abstract

The invention discloses a multi-task vehicle component identification model, method and system based on deep learning. The method comprises the following steps: establishing a vehicle component database based on a vehicle image database and marking the vehicle component, performing image data enhancement on the vehicle component database to obtain a vehicle component training set; training a deepresidual network by using the vehicle component training set to obtain the vehicle component identification network; counting concurrence probability of different types of multiple vehicle componentsto obtain the joint probability of multiple vehicle components, and establishing a data set for the multi-task vehicle component identification and the corresponding multiple labels based on the jointprobability of multiple vehicle components; and training the vehicle component identification network to obtain the multi-task vehicle component identification model. The to-be-detected vehicle imageis identified by using the multi-task vehicle component identification model, thereby obtaining the probability of each vehicle component in the to-be-detected vehicle image. The network disclosed bythe invention is simple in training, easy to converge, easy to acquire data and high in identification accuracy rate.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and more specifically relates to a deep learning-based multi-task vehicle component recognition model, method and system. Background technique [0002] Object recognition algorithm is one of the important fields of image processing and pattern recognition research, and it is a hot research topic at present. The so-called target recognition refers to the realization of human visual function by computer, and its research goal is to enable the computer to have the ability to recognize the surrounding environment from one or more images or videos. The target recognition method is to use various matching algorithms to find the best match with the object model library according to the features extracted from the image. Its input is the image and the model library of the object to be recognized, and the output is the name of the object, attitude, position, etc. Target recognition methods g...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2415G06F18/214
Inventor 桑农杨丽秦李亚成高常鑫
Owner HUAZHONG UNIV OF SCI & TECH
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