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Multi-task vehicle part recognition model, method and system based on deep learning

A technology for vehicle parts and recognition models, applied in the field of pattern recognition, can solve problems such as difficult convergence, troublesome data acquisition, and complex network training, and achieve the effects of improving the recognition rate of vehicle parts, easy data acquisition, and high recognition efficiency

Inactive Publication Date: 2021-08-03
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 recognition model, method and system based on deep learning, including: establishing a vehicle component database based on a vehicle image database and marking the vehicle components, and performing image data enhancement on the vehicle component database to obtain vehicle component training set; use the vehicle part training set to train the deep residual network, and obtain the vehicle part recognition network; based on the vehicle image database, count the probability of simultaneous occurrence of different types of multiple vehicle parts, and obtain the joint probability of multiple vehicle parts, based on multiple vehicle parts The joint probability of the multi-task vehicle part recognition data set and corresponding multi-label is established; it is used to train the vehicle part recognition network to obtain a multi-task vehicle part recognition model. The multi-task vehicle part recognition model is used to identify the image of the vehicle to be detected, and the probability of each vehicle part in the image of the vehicle to be detected is obtained. The invention has simple network training, easy convergence, easy data acquisition and high recognition accuracy.

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...

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Application Information

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Patent Type & Authority Patents(China)
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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