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Vehicle type recognition method based on convolutional neural network under vehicle-mounted environment

A convolutional neural network, vehicle recognition technology, applied in the field of target classification

Inactive Publication Date: 2018-11-27
珠海亿智电子科技有限公司
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  • Abstract
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Problems solved by technology

Although there have been a large number of working studies on hierarchical multi-label learning, they use traditional basic CNN models instead of applying fine-grained methods

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  • Vehicle type recognition method based on convolutional neural network under vehicle-mounted environment
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  • Vehicle type recognition method based on convolutional neural network under vehicle-mounted environment

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Embodiment Construction

[0038] In order to make the object, technical solution and advantages of the present invention clearer, the specific implementation manners of the identification method and system of the vehicle type according to the embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0039] (1) Datasets used in this invention

[0040] The experiments of the present invention are first carried out on the Stanford car dataset. The Stanford Automobile dataset contains 16,185 images of 196 types of automobiles. There are 8,144 training images and 8,041 testing images, and each category is roughly 5-5 split into training and testing sets. In the experiments of the present invention, the distribution of the training set and the test set is the same as the official distribution.

[0041] The...

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Abstract

The invention discloses a vehicle type recognition method based on a convolutional neural network under the vehicle-mounted environment. A semantic compact bilinear pooling method for vehicle type recognition is put forward, which combines the hierarchical tag tree and compact bilinear pooling and demonstrates superior performance on the CompCars data set and the Stanford auto data set. With application of the method, the compact bilinear pooling method uses semantic connection between different levels of semantics of the vehicle and makes them mutually enhanced during training. The softmax loss function is promoted to the loss avoidance function aiming at making full use of prior knowledge. Experiments show that the invention improves the accuracy of the vehicle type recognition task on the CompCars data set and the Stanford auto data set.

Description

technical field [0001] The invention relates to a target classification method based on the field of computer vision, mainly an improved vehicle identification method based on a caffe depth framework and a convolutional neural network. Background technique [0002] Deep learning and Convolutional Neural Networks (CNNs) have achieved astounding achievements in public safety in recent years. In public security systems, car-related tasks account for a large portion of all computer vision tasks. At present, license plate recognition has been widely used in traffic safety systems, and vehicle vehicle recognition has become an increasingly popular task in the field of computer vision. In 2013, Krause et al. released a dataset for car model recognition (Stanford Car Dataset). And various research works have been done in the field of computer vision for vehicle recognition. Compared with the recognition or classification of other general objects such as face recognition and Image...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/584G06V2201/08G06F18/29
Inventor 殷绪成陈敏捷李鑫杰杨春
Owner 珠海亿智电子科技有限公司
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