Vehicle detection method and device based on depth learning
A vehicle detection and deep learning technology, applied in the field of computer vision, can solve the problems of lack of adaptability to different monitoring scenarios, reduced detection effect, etc., to achieve good vehicle recognition effect, avoid repeated calculation, and improve the effect of detection speed
Inactive Publication Date: 2017-09-29
WELLONG ETOWN INT LOGISTICS
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Problems solved by technology
This method lacks adaptability to different monitoring scenarios and can only detect vehicles from a single perspective. If the shooting angle of the video camera changes, the detection effect of this method will be greatly reduced
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[0036] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
[0037] figure 1 It is a flowchart of Embodiment 1 of the vehicle detection method based on deep learning of the present invention, such as figure 1 As shown, the vehicle detection method based on deep learning in this embodiment may specifically include the following steps:
[0038] S101. Acquire a vehicle sample image, and perform preprocessing on the vehicle sample image.
[0039] The vehicle sample image includes a vehicle sample image including a vehicle image and a vehicle sample image only including a background image. A vehicle sample image that only contains a background image, that is, a vehicle sample image that does not contain a vehicle image. This can impr...
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The invention discloses a vehicle detection method and device based on depth learning. The method comprises a vehicle sample image is acquired, and the vehicle sample image is pre-processed; a convolutional neural network model is constructed based on the vehicle sample images after pre-processing; to-be-detected vehicle images are detected through utilizing the convolutional neural network model, and the detection result is further outputted. The method is characterized in that the convolutional neural network model is constructed based on the vehicle sample images after pre-processing, the to-be-detected vehicle images are detected through utilizing the convolutional neural network model, and the detection result is further outputted. The method is advantaged in that plenty of repeated calculation can be avoided, the detection speed is improved, and the better vehicle identification result can be realized.
Description
technical field [0001] The invention relates to the technical field of computer vision, in particular to a vehicle detection method and device based on deep learning. Background technique [0002] Computer vision is an important interdisciplinary subject in the fields of artificial intelligence and image processing. Early solutions to computer vision tasks mainly consisted of two steps, one was to manually design features, and the other was to build a shallow learning system. With the development of artificial intelligence, deep learning was formally proposed in 2006. Deep learning originated from multi-layer artificial neural networks, and has been successfully applied in fields such as computer vision, natural language processing, and intelligent search. Currently existing deep learning networks mainly include convolutional neural networks, deep belief networks, and stacked autoencoders. Convolutional neural networks are widely used in image processing due to their inte...
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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V20/584G06V2201/08G06N3/045
Inventor 施文进施俊
Owner WELLONG ETOWN INT LOGISTICS