Road image intelligent acquisition and identification method based on deep learning

A technology of intelligent collection and identification method, applied in the field of video image and computer vision, can solve the problems of long model training time, low recognition accuracy, and inaccurate positioning, so as to improve the level of intelligent management, high degree of automation, and improve efficiency Effect

Pending Publication Date: 2019-08-30
上海卡罗网络科技有限公司
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Problems solved by technology

The two-stage algorithm Faster-RCNN has precise positioning and recognition effects. However, when the number of categories is large, the model training time is long, the recognition speed decreases proportionally, and the false recognition rate also increases.
The one-stage al...

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  • Road image intelligent acquisition and identification method based on deep learning

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Embodiment Construction

[0019] The present invention will be described in detail below in conjunction with the embodiments and the accompanying drawings.

[0020] The invention performs intelligent identification based on video images captured by image acquisition hardware. First, the road video image is captured by the road image acquisition hardware, the key frame in the video image is extracted through the program, and then the key frame is detected and identified by the image intelligent recognition algorithm to determine the location, category and level of the disease in the road image.

[0021] Such as figure 1 As shown, a method for road image intelligent collection and recognition based on deep learning of the present invention realizes the detection of road diseases through a three-level detection network, and the specific steps are as follows:

[0022] First, after the first-level detection network processing, in the first-level detection network, a sufficient amount of different types of ...

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Abstract

The invention provides a road image intelligent acquisition and identification method based on deep learning. The road image intelligent acquisition and identification method comprises the following steps: shooting a road video image through road image acquisition hardware, extracting key frames in the video image through a program, detecting and identifying the key frames through an image intelligent identification algorithm, and determining a disease position, a disease category and a disease grade in the road image. According to the invention, positioning of the road disease area and classification tasks of types and grades can be completed, and manual intervention is not needed, and unmanned operation is completely realized, and the automation degree is high, and the fault tolerance ishigh, and the road maintenance patrol efficiency can be greatly improved, and the intelligent management level of the industry is improved.

Description

technical field [0001] The invention relates to the technical fields of video images and computer vision, in particular to a method for intelligent collection and recognition of road images based on deep learning. Background technique [0002] Target detection and recognition in images has a wide range of practical needs in applications such as intelligent video surveillance, and it is also a popular development direction in the field of computer vision. Convolutional neural network (CNN) has a strong advantage in extracting deep-level features of images by performing operations such as convolution and pooling on images, and is widely used in the field of computer vision. [0003] The current mainstream deep learning-based target detection algorithms include Faster-RCNN, SSD, YOLO, etc. The two-stage algorithm Faster-RCNN has precise positioning and recognition effects. However, when the number of categories is large, the model training time is long, the recognition speed d...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0004G06T2207/10016G06T2207/20081G06T2207/20084G06T2207/30132G06F18/241
Inventor 汪舰
Owner 上海卡罗网络科技有限公司
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