Road detection method based on Internet of Vehicles
A technology for road detection and Internet of Vehicles, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as unsatisfactory detection accuracy of small targets, reduce the amount of parameters in operation, improve detection speed, and improve detection accuracy. Effect
Inactive Publication Date: 2021-02-19
XIAN CREATION KEJI CO LTD
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However, the detection accuracy of existing detection methods, especially for small targets, is not ideal.
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[0062] For the above second implementation manner, optionally, the transition module includes two or three convolutional network modules, and the convolutional network modules are connected in series. Compared with using one convolutional network module, using two or three convolutional network modules in series can increase the complexity of the model and fully extract features.
[0063] S3, use the improved FPN network to perform top-down and densely connected feature fusion on feature maps of x different scales, and obtain the prediction results corresponding to each scale;
[0064] The following combination image 3 The structure of the improved FPN network, and its top-down and densely connected feature fusion method are introduced.
[0065] The improved FPN network includes x prediction branches Y whose scale increases sequentially 1 ~Y x ;Forecast branch Y 1 ~Y x The scale of is in one-to-one correspondence with the scales of the x feature maps; for example, imag...
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The invention discloses a road detection method based on Internet of Vehicles, which is applied to a vehicle-mounted terminal and comprises the following steps: acquiring a target road image shot by an image acquisition terminal; inputting into an improved YOLOv3 network, and performing feature extraction by using a backbone network in a dense connection form to obtain x feature maps of differentscales; wherein x is a natural number greater than or equal to 4; performing feature fusion on the x feature maps of different scales in a top-down and dense connection mode by using an improved FPN network to obtain a prediction result corresponding to each scale; based on all the prediction results, acquiring attribute information of the target road image, wherein the attribute information comprises the position and category of a target in the target road image; forming an improved YOLOv3 network by changing a residual module in a backbone network into a dense connection module on the basisof the YOLOv3 network, increasing a feature extraction scale, optimizing a feature fusion mode of an FPN network, pruning and guiding network recovery processing in combination with knowledge distillation.
Description
technical field [0001] The invention belongs to the field of image detection, and in particular relates to a road detection method based on Internet of Vehicles and vehicle-mounted electronic equipment. Background technique [0002] In recent years, the Internet of Vehicles technology has developed rapidly. Using this technology, vehicles in motion can use a new generation of information and communication technology to realize network connections between vehicles, pedestrians, road facilities, and service platforms, and improve the overall intelligent driving level of vehicles and traffic operation efficiency. [0003] In order to provide effective road information to the driver, so that the driver can plan the route in time, avoid emergency, and realize safe driving. Some researchers combine the neural network-based target detection algorithm with the Internet of Vehicles technology, take road images, upload them to the cloud for image detection, identify the type and loca...
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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08G06N5/02H04L29/08
CPCG06N3/082G06N5/02H04L67/12G06V20/588G06V10/462G06N3/045G06F18/241G06F18/253G06V10/806G06V10/82G06V10/95G06V10/454G06V20/58G06V10/255G06V2201/08G06V10/762G06V10/75H04L12/40H04L2012/40273
Inventor 刘晨左瑜
Owner XIAN CREATION KEJI CO LTD