Vehicle front trafficability analyzing method based on convolution nerve network

Abstract

The invention discloses a vehicle front trafficability analyzing method based on a convolution nerve network. The method comprises the following steps: first, a vidicon arranged in the front of a vehicle is used for collecting a large number of actual vehicle traveling environment images; a Gamma rectification function is used for pre-processing the images; training of the convolution nerve network is conducted. According to the method, a nonlinear function superimposed Gamma rectification method is used for pre-processing the images, so that influence of light illumination of strong changes on identification of objects is avoided, and the image resolution ratio is improved. According to the method, a geometry normalization method is used, so that the resolution ratio difference caused by identifying the distance of an object distance vidicon is reduced. The convolution nerve network LeNet-5 adopted in the method can extract implicit expression characteristics with class distinguishing capacity and is simple in extracting process. The LeNet-5 is combined with a local receptive field, weight share and secondary sampling to ensure robustness of simple geometry deformation, reduce training parameters of the network, and simplify the structure of the network.

Description

technical field [0001] The invention belongs to the technical field of safety assisted driving and intelligent transportation, relates to a vehicle front passability analysis method, in particular to a vehicle front passability analysis method based on a convolutional neural network by collecting video images in front of a vehicle through a camera. Background technique [0002] The trafficability analysis in front of the vehicle belongs to the field of intelligent transportation facing the environment perception outside the vehicle. It refers to the analysis of the driving safety of the detected environment based on advanced means such as sensor technology, computer technology or communication technology, and finds out the existing safety hazards. Alert and warn drivers or lay the groundwork for driverless vehicle navigation. At present, based on the video image information in front of the vehicle collected by the camera, the research on the trafficability analysis using the...

AI Technical Summary

Problems solved by technology

Among them, the reconstruction-based method is based on three-dimensional or 2.5-dimensional reconstruction technology, and judges whether it is passable from a spatial perspective. It is difficult to avoid the inherent serious ambiguity, small reconstruction range, and poor real-time performance of three-dimensional reconstruction.

In the recognition-based image understanding methods, there are mainly algorithms based on modeling and template matching, general neural networks, support vector machines, self-supervised learning, methods based on statistical learning, etc. These methods need to extract explicit features of the target, extract The process is complicated, it is easy to cause the loss of important information, and the ability to adapt to the environment is poor

[0004] For the structured road environment with strong illumination changes, if the original image is directly recognized, there will be a lot of interference information, and the process of explicit feature extraction is complicated, and the distance between the target and the camera will cause a difference in resolution

In addition, changes in lighting will affect image quality and reduce image resolution

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