Lane line detection method based on full-convolution network

Abstract

The invention provides a lane line detection method based on a full-convolution network and relates to the field of traffic information detection. According to the method, probability operation is performed on an output feature map of the full-convolution lane line detection network, the probability that a lane line appears in each region in an input picture is obtained, a prediction probability threshold is set, and extraction and detection of lane lines are realized. Through the method, detection of straight lane lines and curved lane lines can be realized simultaneously; a lane line detection loss function is used to train the full-convolution lane line detection network, so that the detection effect of the lane lines is improved; the convolutional neural network learns abstract features of the lane lines from a lane line classification dataset instead of simply extracting external features of the lane lines; detection of a newly input image can be realized just by storing a lane line detection network model, so that storage space is saved, and the method is suitable for vehicle-mounted embedded equipment; and the small and shallow full-convolution lane line detection network isadopted to perform detection acceleration, so that detection speed is high.

Description

technical field [0001] The invention relates to the field of traffic information detection, in particular to a lane line detection method Background technique [0002] Intelligent driving needs to perceive and understand the traffic environment and situation. The traffic environment of the vehicle includes surrounding vehicles, lane lines and traffic lights, etc. Lane line detection plays an extremely important role in controlling the vehicle to drive in a safe area. When the vehicle deviates greatly, the lane line detection can be used to alarm the driver in time, adjust the driving direction of the car, and avoid traffic accidents. [0003] Lane line detection technology is mainly divided into three types: detection technology based on color features, detection technology based on texture features and detection technology based on multi-feature fusion. Color features are divided into grayscale features and color features. For grayscale features, the grayscale of lane line...

AI Technical Summary

Problems solved by technology

Although the detection method based on multi-feature fusion has good detection effect, the image processing process is relatively complicated, and the requirements for the operating environment are high, so it is not suitable for vehicle-mounted embedded devices.

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