Road driving area efficient segmentation method based on depth feature compression convolutional network

Abstract

The invention discloses a road driving area efficient segmentation method based on a depth feature compression convolutional network. The method aims to solve the problem that most current road segmentation methods based on deep learning are difficult to meet accuracy and real-time requirements at the same time. The method comprises: establishing a deep feature compression convolutional neural network; firstly, designing a standard convolutional layer and a pooling layer to perform preliminary compression on extracted road characteristics; by means of advantage of the expanded convolution layer that a receptive field can be increased, and optimizing the advantage, to make up road spatial position information loss caused by feature initial compression, then fusing and decomposing a convolutional layer to realize deep feature compression, finally proposing a layer-by-layer hierarchical up-sampling strategy with learnable parameters to decouple the deeply compressed features, then training the network, and inputting the road image to obtain a segmentation result. The depth feature compression convolutional neural network designed by the invention obtains a good balance between accuracy and real-time performance, and realizes efficient segmentation of a road driving area.

Description

technical field [0001] The invention belongs to the technical field of computer vision and intelligent vehicle environment perception, and relates to a road driving area segmentation method, in particular to an efficient road driving area segmentation method based on a deep feature compression convolution network. Background technique [0002] With the rapid growth of car ownership, urban traffic is facing enormous pressure. Problems such as serious road congestion, frequent traffic accidents, and environmental pollution have caused huge losses to the economy and society. In order to reduce these losses, it is an effective solution to improve the automation and intelligence of vehicles while strengthening road infrastructure construction and improving traffic laws and regulations. In this context, technologies such as advanced driver assistance systems and vehicle autonomous driving systems have received great attention and developed rapidly. An important prerequisite for t...

AI Technical Summary

Problems solved by technology

However, most current road segmentation models based on deep learning focus on the improvement of accuracy, and their real-time performance is generally difficult to meet the requirements of intelligent vehicle environment perception.

Images