Lane line detection method based on structural information

Abstract

The invention discloses a lane line detection method based on structural information. The lane line detection method comprises the following steps: 1) acquiring data; 2) preprocessing the data; 3) constructing a model; 4) defining a loss function; 5) training a model; and 6) verifying the model. According to the method, the multi-scale features of the image are extracted by combining the deep convolutional neural network, the features of a lane line can be enhanced by a semantic information guided attention mechanism, the structural features of the lane line can be captured by multi-scale deformable convolution, the segmentation accuracy is improved by a decoding network, and the detection of the lane line is completed more accurately.

Description

technical field [0001] The invention relates to the technical fields of computer vision and deep learning, in particular to a lane line detection method based on structural information. Background technique [0002] Semantic segmentation is one of the important topics in the field of computer vision, and its core task is to convert input data (such as planar images) into masks that can highlight regions of interest. As one of the core tasks in computer vision and image understanding, semantic segmentation helps to achieve more advanced and complex computer vision tasks, which is of great research value and industrial value. [0003] In recent years, with the rapid development of deep learning, deep convolutional neural networks have also made major breakthroughs in the field of semantic segmentation. Semantic segmentation methods based on deep convolutional neural networks are widely used in various scenarios, such as geological detection, facial segmentation, precision agr...

AI Technical Summary

Problems solved by technology

Existing lane line detection methods based on deep learning are dedicated to fusing the features of the context to extract more continuous lane lines, but their methods are prone to missing or misdetecting lane lines when the lane lines are not obvious (such as night roads, etc.)

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