Semantic segmentation method in automatic driving scene based on BiSeNet

Abstract

The invention relates to a semantic segmentation method in an automatic driving scene based on BiSeNet, and the method comprises the following steps: S1, collecting urban street image data, and carrying out the preprocessing; S2, labeling the preprocessed image data to obtain labeled image data; S3, performing data enhancement on the labeled image data, and taking the enhanced image data as a training set; S4, constructing a BiSeNet neural network model, and training the model based on the training set; and S5, preprocessing the video information acquired by the camera, and performing semanticsegmentation on the urban streets in the camera according to the trained BiSeNet neural network model. The safety of automatic driving and the accuracy and rapidity of road scene segmentation can beeffectively improved.

Description

technical field

[0001] The invention relates to the fields of pattern recognition and computer vision, in particular to a semantic segmentation method in a BiSeNet-based automatic driving scene. Background technique

[0002] Semantic image segmentation is an essential part of modern autonomous driving systems, as an accurate understanding of the scene around the car is critical for navigation and action planning. Semantic segmentation can help autonomous vehicles identify drivable areas in an image. Since the emergence of Fully Convolutional Networks (FCN, Fully Convolutional Networks), convolutional neural networks have gradually become the mainstream method for processing semantic segmentation tasks, many of which are directly borrowed from convolutional neural network methods in other fields. In the past ten years, many scholars have made great efforts in the creation of semantic segmentation datasets and algorithm improvement. Thanks to the development of deep learning...

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