Road scene semantic segmentation method based on multi-model fusion

Abstract

The invention discloses a road scene semantic segmentation method based on multi-model fusion. The method comprises the following steps: building a multi-classification model and a binary-classification model; respectively carrying out end-to-end training on the multi-classification model and the two-classification model to obtain an optimal weight value enabling a loss function to be minimum; performing multi-classification prediction and dichotomy prediction on the road scene image by using the optimal weight value to form a preliminary segmentation result image; performing image post-processing on a preliminary segmentation result image formed by binary classification prediction; and fusing the preliminary segmentation result image formed by multi-classification prediction and the segmentation result image after image processing. According to the multi-classification model, visual attention is added to a feature fusion part on the basis of an original HRNet, so that effective feature maps obtain larger fusion weights, invalid or poor-effect feature maps obtain smaller fusion weights, the pixel representation capability of the multi-classification model is improved, and a better segmentation result is obtained.

Description

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AI Technical Summary

Benefits of technology

This patented technology combines multiple types of data from various sources like sensors into one more powerful tool called an integrated system. It achieves this through advanced techniques such as machine learning algorithms, deep learning methods, and generative adversarial networks. By doing these things together, it becomes able to identify patterns within complex datasets accurately even when there are many similarities between them. Overall, this innovations improve both vehicle safety and efficiency during driving operations.

Problems solved by technology

This patents discusses how traditional semisectoric or region specific techniques like FC layers limit their ability to recognize large areas accurately due to limitations with full connection type network architectures used by these models. Additionally, current approaches require that each frame from one aspect separately analyze every single section within it before transferring its content towards another part of the system.

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