Road scene semantic segmentation method based on category grouping in abnormal weather

Abstract

The invention discloses a road scene semantic segmentation method based on category grouping in abnormal weather. The method comprises the following steps: (1) preparing data; (2) road scene categories are grouped according to the importance of automatic driving safety in abnormal weather, it is guaranteed that important categories are separated from non-important categories, and a road scene semantic segmentation model is constructed according to the grouping result; (21) grouping the categories according to the importance of the automatic driving safety in the abnormal weather; (22) constructing a model according to a category grouping result; (3) inputting data into the model to obtain a segmentation result; (31) inputting data, and extracting data features; (32) acquiring full-category features; (33) encoding the category relationship by using the full-category features to obtain category relationship features; (34) completing category relationship feature decoding to obtain a segmentation result; (4) carrying out model iteration training; and (5) model testing. According to the method, the segmentation effect of important groups in abnormal weather can be ensured, and safe proceeding of an automatic driving task is ensured to the greatest extent. And meanwhile, an overall segmentation effect equivalent to that of a normal model can be obtained under the condition that the abnormal degree is relatively small.

Description

technical field [0001] The invention relates to the technical field of computer vision image segmentation, in particular to a road scene semantic segmentation method based on category grouping under abnormal weather. Background technique [0002] For autonomous driving tasks, most of the existing road scene semantic segmentation methods are based on normal weather conditions, without considering the more difficult and common abnormal weather conditions. Moreover, due to the difficulty of data collection and labeling under abnormal weather, various related data sets are lacking. Therefore, the general semantic segmentation model can only deal with scenes under normal weather conditions, and it is difficult to function under abnormal weather conditions. The segmentation effect is very poor, and it cannot be applied to the semantic segmentation task of road scenes under abnormal weather conditions. Considering that in abnormal weather, the most important thing to guarantee for...

AI Technical Summary

Problems solved by technology

These methods have solved the problem of road scenes under some abnormal weather conditions to a certain extent, but there are still problems: all categories are considered equally, regardless of the importance of the category to the task

The above methods do not consider the importance of the category to the task when segmenting, and under abnormal weather conditions, it is impossible to guarantee the segmentation effect of all categories, and it is meaningless to guarantee the segmentation accuracy of some irrelevant categories.

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