Data deep fusion image segmentation method for multispectral rescue robot

Abstract

The invention provides a data deep fusion image segmentation method for a multispectral rescue robot, and aims to further improve the precision of image semantic segmentation of the rescue robot, improve the accuracy of troubleshooting and analysis of the rescue robot in a disaster site, and perform autonomous detection work without manual command and control. The method comprises the following steps: generating a target segmentation training data set; constructing a U-shaped network for identifying single-spectrum single-target image segmentation; establishing an evidence theory-Bayesian two-stage decision fusion model for image semantic segmentation; and training to obtain a semantic segmentation model of multispectral data fusion. The evidence theory-Bayesian two-stage decision fusion model for image semantic segmentation is obviously improved in the aspects of accuracy, recall rate, accuracy and the like, the robot can autonomously check a disaster site in a complex disaster reliefenvironment, the detection accuracy and efficiency are improved, the labor cost is reduced, the disaster relief time is shortened, and casualties are reduced.

Description

technical field [0001] The invention relates to an image segmentation method, in particular to a data deep fusion image segmentation method for multi-spectral rescue robots. Background technique [0002] Earthquakes, mudslides, tsunamis and other natural disasters frequently occur around the world today, and the building structures at the disaster site collapse, resulting in huge changes in the terrain and environment. The space is small and unstable, which seriously threatens human safety. Precious lives, so how quickly and efficiently rescuers carry out rescue work is related to the lives of trapped people. With the continuous advancement of science and technology, rescue robots have begun to be used to enter some dangerous and complex disaster sites to carry out rescue, solving the problems of traditional rescue search and rescue time constraints, disaster site survivors and rescue personnel's life safety cannot be guaranteed. Rescue robots are intelligent robots that ca...

AI Technical Summary

Problems solved by technology

Due to the poor scene understanding ability and low work efficiency of traditional semantic segmentation methods in complex environments such as unstructured, diverse targets, irregular shapes, illumination changes, and object occlusions, in high-risk disaster relief sites such as fires, explosions, and earthquakes, in order to Accurate and efficient on-site inspections can reduce the work pressure of rescuers, and the accuracy of image semantic segmentation still needs to be further improved

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