Semantic segmentation method for unmanned aerial vehicle aerial video based on UVid-Net

Abstract

The invention belongs to the technical field of semantic segmentation, and particularly relates to a semantic segmentation method for an unmanned aerial vehicle (UAV) aerial video based on UVid-Net. The method comprises the following steps: data collection: collecting a data set for unmanned aerial vehicle video semantic segmentation, carrying out the pixel-level marking of the type of the data set, and completing the construction of the data set needed by model training; data preprocessing including normalization, data division, image scaling and the like, and amplifying a data set to ensure a model training effect; model identification: constructing a semantic segmentation model based on UVid-Net, inputting training data, and completing the construction of a parameter model; model storage: when the loss function of the model is not reduced any more, storing the model; and model evaluation: performing performance evaluation on the segmentation result of the model through a plurality of evaluation indexes. According to the coding path, the time dynamic state of the video is captured by extracting features from multiple frames, the features of a coder layer can be reserved, and the semantic segmentation performance is improved.

Description

technical field [0001] The invention belongs to the technical field of semantic segmentation, and in particular relates to a semantic segmentation method of a drone aerial video based on UVid-Net. Background technique [0002] Aerial imagery analysis has been used to assess damage in the immediate aftermath of natural disasters. Usually, aerial images are captured by different imaging methods such as synthetic aperture radar (SAR) and hyperspectral imaging on satellites. In recent years, unmanned aerial vehicles (UAVs) have also been widely used in various applications such as disaster management, urban planning, wildlife tracking, and agricultural planning. Thanks to rapid deployment and customized flight paths, UAV imagery / video can provide additional finer detail and complement satellite-based imagery analysis methods for critical applications such as disaster response. Additionally, drone imagery can be used in conjunction with satellite imagery for better urban planni...

AI Technical Summary

Problems solved by technology

However, UAV image / video analysis is limited to object detection and recognition tasks, such as building detection, road segmentation, etc., and current work on semantic segmentation of UAV images or videos is limited.

[0003] Problems or defects in existing technologies: current semantic segmentation is the process of assigning predetermined class labels to all pixels in an image

However, there are still some difficulties in how to integrate temporal information in the process of semantic segmentation for extended video applications

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