Scene detection reference model training method and system and application method

Abstract

The invention relates to a scene detection reference model training method, a scene detection reference model training system and an application method, and aims to accurately and effectively identify a production construction project under construction and check a water and soil conservation scheme and a water and soil conservation prevention and control responsibility range which are recorded and reported by a national water and soil conservation supervision and management system. Whether the production and construction project breaks rules and is not built in batches and the disturbance exceeds the control responsibility range can be determined, so that the full-coverage supervision of the production and construction project is realized.

Description

technical field [0001] The invention relates to the field of remote sensing image processing, in particular to a scene detection reference model training method, system and application method. Background technique [0002] With the development of computer vision technology and remote sensing image interpretation methods, significant progress has been made in target detection and semantic segmentation based on high-resolution optical remote sensing images. The detection of special ground objects, such as buildings, aircraft, and vehicles, has achieved high accuracy. However, the category definition of these features is relatively clear, and the diversity within the category is relatively limited. In contrast, complex ground scenes represented by production and construction projects lack widely accepted definitions of semantic concepts. The images contain a variety of artificial and natural features, are highly unstructured within the class, have significant differences, and ...

AI Technical Summary

Problems solved by technology

However, the category definition of these features is relatively clear, and the diversity within the category is relatively limited.

In contrast, complex ground scenes represented by production and construction projects lack widely accepted definitions of semantic concepts, images contain a variety of artificial and natural features, are highly unstructured within the class, have significant differences, and the number of training samples is relatively small

Existing target detection methods are difficult to be directly applied to the overall detection of complex ground scenes such as production and construction projects

[0003] Therefore, how to solve the problem of low overall detection accuracy of complex ground scenes in production and construction projects has become an urgent technical problem in this field.

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