Waterlogging intelligent identification method suitable for urban low-light environment and related equipment

By employing illumination normalization, multi-level feature extraction, content-style decoupling, and a hollow spatial pyramid module to process low-light images, the problem of difficult urban nighttime flood identification was solved, achieving highly accurate and adaptive flood identification.

CN121746915APending Publication Date: 2026-03-27SUN YAT SEN UNIV
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Patent Information

Application Number
CN202511874513.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-12
Publication Date
2026-03-27

AI Technical Summary

Technical Problem

Existing technologies struggle to identify urban flooding at night or in dimly lit conditions, resulting in a significant drop in image quality and consequently, a decline in recognition performance, which limits their practical application value.

Method used

A low-light image is processed using an illumination normalization module. Through multi-level progressive feature extraction, content-style decoupling, convolutional block attention, and a hole spatial pyramid module, combined with a decoder for progressive upsampling, binary classification of water and non-water areas is achieved.

Benefits of technology

It significantly improves the recognition accuracy and adaptability in complex and dynamic lighting environments, solves the performance bottleneck of existing technologies in low-light scenarios, and enhances the accuracy and practical application value of waterlogging identification.

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Abstract

The invention discloses a waterlogging intelligent identification method and related equipment suitable for an urban low-illumination environment, and the method comprises the steps: carrying out the illumination normalization processing of a low-illumination image, obtaining a normalized output image, extracting a multi-layer feature through a multi-layer gradual downsampling operation through a feature extraction module, and obtaining an intermediate feature map; decomposing the intermediate feature map into content features and style features, and further removing the style features from the content features by minimizing an orthogonal loss function to obtain decoupled content features; performing feature enhancement on the decoupled content features to obtain enhanced content features; based on the enhanced content features, capturing multi-scale context information through parallel convolution by using a cavity space pyramid module to obtain target features; and based on the target features, a decoder is used to carry out progressive up-sampling to output a segmentation mask, and a waterlogging identification result is obtained. The waterlogging identification accuracy in the low-illumination scene can be improved, and the method can be widely applied to the technical field of image processing.
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