Unmanned aerial vehicle based image collection method for irrigation canal system

By segmenting the grayscale image of the irrigation canal system into canal surface and non-canal surface regions, corner point and waterline features are obtained, and the robustness index is used to determine the stitching region. This solves the problem of computational redundancy in traditional image stitching methods and realizes efficient acquisition of irrigation canal system engineering images.

CN117575898BActive Publication Date: 2026-06-26YELLOW RIVER INST OF HYDRAULIC RES YELLOW RIVER CONSERVANCY COMMISSION
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Patent Information

Application Number
CN202311565828.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-11-22
Publication Date
2026-06-26
Estimated Expiration
2043-11-22

AI Technical Summary

Technical Problem

Traditional image stitching methods result in excessively large overlapping areas in the images to be stitched, leading to high computational redundancy and affecting the real-time performance and efficiency of irrigation canal system monitoring.

Method used

By segmenting the grayscale image of the canal system into canal surface regions and non-canal surface regions, corner point and waterline features of each region are obtained, corner point uniformity factors and regional feature intensity are calculated, and robustness index is used to determine the stitching region and adaptively adjust the image stitching range.

Benefits of technology

It improves the efficiency and accuracy of image stitching, reduces computational redundancy, and ensures the rapid acquisition of overall images of irrigation canal systems.

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Abstract

The application relates to the technical field of image processing, in particular to an image acquisition method for canal system engineering in an irrigation area based on a UAV (unmanned aerial vehicle). A canal system gray-scale image is segmented to obtain a canal surface region and a non-canal surface region. According to the corner point distribution characteristics in the non-canal surface region, a corner point uniformity factor is obtained, and according to the difference of the corner point uniformity factor, a region characteristic intensity is obtained. According to the number of corner points and the region characteristic intensity of the non-canal surface region, a non-canal surface traversal region robustness index is obtained. According to the water line mark length characteristics, water mark completeness is obtained, according to the distribution characteristics and distance characteristics of the pixel points, a canal surface region color distance difference is obtained, and according to the water mark completeness and the region color distance difference, a canal surface traversal region robustness index is obtained. According to the canal system traversal robustness index, the range of image splicing can be adaptively determined, the splicing efficiency is improved while the splicing effect is ensured, and the acquisition efficiency of the canal system overall view image is improved.
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