A method and system for identifying illegal buildings using drones based on multi-threaded training mechanism

By adopting a multi-threaded training mechanism in the drone violation building identification system, the problems of low recognition efficiency and slow training speed in the existing technology are solved, and efficient and accurate identification and model training of violation building are achieved.

CN115131663BActive Publication Date: 2025-03-21CHENGDU XIEEN SCI & TECH CO LTD
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Patent Information

Application Number
CN202210777562.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-07-01
Publication Date
2025-03-21
Estimated Expiration
2042-07-01

AI Technical Summary

Technical Problem

The existing technology has problems in the identification of illegal buildings in cities, such as low recognition efficiency, large amount of resources, slow training speed, weak generalization, unstable convergence and high missed detection rates.

Method used

The drone violation building recognition method based on a multi-threaded training mechanism is adopted. High-resolution images are collected through the drone adaptive image capturer, image preprocessing is performed to generate standard images, and illegal building recognition model with a multi-threaded training mechanism is built, and model training and stable convergence are accelerated using the multi-threaded training mechanism.

Benefits of technology

The recognition performance of illegal buildings is improved, the robustness and generalization capabilities of the model are enhanced, the training time is shortened, and the recognition efficiency and accuracy are improved.

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Abstract

The main purpose of the present invention is to provide a method for identifying illegal buildings using a drone based on a multi-threaded training mechanism. The method first uses a drone adaptive image capturer to collect images and obtain multiple original high-altitude building images. The multiple original high-altitude building images are then preprocessed to generate multiple standard high-altitude building images and multiple groups of authentic illegal building discriminant vectors of standard high-altitude building images. According to the multiple standard high-altitude building images, an illegal building identification model based on a multi-threaded training mechanism is constructed to obtain multiple groups of predictive illegal building discriminant vectors. The multiple standard high-altitude building images, the multiple groups of predictive illegal building discriminant vectors and the multiple groups of authentic illegal building discriminant vectors are used as data samples to train the illegal building identification model based on the multi-threaded training mechanism, and finally the efficiency and performance of the illegal building identification model are improved. The present invention solves the problems of slow training speed, unstable convergence and poor recognition performance in traditional illegal building identification methods.
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