Road crack inspection method based on unmanned aerial vehicle and image recognition

By combining a high-precision instance segmentation algorithm and an Osprey search algorithm, pixel-level identification and adaptive path optimization of road cracks by UAVs were achieved, solving the problems of insufficient crack identification accuracy and path optimization in existing technologies, and improving the automation and intelligence level of inspection.

CN120877149AInactive Publication Date: 2025-10-31HUNAN UNIV OF SCI & ENG
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Patent Information

Application Number
CN202510977056.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-16
Publication Date
2025-10-31
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing drone inspection technology struggles to achieve instance-level segmentation and precise location of road cracks, and lacks dynamic adaptive capabilities based on real-time detection results, making it impossible to optimize inspection paths, resulting in low recognition accuracy and efficiency.

Method used

By combining a high-precision instance segmentation algorithm and an Osprey search algorithm, pixel-level crack identification and adaptive path optimization are achieved. Image data is collected in real time by UAVs, and the inspection strategy is dynamically adjusted to form a closed-loop linkage of detection, analysis and path adaptation.

Benefits of technology

It improves the detection accuracy and coverage efficiency of highway crack inspection, reduces the risk of missed and false detections, enhances the automation and intelligence level of inspection, and provides scientific data decision support.

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Abstract

The invention discloses a road crack inspection method based on an unmanned aerial vehicle and image recognition, and the method comprises the following steps: S1, enabling the unmanned aerial vehicle to collect a road image according to a preset route, and carrying out the image preprocessing; s2, detecting and segmenting cracks by an instance segmentation network, and generating a crack feature data set; s3, optimizing the inspection path by using an eagle search algorithm, and outputting an optimized route scheme; s4, continuing to inspect and collect images according to the optimization scheme, circulating the steps S1 to S3, and performing dynamic closed-loop optimization; and S5, filing crack characteristics and path information, and generating a distribution report and trend analysis. According to the invention, high-precision automatic identification of the road crack and intelligent adaptive optimization of the inspection path of the unmanned aerial vehicle are realized, and the inspection efficiency and the detection accuracy are improved.
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