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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Using the improved YOLOv11 model to enhance computer vision applications for building crack detection algorithms.

Xiaohu Gao^1,2, Chunmei Cao^3,4, Xiaojing Yi⁵

¹School of Electronics and Information, Jiangsu Vocational College of Business, Nantong, 226011, China. gaoxiaohu1979@163.com.

Scientific Reports

|November 6, 2025

Summary

This summary is machine-generated.

This study enhances the YOLOv11 algorithm for building crack detection, achieving 88.6% accuracy. The improved model offers better precision and recall for identifying various crack types, ensuring structural safety.

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Area of Science:

Civil Engineering
Computer Vision
Artificial Intelligence

Background:

Urbanization increases the need for efficient structural safety monitoring.
Traditional crack detection methods suffer from low efficiency and high error rates.
Deep learning, especially YOLO algorithms, shows promise for automated crack detection.

Purpose of the Study:

To develop an enhanced YOLOv11 model for improved building crack detection accuracy and real-time performance.
To address limitations in detecting small and complex crack patterns.

Main Methods:

Introduced C3K2-SG module for complex background crack feature extraction.
Implemented FPSConv module for multi-scale crack detection.
Utilized Inner_MPDIoU loss function for enhanced small crack localization.

Main Results:

Achieved a detection accuracy (mAP@0.5) of 88.6%, a 4.6% improvement over YOLOv11.
Increased precision by 3.5% and recall by 7.6%.
Demonstrated superior performance in detecting small targets and complex crack types.

Conclusions:

The enhanced YOLOv11 model provides an efficient and accurate solution for building crack detection.
The model shows significant advantages for intelligent inspection and structural safety monitoring.