Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Overview

This study presents a protocol utilizing the directional gradient histogram technique to analyze concrete image samples under various vibration states. It incorporates a support vector machine for machine learning, achieving efficient image recognition with minimal training sample requirements.

Key Study Components

Area of Science

• Image recognition
• Machine learning
• Concrete analysis

Background

• Directional gradient histogram technique is employed for feature extraction.
• Support vector machines are used for classification tasks.
• The study addresses the challenges of sample size and computational demands.
• Efficient processing is demonstrated on standard laptop hardware.

Purpose of Study

• To develop a method for recognizing concrete images under varying conditions.
• To reduce the number of training samples needed for effective machine learning.
• To lower the computational requirements for image recognition tasks.

Methods Used

• Directional gradient histogram technique for image feature extraction.
• Support vector machine for classification.
• Image segmentation with a size limit of 128 projects.
• Statistical angle inverse with 12 directional vectors.

Main Results

• The recognition process completes training differentiation in 50 seconds on a 2.3 GHz CPU.
• Significant reduction in sample size requirements for effective recognition.
• Low computational demands make the method accessible for standard laptops.
• Demonstrated effectiveness in recognizing concrete images under various vibration states.

Conclusions

• The proposed method offers a practical solution for concrete image recognition.
• It minimizes the need for extensive training samples.
• The approach is efficient and suitable for use on common computing hardware.

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