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Related Experiment Video

Updated: Jan 26, 2026

Generating Lap Joints Via Friction Stir Spot Welding on DP780 Steel

Generating Lap Joints Via Friction Stir Spot Welding on DP780 Steel

Published on: August 13, 2019

Angle aided circle detection based on randomized Hough transform and its application in welding spots detection.

Qiao Kang Liang^1,2,3, Jian Yong Long^1,2, Yang Nan¹

¹College of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, China.

Mathematical Biosciences and Engineering : MBE

|April 6, 2019

Summary

This study introduces an Angle-Aided Circle Detection (AACD) algorithm, improving upon the randomized Hough transform for efficient and accurate circle detection in images. The method enhances computational efficiency and robustness for complex scenarios.

Keywords:

angle aided randomized Hough transform circle detection image processing machine vision welding spot

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Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

The Hough transform is a key technique in image analysis for detecting shapes like circles.
Traditional randomized Hough transform methods can be computationally intensive.

Purpose of the Study:

To propose a novel Angle-Aided Circle Detection (AACD) algorithm.
To reduce the computational complexity of the randomized Hough transform.
To enhance robustness and accuracy in circle detection.

Main Methods:

The proposed AACD algorithm utilizes a region proposal method to optimize random sampling.
This approach minimizes invalid parameter space accumulation.
It is based on the randomized Hough transform framework.

Main Results:

The AACD algorithm significantly reduces computational load compared to traditional methods.
Demonstrates robustness in detecting multiple circles under complex conditions.
Exhibits strong anti-interference capabilities.

Conclusions:

The Angle-Aided Circle Detection algorithm offers an efficient and accurate solution for circle detection.
Successfully validated for real-world applications like welding spot detection in automotive manufacturing.
The method proves effective in complex image analysis tasks.