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A Novel Four-Step Algorithm for Detecting a Single Circle in Complex Images.

Jianan Cao1, Yue Gao1, Chuanyang Wang1

  • 1School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China.

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This study introduces an improved single-circle detection algorithm for industrial automation and navigation. The novel method enhances accuracy, efficiency, and stability compared to existing techniques.

Keywords:
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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Single-circle detection is crucial for industrial automation, intelligent navigation, and structural health monitoring.
  • Existing methods like random sample consensus and random Hough transform struggle with noisy, complex images, leading to poor accuracy and efficiency.

Purpose of the Study:

  • To develop a robust single-circle detection algorithm that overcomes the limitations of current methods.
  • To improve the accuracy, efficiency, and stability of circle detection in challenging image conditions.

Main Methods:

  • The proposed algorithm integrates Canny edge detection, a clustering algorithm, and an improved least squares method.
  • Performance was evaluated using self-captured image samples and the GH dataset.

Main Results:

  • The novel algorithm achieved an average detection error of two pixels.
  • Demonstrated superior accuracy, efficiency, and stability compared to random sample consensus and random Hough transform.

Conclusions:

  • The combined approach offers a significant advancement in single-circle detection.
  • The algorithm is well-suited for applications requiring precise circle identification in complex environments.