Effective recognition design in 8-ball billiards vision systems for training purposes based on Xception network modified by improved Chaos African Vulture Optimizer DOI Creative Commons

WenKai Pan,

Dong Zhu,

Ju-Tao Wang

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июнь 17, 2024

Abstract This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in development robot vision system specifically designed for 8-ball billiards. The sport billiards, with its various games ball arrangements, unique challenges robotic systems. proposed methodology addresses these through two main components: object detection pattern recognition. Initially, robust algorithm is employed to detect billiard balls using space transformation thresholding techniques. followed by determining position table strategic cropping isolation primary area. crucial phase involves intricate task recognizing patterns differentiate between solid striped balls. To achieve this, modified convolutional neural network utilized, leveraging Xception optimized an innovative known as Improved Chaos African Vulture Optimization (ICAVO) algorithm. ICAVO enhances network's performance efficiently exploring solution avoiding local optima. results this study demonstrate significant enhancement recognition accuracy, Xception/ICAVO model achieving remarkable rates both paves way more sophisticated efficient billiards robots. implications extend beyond highlighting potential advanced systems applications. successful integration processing, learning, optimization shows effectiveness methodology. has far-reaching that go just cutting-edge technology can be utilized detecting tracking objects different sectors, transforming industrial automation surveillance setups. By combining algorithms, proves flexibility. approach sets stage creating productive industries.

Язык: Английский

Effective recognition design in 8-ball billiards vision systems for training purposes based on Xception network modified by improved Chaos African Vulture Optimizer DOI Creative Commons

WenKai Pan,

Dong Zhu,

Ju-Tao Wang

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июнь 17, 2024

Abstract This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in development robot vision system specifically designed for 8-ball billiards. The sport billiards, with its various games ball arrangements, unique challenges robotic systems. proposed methodology addresses these through two main components: object detection pattern recognition. Initially, robust algorithm is employed to detect billiard balls using space transformation thresholding techniques. followed by determining position table strategic cropping isolation primary area. crucial phase involves intricate task recognizing patterns differentiate between solid striped balls. To achieve this, modified convolutional neural network utilized, leveraging Xception optimized an innovative known as Improved Chaos African Vulture Optimization (ICAVO) algorithm. ICAVO enhances network's performance efficiently exploring solution avoiding local optima. results this study demonstrate significant enhancement recognition accuracy, Xception/ICAVO model achieving remarkable rates both paves way more sophisticated efficient billiards robots. implications extend beyond highlighting potential advanced systems applications. successful integration processing, learning, optimization shows effectiveness methodology. has far-reaching that go just cutting-edge technology can be utilized detecting tracking objects different sectors, transforming industrial automation surveillance setups. By combining algorithms, proves flexibility. approach sets stage creating productive industries.

Язык: Английский

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