Badminton Service Foul System based on machine vision

Introduction: In today's sports activity landscape, the identity of fouls and misguided moves in badminton poses extensive challenges. A badminton carrier foul takes place when a player fails to stick to the guidelines in the course of a serve. Common fouls such as improper position, foot placement and racket position.Aim: The purpose of this study is to improve an advanced machine version system using Archerfish looking Optimization-driven intelligent ResNet50 (AHO-IResNet50) to enhance the accuracy of service foul identification in badminton, thereby improving match score analysis and decision-making for the Badminton practices.Methodology: The dataset were obtained that incorporates numerous images capturing various phases of badminton matches, with racket positions and player movements during service, to train the proposed model. A discrete Wavelet rework (DWT) algorithm is utilized to extract the huge features. The proposed method includes an AHO algorithm to fine-tune the IResNet50 model for more desirable badminton service foul identification. This proposed approach leverages the adaptability of Archerfish hunting strategies to optimize IResNet50's parameters, enhancing accuracy and reducing errors in badminton foul recognition.Results: The suggested recognition model is applied in a Python software program. During the result analysis phase, we evaluated the model's efficacy across diverse parameters along with accuracy (94.7%), precision (86.7%), recall (84.9%), and specificity (93.5%). We additionally conduct comparative analyses with existing methodologies to examine the effectiveness of our suggested classification. Conclusion: The acquired findings show the efficacy and superiority of the proposed framework, significantly lowering errors and improving the accuracy of foul identification