Hand Posture Recognition on Photographic Images using YOLO Algorithm in Comparison with Convolution Neural Network to Improve Accuracy

Aim: The main aim of the research work is to compare the performance of a novel YOLO algorithm in comparison with Convolution Neural Networks (CNN) to improve accuracy. Materials and Methods: In this research, Group 1 is considered as the YOLO algorithm and Group 2 is considered as CNN, recognizing the YOLO algorithm for public use when compared to CNN with higher significance value with 20 samples each group consists of 10 samples. The G power is 0.8, the alpha is 0.05, the beta is 0.2. Results: The accuracy of YOLO is significantly improved than CNN and there is a statistical significance observed as 0.0049
$(\mathbf{p} < \boldsymbol{0.05})$
. The accuracy for Group 1 is 95.66%, for Group 2 is 94.76%. Conclusion: YOLO algorithm is significantly more accurate compared to the convolution neural network.