Autonomous Drone for High Accuracy Gender & Age Detection, Location Tracking and Speech Recognition using Machine Learning

The objective of the project is to realize a fully autonomous drone system capable of multitasking operations namely, speech recognition, location tracking and estimating the gender and age of the person. Performance is measured on various kinds of CNN-based models: YOLOv8, R-CNN, Wav2Vec, and Deep Speech 2 combining with the variables as accuracy, efficiency, and respond time. The project is divided into two subsections: one makes use of advanced models (YOLOv8, R-CNN, Wav2Vec, and Deep Speech 2) with 30 sample data in Group 1, while Group 2 explores the non-conventional with the same sample number. Statistical analysis with G Power equal to 80%, a threshold of significance value 0.05%, and a confidence interval of 95% was carried out. In the study,the proposed model showed high performance in recognition accuracy rates compared with conventional methods. Mean Accuracy for the proposed model is greater than 2.220, where the confidence interval would be between 1.993 and 2.447. The significance level has value P less than 0.05, showing strong support for the robustness and reliability of this model in recognition tasks. To conclude, the designed recognition system showed an improved recognition accuracy compared to conventional systems.