A Dynamic Architecture to Control Multi-Rotors Using Hand Gestures
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 3
Abstract
Traditional methods for controlling multi-rotors typically involve joysticks, radio controllers, and mobile applications. However, these methods pose significant challenges, particularly for novice users like farmers, due to the extensive training and understanding required to effectively operate a copter. This paper introduces a highly adaptable architecture designed to offer an end-to-end solution for controlling a copter using hand gestures. The proposed system leverages a depth sensor and Convolutional Neural Network (CNN) to recognize hand gestures, utilizing a custom dataset collected from both indoor and outdoor environments. Through a series of simulations with novice users, the system has demonstrated successful operation in real-world scenarios. Currently, the architecture can accurately recognize six distinct gestures with an average accuracy of 90.5% across three different test environments with varying lighting conditions. Key features of this proposed solution include its adaptability, reliable performance, especially in low-light conditions, and its user-friendly design, making it particularly well-suited for farmers and other inexperienced users.
Authors and Affiliations
Nabeel Hussain, Hassan Yousuf, Syed Atif Mehdi, Kanwal Atif
Keywords
Related Articles
- EP ID EP760435
- DOI -
- Views 18
- Downloads 0
Clinical Prediction of Female Infertility Through Advanced Machine Learning Techniques
Infertility in females implies failure by such women to conceive even after having at least one year of intercourse without using any contraceptives. Infertility can be caused by a variety of factors, including ovulati...
A Comparative Analysis of BER Performance for NOMA in the Presence of Rayleigh Fading and Impulse Noise
Importance of Study: This research investigates the integration of wired and wireless communication in Smart Grid (SG) systems, addressing the challenges posed by impulse noise and the increasing demand for bandwidth....
Deep Learning Based Identification and Categorization of Various Phases of Diabetic Retinopathy
Diabetic Retinopathy is a growing disease that affects the human retina of diabetic patients, leading to loss of vision if left untreated. Early diagnosis and accurate classification of various stages of DR are crucial...
Leveraging CIELAB Segmentation and CNN for Wheat Fungi Disease Classification
Wheat is the third most harvested and consumed grain globally, but a significant portion of its production is wasted due to diseases. Fungal infections caused by pathogenic fungi are particularly harmful, greatly reduc...
Enhancing Security in Mobile Cloud Computing: An Analysis of Authentication Protocols and Innovation
Introduction/Importance of Study: Cloud computing is a model facilitating ubiquitous, convenient, and on-demand network access to a shared pool of computing resources, offering flexibility, reliability, and scalability...