INTELLIGENT MOTION-ACTIVATED LED SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

The LED Light Control System using Motion Detection focuses on the development of an intelligent, energy-efficient lighting solution suitable for home automation, security, and public spaces. The project aims to design a system where LED lights are automatically activated based on human motion, detected through a Convolutional Neural Network (CNN) model. By leveraging deep learning techniques, the system offers high accuracy in distinguishing between human presence and irrelevant movements, thus enhancing operational efficiency.
The primary objective of this project is to minimize unnecessary energy consumption by ensuring that LED lights are only activated when needed. A real-time camera feed is analyzed by the CNN model, which identifies motion patterns and triggers the LED control circuit accordingly. The system is designed to perform reliably under various lighting conditions and environments, ensuring robust detection performance both indoors and outdoors.
The report provides a detailed explanation of the hardware setup, CNN model architecture, training process, and integration of the motion detection module with the LED control system. It also covers component selection, software development, and testing methodologies. Special attention is given to energy management, system latency, and false-positive minimization to ensure an optimal balance between responsiveness and power saving.
In conclusion, the LED Light Control System using Motion Detection demonstrates the practical application of deep learning and modern embedded electronics to create a smart, eco-friendly lighting solution. The project highlights the potential for future enhancements, such as multi-object tracking, adaptive lighting based on motion intensity, and cloud-based control for smarter energy management and greater user convenience.