International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
www.irjet.net
DYNAMIC ENERGY MANAGEMENT USING REAL TIME OBJECT DETECTION Archana A, Abarna S, G Bhuvanesh, Sri Ram R, Dr. A Kannammal Student, Dept. of Electronics and Communication Engineering PSG College of Technology Coimbatore, India Student, Dept. of Electronics and Communication Engineering PSG College of Technology Coimbatore, India Student, Dept. of Electronics and Communication Engineering PSG College of Technology Coimbatore, India Student, Dept. of Electronics and Communication Engineering PSG College of Technology Coimbatore, India Assistant Professor, Dept. of Electronics and Communication Engineering PSG College of Technology Coimbatore, India ---------------------------------------------------------------------***--------------------------------------------------------------------movements over time. However, this process can be challenging due to factors such as changes in lighting, occlusion, and object deformation. To overcome these challenges, our project developed various techniques, including deep learning-based methods, to improve the accuracy and speed of object detection in videos. Additionally, advancements in computer hardware and GPUaccelerated computing have enabled real-time video object detection and tracking, making it an essential tool for a wide range of applications.
Abstract - The system mainly concentrates on the human
detection using YOLO CV2 algorithm and sectoring the area into four parts. The hardware requirements used is Raspberry Pi4 and webcam. The software requirements include YOLO CV2 and VNC viewer to display the area detected by the camera. The sectored area in the software is merged with the area that has been chosen in real time in which camera identifies a human detection in a particular sector and the specified sector is alone turned on instead of using the electrical energy in the whole unused space. The project's primary goal is to minimize energy usage dynamically. Additionally, the project incorporated intrusion detection, automatic on & off of the system at specified timing, and to alert when unwanted movement is detected.
1.2 IMAGE SEGMENTATION Image sectoring, also known as image segmentation, is the process of dividing an image into multiple segments or regions based on certain criteria, such as colour, texture, or shape. This technique is widely used in computer vision and image processing applications, including object detection, image recognition, and scene analysis. In the context of reducing electricity consumption, image sectoring can be used to identify the presence of humans in a particular area or sector, such as a classroom or office space. Image sectoring can be performed using various algorithms and techniques, such as thresholding, clustering, and edge detection. These algorithms can be tailored to specific applications and environments, allowing for accurate and reliable detection of human presence in different spaces. Additionally, advancements in computer vision and deep learning technologies have enabled more advanced image sectoring algorithms that can analyse images in real-time and detect more complex patterns and objects.
Key Words: Machine Learning, Energy Management, Object Detection, Electrical Control, Intruder Detection
1.INTRODUCTION The project's objective is to minimize energy consumption by managing electrical devices through human detection in a designated area. The observation area is divided into four sectors, and each sector's electrical devices are managed separately based on human presence in that sector. To detect and identify human presence, Machine Learning algorithms are employed. The Raspberry Pi serves as the central unit for the project, and a working prototype has been developed in a real-time environment. Overall, the project's innovative use of YOLO CV2 algorithm provides an efficient and costeffective solution for energy management and security.
Object detection and sectoring images are two critical tasks that rely heavily on object detection technology. Object detection enables computers to recognize and locate objects in images or videos, while sectoring images involves dividing an image into smaller segments or regions. By combining these techniques, we effectively identify and analyse the contents of an image or video.
1.1 OBJECT DETECTION Object detection plays a crucial role in video processing, allowing computers to automatically identify and track objects in real-time. Video object detection is used in a variety of applications, including security and surveillance, traffic monitoring, and autonomous vehicles. To perform object detection in video processing, algorithms typically analyze frames of video by identifying objects and tracking their
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