Abstract: Object detection is a crucial field in computer vision that allows machines to identify and track objects in real time. This project develops a live object detector using yolov8, integrated with tkinter to provide an interactive and user-friendly graphical interface. The system captures live video through a webcam, processes frames with yolov8, and delivers real-time object detection with voice feedback using pyttsx3.
The application features a graphical user interface (gui) that enables users to start and stop detection, toggle voice alerts, and view detected objects dynamically. The yolov8 model is chosen for its high efficiency and accuracy, while opencv and pillow (pil) are used for real-time image processing and display. The system also employs a queue-based text-to-speech mechanism to provide audio notifications when objects are detected. A movement detection feature ensures real-time tracking of detected objects, updating the interface accordingly.
This project has practical applications in security surveillance, inventory management, accessibility support, and smart home automation. The ability to detect and identify objects in real time makes it useful for various domains, including education, healthcare, and transportation. The voice feedback feature enhances accessibility, making it beneficial for visually impaired users.
Future enhancements may include custom training of yolo models, multi-object tracking, integration with iot devices, and edge computing for improved performance. The project demonstrates the potential of ai-driven object detection in real-world applications, showcasing its effectiveness in developing intelligent vision-based systems.
Keywords: Yolov8, Tkinter, OpenCV, PIL (Pillow), Pyttsx3, Threading, Queue, Real-time Object Detection, GUI, Webcam, Text-to-Speech, Machine Learning, Computer Vision, Deep Learning, Python.
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DOI:
10.17148/IJARCCE.2025.14253
