A Review paper on object detector based on changeable size light weight convolution and context argumentation module or images captured by  UAVs

Author(s)	Mr. Priya Shriram Dhongade, Mr. Prof. Sunil Kuntawar
Country	India
Abstract	In recent years, object detection has become a crucial component in various applications such as surveillance, autonomous vehicles, medical imaging, and smart cities. This project focuses on object detection using state-of-the-art algorithms YOLOv8 and YOLOv9. Leveraging the power of deep learning, YOLO (You Only Look Once) models are renowned for their speed and accuracy in identifying multiple objects within images and video streams. The proposed system allows users to upload images or videos and utilizes the webcam for live object detection. The frontend is built using Streamlit, ensuring an intuitive and interactive user interface, while the backend is implemented in Python and executed on Google Colab to leverage GPU acceleration. The system processes the input through pre-trained YOLO models, identifies objects, and displays them with bounding boxes and confidence scores. This project demonstrates how advanced object detection algorithms can be integrated into user-friendly applications, promoting accessibility and real-world deployment. Additionally, it serves as a foundation for further enhancements such as object tracking, anomaly detection, and integration with IoT systems for smarter decision-making in dynamic environments.
Keywords	YOLOv8, YOLOv9, Object Detection, Streamlit, Analysis
Field	Engineering
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-06

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