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YOLO: Real-Time Object Detection with End-to-End Gradio Integration

Year: 2025

Type: article

Abstract: This project report details the development and deployment of a real-time object detection application that leverages the YOLOv10 model and Gradio for its user interface. The application is capable of processing both images and videos, with configurable parameters such as model variant, image resolution, and confidence threshold. The report outlines the motivation behind the project, the design decisions made, the implementation process, experimental results, and potential avenues for future development. The goal is to demonstrate how deep learning-based object detection can be seamlessly integrated into an interactive web application, thereby providing a practical tool for applications in fields such as security, automation, and surveillance.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15124983

Authors: K. Venkata Ramaiah, M. Vivek, Sk. Rafi, T. Kulashekar, P. Sai Pavan

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6949384928

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