Enhancing Object Detection: A Comprehensive Study and Implementation of Faster R-CNN with Streamlit Dashboard
Dhavan Arjampudi, Dr. A. Mary Sowjanya
Object detection, Computer vision, Faster R-CNN, Streamlit, COCO dataset, Image preprocessing
Object detection, a fundamental task in computer vision, has witnessed significant advancements in recent years. This research presents a comprehensive study that explores the integration of Faster R-CNN (Region Convolutional Neural Network), a state-of-the-art object detection model trained on the COCO (Common Objects in Context dataset), with Streamlit, an interactive web application framework. The goal is to create an intuitive and user-friendly dashboard that facilitates real-time object detection and visualization. The proposed approach combines the power of Faster R-CNN's accurate object localization with Streamlit's simplicity in creating interactive interfaces. To evaluate the effectiveness of the approach, a series of experiments were conducted using various images containing diverse objects. The results showcase the successful integration of Faster R-CNN with Streamlit. By combining the strengths of Faster R-CNN, the COCO dataset, and Streamlit, the research presents a novel approach that holds promise in various domains, including surveillance, retail, and automation.
Article Details
Unique Paper ID: 161469

Publication Volume & Issue: Volume 10, Issue 4

Page(s): 89 - 93
Article Preview & Download

Share This Article

Conference Alert


AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management


Last Date: 7th November 2023

Go To Issue

Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews