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
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