Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM34853
Volume:
8 |
5 |
May 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
This research paper presents Detectify, an innovative web application that utilizes machine learning techniques for real-time object detection directly from live video feeds captured by usersâ webcams. Built upon TensorFlow.js and Next.js frameworks, Detectify empowers users to effortlessly identify and classify objects appearing in their surroundings. Leveraging the power of convolutional neural networks (CNNs), Detectify delivers accurate and efficient object detection capabilities without the need for server-side processing.
KEYWORDS
Publication Date
5/28/2024
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
0
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...