Vaishnavi Bhapkar,
Sankita Salvi,
Jankar Tejaswini,
Bandal Rutuja,
K.S. Khamkar,
- Student, RDTC’s Shri Chhatrapati Shivajiraje College of Engineering, Dhangwadi, Bhor, Pune Maharashtra India
- Student RDTC’s, Shri Chhatrapati Shivajiraje College of Engineering, Dhangwadi, Bhor, Pune Maharashtra India
- Student RDTC’s, Shri Chhatrapati Shivajiraje College of Engineering, Dhangwadi, Bhor, Pune Maharashtra India
- Student RDTC’s, Shri Chhatrapati Shivajiraje College of Engineering, Dhangwadi, Bhor, Pune Maharashtra India
- Professor, Dhangwadi, Bhor, Pune Maharashtra India
Abstract
Object detection systems are essential tools for identifying and locating objects within images or videos. When integrated into spectacles or wearable devices, these systems provide users with real-time information about objects present in their surroundings. This functionality serves diverse purposes, such as assisting visually impaired individuals in navigating their environment or offering augmented reality data to workers during tasks. Region-based Convolutional Neural Networks (RCNN) represent a prominent machine learning model used extensively for object detection. The RCNN model operates in two main stages: initially employing a convolutional neural network (CNN) to extract distinctive features from the input image. Subsequently, it applies a region proposal algorithm to pinpoint potential object locations within the image. These proposed regions are then processed through a second CNN, which classifies them into either objects or backgrounds. Effectively, the RCNN model has demonstrated its capability to detect a broad spectrum of objects across various types of images and videos. Its proficiency lies in leveraging deep learning techniques to accurately identify and categorize objects, making it a versatile tool for applications ranging from enhancing accessibility for the visually impaired to improving productivity through augmented reality in industrial settings.
Keywords: Moving Object Detection Systems, Machine Learning, Region-based CNN, Spectacles, Algorithm
[This article belongs to International Journal of Optical Innovations & Research(ijoir)]
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| Volume | 02 |
| Issue | 01 |
| Received | May 31, 2024 |
| Accepted | June 27, 2024 |
| Published | August 7, 2024 |