Object Detection Using Yolo Model

Project Code :TCMAPY513

Objective

The main goal of this project is to detect multiple objects in a single frame using Yolo (You Only look once) model.

Abstract

Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this project, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely YOLO.

Keywords: deep learning, object detection, YOLO, objects.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Software and Hardware requirements

Accessories- Webcam.

Technology - Python 3.6+

 RAM- 8 GB

Technology -   Python 3.6+

IDE - PyCharm

Packages - Tensor Flow, Open CV, Flask

Learning Outcomes

LEARNING OUTCOMES:

·         About Data Science.

·         About Python programming.

·         About Yolo.

·         About Deep learning frameworks.

·         About data transmission

·         About Deep learning and Machine learning.

·         Different toolboxes in Deep Learning

·         Working of open CV.

·         About transfer learning.

·         Project development skills:

o   Problem analyzing skills.

o   Problem solving skills.

o   Creativity and imaginary skills.

o   Programming skills.

o   Deployment.

o   Testing skills.

o   Debugging skills.

o   Project presentation skills.

o   Thesis writing skills.

Demo Video

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