Authors :
Dr.P.Bhaskar Naidu; Pulakanam Anusha; Gothula Naveena; Thota Anusha; Chimakurthi Balaji
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/4w9rcyh6
Scribd :
https://tinyurl.com/3prk7ujs
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR715
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Object detection system using Convolutional
Neural Network(CNN) that can accurately identify and
classify objects in videos. The purpose of object detection
using CNN to enhance technology such as security
cameras, smart devices by enabling them to identify and
understand objects in videos. Object detection using
CNN is a fascinating filed in computer vision. Detection
can be difficult since there are all kinds of variations in
orientation, lighting, background that can result in
completely different videos of the very same object. Now
with the advance of deep learning and neural network,
we can finally tackle such problems without coming up
with various heuristics real-time. The project “Object
detection using CNN for video streaming” detects objects
efficiently based on CNN algorithm and apply the
algorithm on image or video data. In this project, we
develop a technique to identify an object considering the
deep learning pre-trained model MobileNet for Single
Shot Multi-Box Detector (SSD). This algorithm is used
for real-time detection and for webcam, which detects
the objects in a video stream. Therefore, we use an object
detection module that can detect what is in the video
stream. In order to implement the module, we combine
the MobileNet and the SSD framework for a fast and
efficient deep learning-based method of object detection.
The main purpose of our research is to elaborate the
accuracy of an object detection method SSD and the
importance of pre-trained deep learning model
MobileNet. The experimental results show that the
Average Precision (AP) of the algorithm to detect
different classes as car, person and chair is 99.76%,
97.76% and 71.07%, respectively. The main objective of
our project is to make clear the object detecting
accuracy. The existing methods are Region Based
Convolutional Neural Network(R-CNN) and You Only
Look Once(YOLO).R-CNN could not pushed real time
speed though its system is updated and new versions of it
are deployed and YOLO network is popular but YOLO
is to struggle to detect objects grouped close together,
especially smaller ones. To avoid the drawbacks of these
methods we proposed this model which included single
shot multi-box detector (SSD), this algorithm is used for
real time detection and Mobile-Net architecture.
Keywords :
Computer Vision, Mobilenet, SSD(Single Shot Multi-Box Detector),Object Detection, Accuracy, Efficiency.
Object detection system using Convolutional
Neural Network(CNN) that can accurately identify and
classify objects in videos. The purpose of object detection
using CNN to enhance technology such as security
cameras, smart devices by enabling them to identify and
understand objects in videos. Object detection using
CNN is a fascinating filed in computer vision. Detection
can be difficult since there are all kinds of variations in
orientation, lighting, background that can result in
completely different videos of the very same object. Now
with the advance of deep learning and neural network,
we can finally tackle such problems without coming up
with various heuristics real-time. The project “Object
detection using CNN for video streaming” detects objects
efficiently based on CNN algorithm and apply the
algorithm on image or video data. In this project, we
develop a technique to identify an object considering the
deep learning pre-trained model MobileNet for Single
Shot Multi-Box Detector (SSD). This algorithm is used
for real-time detection and for webcam, which detects
the objects in a video stream. Therefore, we use an object
detection module that can detect what is in the video
stream. In order to implement the module, we combine
the MobileNet and the SSD framework for a fast and
efficient deep learning-based method of object detection.
The main purpose of our research is to elaborate the
accuracy of an object detection method SSD and the
importance of pre-trained deep learning model
MobileNet. The experimental results show that the
Average Precision (AP) of the algorithm to detect
different classes as car, person and chair is 99.76%,
97.76% and 71.07%, respectively. The main objective of
our project is to make clear the object detecting
accuracy. The existing methods are Region Based
Convolutional Neural Network(R-CNN) and You Only
Look Once(YOLO).R-CNN could not pushed real time
speed though its system is updated and new versions of it
are deployed and YOLO network is popular but YOLO
is to struggle to detect objects grouped close together,
especially smaller ones. To avoid the drawbacks of these
methods we proposed this model which included single
shot multi-box detector (SSD), this algorithm is used for
real time detection and Mobile-Net architecture.
Keywords :
Computer Vision, Mobilenet, SSD(Single Shot Multi-Box Detector),Object Detection, Accuracy, Efficiency.
