Authors :
D. Tharun Reddy; N. Durga Prasad; M. Sheshu Kumar; Dr. A. N. Satyanarayana
Volume/Issue :
Volume 9 - 2024, Issue 5 - May
Google Scholar :
https://tinyurl.com/mr38nd2x
Scribd :
https://tinyurl.com/352r9sec
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAY156
Abstract :
An important use of computer technology in
recent years has been the automatic helmet recognition
of motorcy clists in real-time surveillance film. Deep
learning methods are becoming more and more popular
as a result, especially for object detection and
classification. Nevertheless, a number ofissues, including
limited resolution, inadequate lighting, adverse weather,
and occlusion, restrict the accuracy of current models
in identifying motorcycle helmets. A unique method that
makes use of the Faster R-CNN model has been put
out to addressthese issues. Using the input image as the
starting point, this method first trains the Region
Proposal Network (RPN), and then it uses the RPN
weights to train the Faster RCNN model. The goal of this
method is to increase helmet detection accuracy in live
surveillance footage. This method’s experimental results
have demonstrated encouraging results, with a 95%
accuracy rate in identifying motorcycle helmets in live
surveillance footage.This illustrates the promise of deep
learning approaches in the field of automatic helmet
detection for motorcyclists in real-time surveillance film,
as well as the efficacy of the suggested strategy in
overcoming the issues encountered by current models.
Keywords :
Helmet, Faster-RCNN, CNN, Deep Learning, Region Proposal Network, Surveillance Videos.
An important use of computer technology in
recent years has been the automatic helmet recognition
of motorcy clists in real-time surveillance film. Deep
learning methods are becoming more and more popular
as a result, especially for object detection and
classification. Nevertheless, a number ofissues, including
limited resolution, inadequate lighting, adverse weather,
and occlusion, restrict the accuracy of current models
in identifying motorcycle helmets. A unique method that
makes use of the Faster R-CNN model has been put
out to addressthese issues. Using the input image as the
starting point, this method first trains the Region
Proposal Network (RPN), and then it uses the RPN
weights to train the Faster RCNN model. The goal of this
method is to increase helmet detection accuracy in live
surveillance footage. This method’s experimental results
have demonstrated encouraging results, with a 95%
accuracy rate in identifying motorcycle helmets in live
surveillance footage.This illustrates the promise of deep
learning approaches in the field of automatic helmet
detection for motorcyclists in real-time surveillance film,
as well as the efficacy of the suggested strategy in
overcoming the issues encountered by current models.
Keywords :
Helmet, Faster-RCNN, CNN, Deep Learning, Region Proposal Network, Surveillance Videos.
