International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 09 | Sep 2022
p-ISSN: 2395-0072
www.irjet.net
YOLOv4: A Face Mask Detection System Akanksha Soni1, Avinash Rai2 1Ph.D.
Scholar, Dept. of Electronics and Communication Engineering, UIT-RGPV, Bhopal,462033, India prof, Dept. of Electronics and Communication Engineering, UIT-RGPV, Bhopal,462033 India
2Asst
---------------------------------------------------------------------***--------------------------------------------------------------------Although there are other methods of object identification, in this work we will focus on YOLOv4. The advantage of YOLO is rapidly spreading viral infection that has affected millions all that it is faster than other networks while keeping accuracy. over the world. The greatest risk of transmission exists. In When tested, the complete image is examined, enabling the public locations one of the most efficient methods to be careful model to draw conclusions about the image's broader is to wear a mask. However, some irresponsible people refuse context. to wear face mask with so many excuses. Moreover, developing the face mask detector is very crucial in this case. In this work, What does the COCO record mean in YOLO? openCV is utilized to locate people who are wearing masks. Using real-time video processing, we will develop a deep Common Objects in Context (COCO) object detection, learning model that can be used to evaluate the ratio of people instance segmentation, image captioning, and human wearing masks to those who aren't in crowded places. We hotspot localization are some of the areas where COCO is evaluate the video stream using a real-time video camera and expected to support future studies. COCO is a comprehensive issue a notification when the zone contains persons who are data set for object detection, segmentation, and labelling. not wearing masks. We used YOLOv4 to determine whether the mask is worn correctly on the face. Darknet framework is 1.1 Motivation employ for YOLO training, which defines the network's architecture and aids CPU and GPU processing. We utilized It is difficult for the individual to constantly check on the Tkinter from the Python GUI for the user interface. video at all times. As a result, we developed software that alerts authorities if the number of persons who are not Key Words: Deep Learning, Face Mask Detection, Object wearing masks exceeds the limit we set. Also, give a user detection, Open CV, Darknet, YOLOv4 interface via which users may manually evaluate the picture and video. Since we are using Yolov v4 and OpenCV for 1. INTRODUCTION processing, the accuracy is greater than in earlier models.
Abstract – Corona virus disease of 2019 or COVID-19 is a
Even though the majority of people in India have been vaccinated, masks are necessary in populated areas because the majority of people do not use masks and do not practice social distance. In our work, we utilize Yolov4 to recognize faces with and without masks. It employs cspdarknet53 as a backbone for feature extraction, and PANet is employed for feature aggregation, which serves as the algorithm's neck. This project is delivered as software that is extremely user friendly. We utilised the Python GUI library, and the user interface was provided by Tkinter. The interface allows users to give multiple forms of input for processing. We used nvidia for CPU and GPU computation. This gives improved performance by providing GPU utilization, GPU memory access and usage, Power usage and temperatures, Time to solution. They are a major element of today's artificial intelligence infrastructure, and new GPUs have been designed and tuned particularly for deep learning.
1.2 Contributions In this paper, we offer software that will shorten the time authorities spend on-screen examining the covid transmission area. Because of the employment of the YOLOv4 object detecting algorithm, it outperforms the prior models. The contributions are summarized as follows:
You Only Look Once is a method for quickly recognizing objects (YOLO). It is an object identification system that is capable of quickly locating objects in images, real-time coverage, and video streams. Object recognition is one of the most challenging problems in image processing.
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Impact Factor value: 7.529
Designed the deep learning based object recognition system to detect whether a mask is worn or not. A survey on the key difficulties in face mask detection, which might be useful for developing new face mask detectors in the future. Using the Tkinter module of the Python library to provide a user interface. Utilized CSPDarknet53 as the backbone and PANet to aggregate features.
2. RELATED WORK Chaitali & Wanjale [1] ‘Survey On Image Classification Methods. In Image Processing’ This study provides an overview of different supervised classification algorithms
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