Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 22 Issue 1

only has one class, the maximum batch was set to 8000, the steps were set to 6400, 7200, and 18 filters in the three convolutional layers before the YOLO layers, and the number of classes in the YOLO layers was set to 1 and also set network size width 608 height 608 in Yolov3.cfg file. Map value test on 500 people images and got 85% map value for our yolov3 trained model [5]. Table I [5] compares our Yolov3 approach to a variety of different object detection methods in terms of mAP.Fig.6 shows the results of the density risk estimation. if the real-time visitors count inside the area is higher than the maximum count that area is a high-risk area and an email is sent to the nearest police station. Table 1: Map comparisons Model Dataset MAP (%) Our YOLOv3 Google Open Images 85.0% Alexey AB YOLOv3 Pascal Voc 87.0% R-CNN Pascal Voc 53.2% Fig. 6: Final output of Density Risk detection b) Social Distance Risk Analysis Social Distance Algorithm is a method for controlling epidemic diseases. People use social distance to protect in any epidemic circumstance. This system calculates distances between people and draws various border colors for three risk degrees. Used SSD- Mobile Net model for object detection. The accuracy of developed model SSD-Mobilenet was 92.8 percentage. Authors tested the proposed model using a video stream and images. In each frames were also labelled as unsafe and safe accordingly. To bad visibility areas proposed using CLAHE prepossessing technique. It is vital to have individuals moving continuously while utilizing the webcam, or else the detection will be wrong. Fig. 7: Final output of social distance detection c) Human Behaviour Risk Analysis Another main part in this system is Human behaviour risk analysing. To estimate the human actions, we mainly used two pre-trained Open Pose models to estimate the human actions. The main part of this function is recognizing human actions using estimated human actions. To do that we used over 4000 image data to train a model. After the training, we were able to get a 98.3 percentage training accuracy and 95.9 percentage of test accuracy. Fig. 8: Final output of Human behaviour Recognition According to above Fig.8, we can get a clear and good idea about the result of this Human Behaviour recognizing function. d) Face Mask Risk Analysis The wearing of masks correctly and consistently is a vital step that everyone can take to avoid contracting and spreading COVID-19. Masks are most effective when everyone wears them, but not all masks offer the same level of protection. Consider how well a mask fits, how well it filters the air, and how many layers it has when purchasing one. For the purpose of this research, the data sets which have two classes (MASK and No Mask) and four classes (Surgical, KN95, homemade and bare) were obtained. For the facemask risk detection using facemask type, a YOLO(v3) model was pre-trained with Pytorch Geometric using custom dataset imported from YOLO v3 achieving a train mean average precision of 99.24% and test mean average precision of 73% with 6000 images in training and 2000 test images under 4 classes in validating the model. Figure 10 is shown it efficiently. With these findings, our model has also demonstrated success in detecting face masks in images beyond the our training and validation range. We initialized our learning rate at (LR=0.001), the number of training epochs at (EPOCHES = 45000), and the batch size at (BS = 64) for the testing phrase. Figure 9 depicts various scenarios for detecting different sorts of face masks in real time from a live-stream. Additionally, Table 1 discusses the importance of performance indicators in gaining a better knowledge of how suggested models behave throughout the testing process. The result analysis demonstrates that our suggested approach for face mask detection based on several types of masks performs really well despite the fact that testing data is limited. Global Journal of Computer Science and Technology Volume XXII Issue I Version I 13 ( )D © 2022 Global Journals Human Tracking and Profiling for Risk Management Year 2022