Detection of Worker’s Safety Helmet and Mask and Identification of Worker Using Deeplearning

This paper proposes a method for detecting a helmet for thesafety of workers from risk factors and a mask worn indoors and verifying aworker’s identity while wearing a helmet and mask for security. The proposedmethod consists of a part for detecting the worker’s helmet and mask and apart for verifying the worker’s identity. An algorithm for helmet and maskdetection is generated by transfer learning of Yolov5’s s-model and m-model.Both models are trained by changing the learning rate, batch size, and epoch.The model with the best performance is selected as the model for detectingmasks and helmets. At a learning rate of 0.001, a batch size of 32, and anepoch of 200, the s-model showed the best performance with a mAP of0.954, and this was selected as an optimal model. The worker’s identificationalgorithm consists of a facial feature extraction part and a classifier partfor the worker’s identification. The algorithm for facial feature extraction isgenerated by transfer learning of Facenet, and SVMis used as the classifier foridentification. The proposed method makes trained models using two datasets,a masked face dataset with only a masked face, and a mixed face datasetwith both a masked face and an unmasked face. And the model with the bestperformance among the trained models was selected as the optimal model foridentification when using a mask. As a result of the experiment, the model bytransfer learning of Facenet and SVM using a mixed face dataset showed thebest performance. When the optimal model was tested with a mixed dataset,it showed an accuracy of 95.4%. Also, the proposed model was evaluated asdata from 500 images of taking 10 people with a mobile phone. The resultsshowed that the helmet and mask were detected well and identification wasalso good.

Peritonitis with small bowel perforation caused by a fish bone in a healthy patient

Perforation of the gastrointestinal tract by ingested foreign bodies is extremely rare in otherwise healthy patients, accounting for < 1% of cases. Accidentally ingested foreign bodies could cause small bowel perforation through a hernia sac, Meckel's diverticulum, or the appendix, all of which are uncommon. Despite their sharp ends and elongated shape, bowel perforation caused by ingested fish bones is rarely reported, particularly in patients without intestinal disease. We report a case of 57-year-old female who visited the emergency room with periumbilical pain and no history of underlying intestinal disease or intra-abdominal surgery. Abdominal computed tomography and exploratory laparotomy revealed a small bowel micro-perforation with a 2.7-cm fish bone penetrating the jejunal wall.

A Method for Detecting Non-Mask Wearers Based on Regression Analysis

A novel practical and universal method of mask-wearing detection has been proposed to prevent viral respiratory infections.The proposed method quickly and accurately detects mask and facial regions using welltrained You Only Look Once(YOLO)detector,then applies image coordinates of the detected bounding box(bbox).First,the data that is used to train our model is collected under various circumstances such as light disturbances,distances,time variations,and different climate conditions.It also contains various mask types to detect in general and universal application of the model.To detect mask-wearing status,it is important to detect facial and mask region accurately and we created our own dataset by taking picture of images.Furthermore,the Convolutional Neural Network(CNN)model is trained with both our own dataset and open dataset to detect under heavy foot-traffic(Indoors).To make the model robust and reliable in various environment and situations,we collected various sample data in different distances.And through the experiment,we found out that there is a particular gradient according to the mask-wearing status.The proposed method searches the point where the distance between the gradient for each state and the coordinate information of the detected object is the minimum.Then it carry out the classification of mask-wearing status of detected object.Lastly,we defined and classified three different mask-wearing states according to the mask’s position(With mask,Wear a mask around chin and Without mask).The gradient according to the mask-wearing status,is analyzed through linear regression.The regression interpretation is based on coordinate information of mask-wearing status and the sample data collected in simulated environment that considering distances between objects and the camera in the World Coordinate System.Through the experiments,we found out that linear regression analysis is more suitable than logistic regression analysis for classification of people wearing masks in general-purpose environments.And the proposed method,through linear regression analysis,classifies in a very concise way than the others.