scholarly journals Applying the Haar-cascade Algorithm for Detecting Safety Equipment in Safety Management Systems for Multiple Working Environments

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1079
Author(s):  
Le Tran Huu Phuc ◽  
HyeJun Jeon ◽  
Nguyen Tam Nguyen Truong ◽  
Jung Jae Hak
Keyword(s):  
Safety Management ◽  
Learning Algorithm ◽  
Warning System ◽  
Warning Signal ◽  
Working Environment ◽  
Safety Equipment ◽  
Cascade Algorithm ◽  
Working Environments ◽  
Safety Warning ◽  
Haar Cascade

There are many ways to maintain the safety of workers on a working site, such as using a human supervisor, computer supervisor, and smoke–flame detecting system. In order to create a safety warning system for the working site, the machine-learning algorithm—Haar-cascade classifier—was used to build four different classes for safety equipment recognition. Then a proposed algorithm was applied to calculate a score to determine the dangerousness of the current working environment based on the safety equipment and working environment. With this data, the system decides whether it is necessary to give a warning signal. For checking the efficiency of this project, three different situations were installed with this system. Generally, with the promising outcome, this application can be used in maintaining, supervising, and controlling the safety of a worker.

scholarly journals Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed

2021 ◽  
Vol 11 (2) ◽  
pp. 897-910
Author(s):  
K. Pavani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Road Traffic ◽  
Learning Algorithm ◽  
Traffic Density ◽  
Cascade Algorithm ◽  
Real Time Traffic ◽  
Accuracy And Precision ◽  
Haar Cascade ◽  
Better Than

Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was used to create a classifier by combining individual classifiers. The significance value achieved for finding the accuracy and precision was 0.445 and 0.754 respectively. Results and Discussions: Detection of vehicles in high speed videos is performed by using Haar Cascade which has mean accuracy with 85.22% and mean precision with 90.63% and 60% of mean accuracy and 58.53% mean precision in KNN classifiers. Conclusion: The performance of the Haar Cascade appears to be better than KNN in terms of both Accuracy and Precision.

scholarly journals Student Smart Attendance Through Face Recognition using Machine Learning Algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 2348-2352
Keyword(s):  
Face Recognition ◽  
Learning Algorithm ◽  
Working Hours ◽  
Machine Learning Algorithm ◽  
Decision Tree Algorithm ◽  
Cascade Algorithm ◽  
Face Region ◽  
The Face ◽  
Haar Cascade ◽  
Classroom Time

In today’s competitive world, with very less classroom time and increasing working hours, lecturers may need tools that can help them to manage precious class hours efficiently. Instead of focusing on teaching, lecturers are stuck with completing some formal duties, like taking attendance, maintaining the attendance record of each student, etc. Manual attendance marking unnecessarily consumes classroom time, whereas smart attendance through face recognition techniques helps in saving the classroom time of the lecturer. Attendance marking through face recognition can be implied in the classroom by capturing the image of the students in the classroom via the camera installed. Later through the HAAR Cascade algorithm and MTCNN model, face region needs to be taken as interest and the face of each student is bounded through a bounding box, and finally, attendance can be marked into the database based on their presence by using Decision Tree Algorithm.

Serum of 8-OHdG as a potential biomarker of oxidative DNA damage in workers exposed to harmful working environments

2020 ◽  
pp. 783-783
Author(s):  
I. A. Umnyagina ◽  
L. A. Strakhova ◽  
T. V. Blinova
Keyword(s):  
Oxidative Stress ◽  
Dna Damage ◽  
Blood Serum ◽  
Oxidative Dna Damage ◽  
Working Environment ◽  
Working Environments ◽  
Potential Biomarker ◽  
High Level ◽  
Damage Marker

In the blood serum of 70% individuals exposed to harmful factors of the working environment, a high level of oxidative stress and the DNA damage marker 8-Hydroxy-2’-Deoxyguanosine (8-OHdG) were detected.

scholarly journals Does Employee Care Trigger Innovation Under a Healthy and Safe Working Environment? Evidence from the Pharmaceutical Industry in China

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 194
Author(s):  
Minghui Yang ◽  
Qian Lin ◽  
Petra Maresova
Keyword(s):  
Safety Management ◽  
Positive Impact ◽  
Health And Safety ◽  
Working Environment ◽  
Firm Innovation ◽  
Corporate Managers ◽  
Pharmaceutical Firms ◽  
Corporate Ability ◽  
Safe Working

Sustainability of the workforce becomes a crucial issue, of which responsible care for employees can increase job satisfaction and human capital that impact corporate ability to absorb and generate new knowledge. Firms are obligated to provide a healthy and safe working environment for their employees, but it may in turn hinder innovation due to rigid and structured institutional regulations. Drawing on data of 308 China’s pharmaceutical firms from 2010 to 2017, we investigated whether employee care can trigger innovation under corporate adoption of the occupational health and safety management system (OHSMS). Our results suggest that both employee care and OHSMS adoption have a positive impact on innovation. Moreover, the positive relationship between employee care and innovation was more pronounced in firms that had adopted the OHSMS certification. These findings are valuable to policymakers and corporate managers in emerging economies through corroborating the important role of workforce sustainability in facilitating firm innovation.

scholarly journals Development of an early warning system for loose automobile tires

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042094088
Author(s):  
Huibo Wu ◽  
Fei Song ◽  
Kainan Wu ◽  
Cheng Chen ◽  
Xiaohua Wang
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Traffic Accidents ◽  
Warning System ◽  
Working Environment ◽  
Vehicle Body ◽  
Economic Losses ◽  
Control Module ◽  
Alarm Signal ◽  
Monitoring Module

The looseness of tires or even falling off from cars will lead to serious traffic accidents. Once it occurs, it will bring casualties and huge economic losses to society, seriously affecting the traffic safety. To mitigate such possible safety concerns, an early loosening warning system is developed in this article. The system consists of the tire monitoring module and the working control module. The tire monitoring module is installed on the tire and is designed with no power supply. The control module is installed in the vehicle body. Signal transmission between the two modules is achieved through wireless radio frequency. In the driving, once the tire is loosened, the monitoring device will send out the alarm signal automatically and wirelessly. After the driver gets the alarm signal, he can immediately perform the emergency processing, parking, and inspection, which can avoid traffic accidents caused by it. This article introduces the detailed structure, working principle, and operation process of the system. This early warning system has simple structure, high reliability, and is easy to use. It can be used in the common working environment of automobiles. Meanwhile, it is also the foundation of intelligent connected vehicle.

Robot obstacle avoidance system using deep reinforcement learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaojun Zhu ◽  
Yinghao Liang ◽  
Hanxu Sun ◽  
Xueqian Wang ◽  
Bin Ren
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Environmental Parameters ◽  
Working Environment ◽  
Content Type ◽  
Practical Applications ◽  
Human Operators

Purpose Most manufacturing plants choose the easy way of completely separating human operators from robots to prevent accidents, but as a result, it dramatically affects the overall quality and speed that is expected from human–robot collaboration. It is not an easy task to ensure human safety when he/she has entered a robot’s workspace, and the unstructured nature of those working environments makes it even harder. The purpose of this paper is to propose a real-time robot collision avoidance method to alleviate this problem. Design/methodology/approach In this paper, a model is trained to learn the direct control commands from the raw depth images through self-supervised reinforcement learning algorithm. To reduce the effect of sample inefficiency and safety during initial training, a virtual reality platform is used to simulate a natural working environment and generate obstacle avoidance data for training. To ensure a smooth transfer to a real robot, the automatic domain randomization technique is used to generate randomly distributed environmental parameters through the obstacle avoidance simulation of virtual robots in the virtual environment, contributing to better performance in the natural environment. Findings The method has been tested in both simulations with a real UR3 robot for several practical applications. The results of this paper indicate that the proposed approach can effectively make the robot safety-aware and learn how to divert its trajectory to avoid accidents with humans within the workspace. Research limitations/implications The method has been tested in both simulations with a real UR3 robot in several practical applications. The results indicate that the proposed approach can effectively make the robot be aware of safety and learn how to change its trajectory to avoid accidents with persons within the workspace. Originality/value This paper provides a novel collision avoidance framework that allows robots to work alongside human operators in unstructured and complex environments. The method uses end-to-end policy training to directly extract the optimal path from the visual inputs for the scene.

scholarly journals Computer Vision Based Industrial Robotic Arm for Sorting Objects by Color and Height

2020 ◽  
Vol 01 (04) ◽  
pp. 116-122
Author(s):  
Abu Salman Shaikat ◽  
Suraiya Akter ◽  
Umme Salma
Keyword(s):  
Computer Vision ◽  
Production Process ◽  
Production Systems ◽  
Robotic Arm ◽  
Cascade Algorithm ◽  
Manual Sorting ◽  
System Requirement ◽  
Conveyor Belts ◽  
Haar Cascade ◽  
A New Technique

In industrial production systems, manufacturers often face difficulties in sorting different types of objects. Color and height-based sorting which is done manually by human is quite a tedious task and its needs countless time as well. For manual sorting, many workers are required, which can be quite expensive. Moreover, robots that can sort only color or height can’t be effective when there is a need of products with same color with different heights and vice versa. In this paper, a computer vision based robotic sorter is proposed, which is capable of detecting and sorting objects by their colors and heights at the same time. This work isn’t done before as height sorting of same shapes is a new technique, which is done with color sorting techniques by computer vision. It is equipped with a robotic arm having 6 degree of freedom (DOF), by which it picks up and then place objects according to its color and height, to a predetermined place as per the production system requirement. A camera with the computer vision software detects various colors and heights. Haar Cascade algorithm has been used to sort the products. This multi-DOF robotic sorter can be a remarkably useful tool for automating the production process completely, where multiple conveyor belts are used, which can reduce time complexity as well. In the proposed system, the efficiency of color and height sorting is around 99%, which proves the efficiency of our system. The overall improvement in the efficiency of the production process can be significantly enhanced by using this system.

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