International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022
p-ISSN: 2395-0072
www.irjet.net
Safety Helmet Detection in Engineering and Management Jyothika R1, Shweta Salapur2, Mrs. Swathi Sridharan 3 Font Size 12 1,2 Student
Dept. of Information Science and Engineering, BNM Institute of Technology, Karnataka, India Professor, Dept. of Information Science and Engineering, BNM Institute of Technology, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract— Due to a lack of knowledge about safety 2. RELATED WORKS 3Assistant
helmets, accidents and injuries on construction sites are now increasingly common. Worker supervision by hand is challenging and ineffective. This study is visually checking the construction site to see if anyone is wearing a safety helmet, and then notifying the worker and manager with a sound BUZZER and an SMS if they are. In order to recognise a safety helmet in real time at a building site, we built a deep learning-based technique. Helmet detection was done, and the experimental results indicate that less than 90% of people wore helmets. Compared to previously used methods, our model has an accuracy rate of 92%. The YOLO-V3 algorithm, which is based on convolutional neural networks, is used in the method that is being discussed.
To prevent injuries to workers at the construction site, it is crucial to keep an eye on the area and be promptly alerted if someone is not wearing a safety helmet. According to physiologic research, people are only capable of monitoring two signals at once with an accuracy rate of less than 70% [1]. A multidisciplinary field related to computer vision, pattern recognition, signal processing, communication, embedded computing, and image sensor is known as intelligent multi camera video surveillance. The scales and complexities of camera networks are growing as surveillance technologies advance quickly, and the monitored environments are getting more complex and denser [2]. A video surveillance application that automatically analyses a motorbike rider's helmet use shows promise because helmets are crucial for preventing brain injuries in traffic accidents. In order to segment the objects, a Gaussian-N mixing model is used (GMM). In order to recognize motorcycles in the foreground objects that have been labelled, the suggested system then adapts a faster regionbased convolutional neural network (faster R- CNN) [3]. Recently, it was recognized that employing a Convolutional Neural Network (CNN) or Deep Learning, digital image pattern recognition and feature extraction have been successful over the years. The effectiveness of utilizing a convolutional neural network for feature extraction and pattern detection in digital images. Wearing a helmet before entering the workplace is a requirement for the factory since the environment of the steel industry workshop is complicated and there may be unanticipated dangers. Helmet testing is a crucial component of the intelligent monitoring system for steel plant staff, since it allows for the monitoring of this condition. Accuracy of quicker RCNN algorithms decreases in dim light and complex backgrounds [5]. employing object segmentation and background subtraction in the surveillance footage. Using visual cues and binary classification, it then establishes whether the bike rider is wearing a helmet or not. Results from the experiment show that the accuracy for detecting bike riders and violators is 98.88 percent and 93.80 percent, respectively. A frame is processed on average in 11.58 milliseconds, which is suitable for real-time use [6]. The task of automated motion detection in traffic monitoring is difficult. This study develops a technique to get meaningful data from security cameras for tracking moving objects in digital films. The outcomes demonstrate that the suggested method recognises and tracks moving objects in urban
Key Words: Accidents, BUZZER, SMS, YOLO-V3, Deep Learning
1.INTRODUCTION Currently, complex structures and industries are expanding quickly over the globe, requiring a large workforce on construction sites. Unexpected accidents and injuries are more likely to occur on a construction site because of the complicated environment. In order to prevent these occurrences, safety helmets are required in this region. A significant amount of unstructured image data is available on-site thanks to the video monitoring equipment. According to the State Administration of Work Safety's accident statistics from 2015 to 2019, of the 80 construction accidents that were reported, 57 occurred as a result of improper use of safety helmets by the workers, making up 69 percent of the total. number of mishaps on construction sites, traditional safety helmet wear checking is quite challenging and requires a lot of manual labor. Additionally, using a visual monitor forces inspectors to spend a lot of time staring at the screen, which can be ineffective. On construction sites, researchers create deep learning-based ways to identify safety helmet use, which can help prevent accidents, injuries from false alarms, and reduced accuracy rates. This study used the real-time object detection method YOLO-V3 (You Only Look Once). utilises deep convolution neural network characteristics. Yolo-V3 has the advantage of being a lot faster and more accurate than other networks. The main goal of this study is to use live streaming to monitor the building site, identify any workers who are not wearing safety helmets, and notify them via SMS and buzzer.
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