A Study on the Detection of Protective Helmets for the Safety of Construction Workers

In recent years, with the rapid development of the construction industry, the number of accidents and deaths on construction sites increased, so the prevention of accidents is one of the important issues. Worker safety during construction is a major concern of the construction industry. Wearing helmets can reduce injuries among construction workers, but helmets are not always worn and used correctly for a variety of reasons. Therefore, computer vision-based automatic helmet detection systems are very important. Although many researchers have developed machine and deep learning-based motorcycle helmet detection systems, there is little research on helmet detection for construction workers. Therefore, in this research work, an automatic system for detecting the helmets of construction workers based on real-time computer vision is presented. In this study, machine learning method is used to detect helmets, and a model is trained using 1,500 images. The test results show that the average accuracy is above 95% in laboratory conditions

Transforming traffic surveillance: a YOLO-based approach to detecting helmetless riders through CCTV

CCTV systems, while ubiquitous for traffic surveillance in Indonesian roadways, remain underutilized in their potential. The integration of AI and Computer Vision technologies can transform CCTV into a valuable tool for law enforcement, specifically in monitoring and addressing helmet non-compliance among motorcycle riders. This study aims to develop an intelligent system for the accurate detection of helmetless motorcyclists using image analysis. The approach relies on deep learning, involving the creation of a dataset with 764 training images and 102 testing images. A deep convolutional neural network with 23 layers is configured, trained with a batch size of 10 over ten epochs, and employs the YOLO method to identify objects in images and subsequently detect helmetless riders. Accuracy assessment is carried out using the mean Average Precision (mAP) method, resulting in a notable 82.81% detection accuracy for riders without helmets and 75.78% for helmeted riders. The overall mAP score is 79.29%, emphasizing the system's potential to substantially improve road safety and law enforcement effort

Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning

The proper enforcement of motorcycle helmet regulations is crucial for ensuring the safety of motorbike passengers and riders, as roadway cyclists and passengers are not likely to abide by these regulations if no proper enforcement systems are instituted. This paper presents the development and evaluation of a real-time YOLOv5 Deep Learning (DL) model for detecting riders and passengers on motorbikes, identifying whether the detected person is wearing a helmet. We trained the model on 100 videos recorded at 10 fps, each for 20 seconds. Our study demonstrated the applicability of DL models to accurately detect helmet regulation violators even in challenging lighting and weather conditions. We employed several data augmentation techniques in the study to ensure the training data is diverse enough to help build a robust model. The proposed model was tested on 100 test videos and produced an mAP score of 0.5267, ranking 11th on the AI City Track 5 public leaderboard. The use of deep learning techniques for image classification tasks, such as identifying helmet-wearing riders, has enormous potential for improving road safety. The study shows the potential of deep learning models for application in smart cities and enforcing traffic regulations and can be deployed in real-time for city-wide monitoring

Violation of Traffic Rules and Detection of Sign Boards

Today's society has seen a sharp rise in the number of accidents caused by drivers failing to pay attention to traffic signals and regulations. Road accidents are increasing daily as the number of automobiles rises. By using synthesis data for training, which are produced from photos of road traffic signs, we are able to overcome the challenges of traffic sign identification and decrease violations of traffic laws by identifying triple-riding, no-helmet, and accidents, which vary for different nations and locations. This technique is used to create a database of synthetic images that may be used in conjunction with a convolution neural network (CNN) to identify traffic signs, triple riding, no helmet use, and accidents in a variety of view lighting situations. As a result, there will be fewer accidents, and the vehicle operator will be able to concentrate more on continuing to drive but instead of checking each individual road sign. Also, simplifies the process to recognize triple driving, accidents, but also incidents when a helmet was not used

Improving industrial security device detection with convolutional neural networks

Employee safety is paramount in the manufacturing industry to ensure their well-being and protection. Technological advancements, particularly convolutional neural networks (CNN), have significantly enhanced this safety aspect by facilitating object detection and recognition. This project aims to utilize CNN technology to detect personal protective equipment and implement a safety implement detection system. The CNN architecture with the YOLOv5x model was employed to train a dataset. Dataset videos were converted into frames, with resolution scale adjustments made during the data collection phase. Subsequently, the dataset was labeled, underwent data cleaning, and label and bounding box revisions. The results revealed significant metrics in safety equipment detection in industrial settings. Helmet precision reached 91%, with a recall of 74%. Goggles achieved 85% precision and an 87% recall. Mask absence recorded 92% precision and an 89% recall. The YOLOv5x model exhibited commendable performance, showcasing its robust ability to accurately locate and detect objects. In conclusion, the utilization of a CNN-based safety equipment detection system, such as YOLOv5x, has yielded substantial improvements in both speed and accuracy. These findings lay a solid foundation for future industrial security applications aimed at safeguarding workers, fostering responsible workplace behavior, and optimizing the utilization of information technology resources

Fine-Tuning YOLOv5 with Genetic Algorithm For Helmet Violation Detection

The present study addresses the issue of non-compliance with helmet laws and the potential danger to both motorcycle riders and passengers. Despite the well-established advantages of helmet usage, compliance remains a formidable challenge in many regions of the world, with various factors contributing to the issue. To mitigate this concern, real-time monitoring and enforcement of helmet laws have been advocated as a plausible solution. However, previous attempts at real-time helmet violation detection have been limited by their inability to operate in real-time. To remedy this issue, the current paper proposes a real-time helmet violation detection system utilizing a single-stage object detection model called YOLOv5. The model was trained on the 2023 NVIDIA AI City Challenge Track 5 dataset and employed genetic algorithms in selecting the optimal hyperparameters for training the model. Furthermore, data augmentation techniques such as flip, and rotation were implemented to improve model performance. The efficacy of the model was assessed using mean average precision (mAP). Our developed model achieved an mAP score of 0.5377 on the experimental test data which won 10th place on the public leaderboard. The proposed approach represents a noteworthy breakthrough in the field and holds the potential to significantly improve motorcycle safety

An Automatic Helmet Detection System using Convolution Neural Network

Applications for automatic licence plate identification and helmet detection are most useful on busy highways where accidents are more often. Although the government has adopted a number of restrictions, the motorcycle riders haven't been following them very well, necessitating the use of several cunning strategies. Today, it is difficult to distinguish between helmeted and non-helmeted motorcyclists, thus new technology is required to do so as well as to read the rider's licence plate. It aids in accident prevention and increases people's mental alertness. In this study, CNN machine learning set of rules are used to create automatic helmet identification and automatic licence plate recognition applications. faster CNN algorithm is utilised to find the helmet