Deteksi Helmet Dan Vest Keselamatan Secara Realtime Menggunakan Metode Yolo Berbasis Web Flask

Hatami, Muhammad; Tukino, Tukino; Nurapriani, Fitria; Widiyawati, Widiyawati; Andriani, Wresti

doi:10.47668/edusaintek.v10i1.651

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Using object detection technology for workplace safety is one of the solutions offered to reduce the number of work accidents. Several methods are used in the object detection process such as Haar Cascade [5], Support Vector Machine (SVM) [2], Component Principal Analyst (CPA) [4], Naïve Bayes [6] [7] and YOLO [3] [12]. Naïve Bayes only requires a small amount of training data compared to other methods that require quite a lot of data to achieve a high level of accuracy [6], this makes Naïve Bayes widely used to optimize the object detection process.…”

Section: Introductionmentioning

confidence: 99%

Arc Circularitie Naive Bayes for Occupational Safety Helmet Detection

Rizaldi,

Putranto

2023

Compiler

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Occupational Safety and Health (OHS) is an effort to guarantee and protect the safety and health of every worker through efforts to prevent work accidents and work-related diseases. Safety Helmet is one of the components that must exist and be used in accordance with Occupational Safety and Health standards. Detection of safety helmets usage is one of the efforts to support these activities. The application of Arc Circularity Naive Bayes is used to detect whether an object meets the ratio of a circle by utilizing RGB and HSV image filtering and classification using Naïve Bayes. That method is used to detect whether a worker uses a safety helmet or not, it also detects helm color. The average value of accuracy is 50.8, precision is 58.3%, recall is 66.0%, and f1-score is 59.5% which are calculated using the Confusion Matrix

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Section: Introductionmentioning

confidence: 99%

Arc Circularitie Naive Bayes for Occupational Safety Helmet Detection

Rizaldi,

Putranto

2023

Compiler

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Applying Digital Images to Identify Pavement Damage in Support of The Road Infrastructure Development Program

Hardiyanti,

Kristanto,

Yustita

et al. 2024

E3S Web Conf.

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Road damage can cause discomfort while driving and even lead to accidents. According to the National Road Network Condition Map Data in 2017, the level of severe and minor road damage in the East Java region had reached 288 kilometers. Based on this data, periodic road condition assessments and maintenance are essential to minimize damage. Road maintenance efforts are crucial to support road infrastructure development programs. The initial step in road maintenance is to identify road damage, determining the necessary actions to be taken. In this research, road pavement damage identification is carried out using the Yolov5, Yolov6, and Yolov7 methods. Test results indicate that the Yolov5 method performed the best with a validation mAP (mean Average Precision) score of 42%, a Precision value of 0.544, and a Recall value of 0.453. These scores indicate that the accuracy of road pavement damage detection using the YOLO algorithm for depression, corrugation, potholes, and alligator cracking is at its maximum level.

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Deteksi Helm Keselamatan Menggunakan Jetson Nano dan YOLOv7

Hadi Supriyanto,

Sarosa Castrena Abadi,

Aliffa Shalsabilah

2024

J. Appl. Comput. Sci. Technol.

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Increasing awareness of the importance of head personal protective equipment in industrial and construction environments has become a major concern in efforts to improve occupational safety. This research developed an early detection system for the use of computer vision-based head protective equipment using the YOLOv7 model and the Jetson Nano controller. The YOLOv7 algorithm was chosen for its ability for fast and accurate object detection. The YOLOv7 model was trained with a total dataset of 2799 images and iterations of 100 epochs to detect head personal protective equipment with a high degree of accuracy. The system captures imagery, activates a warning alarm, and sends a notification to Telegram when a violation occurs on an object that is not wearing a safety helmet. The test results using the confusion matrix method showed that the developed system was able to detect head personal protective equipment with an accuracy rate of 97.23%, which shows the system's ability to recognize personal protective equipment with very high accuracy. In addition, the system also showed a precision value of 98.71% indicating that all detections performed were correct, and a recall of 95.63% which describes the system's ability to recognize most of the head personal protective equipment available. The average FPS result using GPU with CUDA on Jetson Nano reached 5,723 FPS.

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Deteksi Helmet Dan Vest Keselamatan Secara Realtime Menggunakan Metode Yolo Berbasis Web Flask

Cited by 3 publications

References 12 publications

Arc Circularitie Naive Bayes for Occupational Safety Helmet Detection

Arc Circularitie Naive Bayes for Occupational Safety Helmet Detection

Applying Digital Images to Identify Pavement Damage in Support of The Road Infrastructure Development Program

Deteksi Helm Keselamatan Menggunakan Jetson Nano dan YOLOv7

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