Implementation of Personal Protective Equipment Detection Using Django and Yolo Web at Paiton Steam Power Plant (PLTU)

Work accidents can occur at any time and unexpectedly, so work safety is associated with health because the work safety system in Indonesia is related to the K3 (Occupational Safety and Health) program. To create a safe and healthy work environment, occupational safety and health management are implemented to avoid work accidents by requiring every worker to use Personal Protective Equipment (PPE). This research aims to develop an immediate detection system for violations of Personal Protective Equipment (PPE) in the workplace using the Yolov8 Method and the Django web-based user interface framework. Yolov8 is one of the latest deep-learning object identification models while Django is the most popular Python developer framework. The system is designed to improve workplace safety and prevent accidents by monitoring compliance with PPE requirements. The research methodology involves literature study, image data collection, preprocessing, model training, and system deployment using the Django framework. There are four classes of detection based on the bounding box according to the specified color, the use of helmets and safety vests based on the red bounding box for helmets and blue for vests while when helmets and safety vests are not being used, based on green and yellow bounding boxes. The system successfully detected four PPE classes with an average accuracy of 82.3% from 230 test data, a mAP50 value of 81.6%, a precision value of 90.3%, and a recall value of 75.1%. The findings from this study indicate that the developed system can effectively improve occupational safety and health management. However, there is a detection error factor caused by the lighting and specifications of the camera used. Future research can focus on integrating the system with other work safety systems to provide a comprehensive solution for accident prevention

Centralised and decentralised sensor fusion‐based emergency brake assist

Copyright: © 2021 by the authors. Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sen-sors. As mono‐sensor systems cannot provide reliable and consistent readings under all circum-stances because of errors and other limitations, fusing data from multiple sensors ensures that the environmental parameters are perceived correctly and reliably for most scenarios, thereby substan-tially improving the reliability of the multi‐sensor‐based automotive systems. This paper first high-lights the significance of efficiently fusing data from multiple sensors in ADAS features. An emergency brake assist (EBA) system is showcased using multiple sensors, namely, a light detection and ranging (LiDAR) sensor and camera. The architectures of the proposed ‘centralised’ and ‘decentral-ised’ sensor fusion approaches for EBA are discussed along with their constituents, i.e., the detection algorithms, the fusion algorithm, and the tracking algorithm. The centralised and decentralised architectures are built and analytically compared, and the performance of these two fusion architectures for EBA are evaluated in terms of speed of execution, accuracy, and computational cost. While both fusion methods are seen to drive the EBA application at an acceptable frame rate (~20fps or higher) on an Intel i5‐based Ubuntu system, it was concluded through the experiments and analyt-ical comparisons that the decentralised fusion‐driven EBA leads to higher accuracy; however, it has the downside of a higher computational cost. The centralised fusion‐driven EBA yields compara-tively less accurate results, but with the benefits of a higher frame rate and lesser computational cost