Computer Vision-Based Smart Camera for Safety Helmet Detection in Work Areas
TL;DR
A YOLOv8-based visual detection application in ONNX format to identify safety helmet violations in real-time to demonstrate the application's stability and accuracy in computer vision-based automated surveillance.
Abstract
The application of computer vision technology in automation systems plays a crucial role in improving the efficiency of occupational safety monitoring in industrial environments. This study developed a YOLOv8-based visual detection application in ONNX format to identify safety helmet violations in real-time. The system was developed using Python with a Tkinter-based user interface and integrated with a Flask web dashboard that displays violation log data. The application can accept video input from various sources, including webcams, USB cameras, and IP cameras, to classify the type of helmet being used. Only orange and white safety helmets are considered valid. Detecting a new helmet, a motorcycle helmet, or a helmet with an inappropriate colour will trigger an alarm and store the image as evidence of the violation. The YOLOv8 model was trained on a six-class dataset and demonstrated good performance, with a precision of 0.921, a recall of 0.859, an mAP50 value of 0.919, and an mAP50-95 value of 0.619. System evaluation demonstrated the application's stability and accuracy in computer vision-based automated surveillance.