Automated Student Detection for Safety Assurance within Challenge-Based Learning

Detecting the pose of an student in an educational workspace that has heavy machinery or dangerous hazards is a safety precaution that in automated safeguard protocols is not yet applied. To address this gap, we propose a novel solution that combines the Intel (R) RealSenseT Depth Camera D435 with the YOLOv8 algorithm for object detection. The utilization of YOLOv8, is a technology which allows for rapid detection of objects of interest, making it ideal for maintaining low response times. Additionally, the integration of stereo camera technology enables the precise identification of human positions within an XY divided space. The convolutional neural network architecture formulated by the algorithm enables the ability to identify and classify human beings on an image with a confidence level superior to 50%. Once this is done, the infrared sensors in the camera are utilized to deliver an estimate of the distance in meters from the camera to the median location of the person in the picture. With the use of triangulation performed by the stereoscopic vision with a reference marker, one can proceed to generate an approximation of the individual's relative coordinates in the closed space where the task is monitored. This technology empowers students to proactively assess their proximity to restricted or hazardous areas in educational laboratories, enhancing their safety awareness. By avoiding potentially dangerous spaces, students increase their adherence to safety protocols, leading to improved work efficiency and productivity within real workshop settings.