• artificial intelligence
• computer vision
• image analysis
• indoor localization
• industrial safety
• iot
• performance evaluation
• uwb

This thesis presents a system designed to detect and prevent hazardous situations in industrial environments, with the objective of enhancing the safety of workers. The system employs a high-precision indoor positioning system based on ultra-wideband wireless technology to detect the presence of individuals in potentially hazardous areas. Additionally, the system utilizes image analysis from cameras based on deep learning techniques to determine whether workers are wearing personal protective equipment. Furthermore, a performance evaluation of a commercial UWB-based system is conducted to assess the accuracy and reliability of UWB-based RTLS in a variety of operational environments.