Intelligent mask wearing detection system based on YOLOv8 algorithm

The world is facing the challenge of public health emergencies, and wearing masks has become a key measure to prevent the spread of viruses. Therefore, it is crucial to develop an efficient and accurate mask wearing detection system. We have designed an intelligent detection system based on the YOLOv8 framework, which utilizes high-precision cameras for face detection and combines YOLOv8 training models to accurately recognize mask wearing situations. A convenient human-computer interaction interface has been built using the PyQt5 development library, supporting image and video detection. Software testing has validated the system's high accuracy and rapid detection capability. This system has made positive contributions to improving the efficiency of epidemic prevention and control, enhancing public self-protection awareness, and providing a framework and ideas for the development of similar systems. In addition, there is still room for improvement in the adaptability and recognition accuracy of the system in complex environments, and further optimization of algorithms is needed to cope with more complex and changing application scenarios.