Digital Construction of Intelligent Laboratories Based on the Internet of Things and Improved YOLOv4 Algorithm

Creating a digital laboratory based on the IoT can achieve a laboratory management system that is standardized on informatization and intelligence. In recent years, the application research of combining deep learning-based object detection technology with various public area surveillance cameras for intelligent monitoring has become increasingly widespread. The improved Y0LOV4 algorithm can identify and locate students’ behaviors in the experimental course, so as to achieve the purpose of automatically supervised learning and improving the teaching environment, and it can also carry out safety monitoring for experimental equipment. This article designs and implements a laboratory information collection device, combined with the IoT technology, to collect, process, and transmit the operational status information and environmental information of experimental equipment through the information collection device. This article proposes an improved YOLOv4 laboratory equipment detection algorithm and conducts research on computer equipment detection and recognition. The aim is to use the algorithm model to identify experimental equipment and achieve the goal of monitoring changes in the amount of equipment. Finally, the effectiveness of the proposed algorithm was verified through comparative experiments. It can address the shortcomings of existing laboratory management systems in monitoring hazardous chemicals, detecting environmental conditions and monitoring security status.