Research on Flame Image Temperature Prediction Based on CA-ResNet50 and 5G Technology

Abstract

As intermittent kilns, shuttle kilns are often used in the production of daily-use ceramics. The temperature has a significant impact on the products inside the kiln, and currently, most shuttle kilns still rely on human observation of the flame to adjust the temperature, which has uncertainties and limitations. This paper proposes a method for predicting flame images based on CA-ResNet50 and 5G Technology, which utilizes the low latency and high bandwidth characteristics of 5G networks to collect real-time data and ensure the correspondence between flame images and temperature. And combine the CA attention mechanism with the ResNet50 network to improve the network's attention to flame image features, thereby enhancing prediction accuracy. The experimental results show that the proposed method can improve the accuracy of temperature prediction based on flame images, providing new ideas for temperature control in shuttle kilns.