Industrial Software Based on Incremental Learning:Anomaly Recognition System of Converter Image
A converter furnace image anomaly detection system based on incremental learning has been proposed to simplify the research and development process for industrial software and enhance the accuracy and efficiency of image anomaly detection.This system employs machine vision technology to capture images of the converter furnace.It introduces a deep residual network to form an image anomaly detection model,using the captured images to train this model.The system is implemented through low-code development methods and optimizes the iterative updates of the model by incorporating incremental learning algorithms.The recognition accuracy of the converter furnace image anomaly detection model is compared across various neural network archi-tectures.Additionally,the disparities in model accuracy and time consumption between incremental learning and full learning are assessed on a low-code platform.Experimental results demonstrate that this system exhibits good precision and stability in image anomaly detection.On the low-code development platform,the system software based on incremental learning performs excellently in handling large-scale data and real-time scenarios,providing an efficient and low-cost solution for converter furnace image anomaly detection.
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