Automatic detection of ultrasonic water meter LCD pattern elements based on ConvNeXt

Liquid crystal display (LCD) screens are widely used in various types of smart meters, with ultrasonic water meters being one of their applications. The display on LCD screens is composed of various digits and icons, which are LCD pattern elements that carry significant attribute information. The current industrial inspection of LCD pattern elements often relies on manual meter reading methods, which has high labor cost and low accuracy. Therefore, In this study, the ConvNeXt model based on deep learning is used to achieve automatic detection of ultrasonic water meter LCD pattern elements. Initially, image processing techniques are utilized to segment the images, thereby extracting a dataset of independent LCD pattern elements; this dataset is input into the ConvNeXt model for training. Subsequently, a user interface is designed to visualize the detection results. The study presents an automatic detection system capable of identifying 18 types of LCD pattern elements with an accuracy rate of up to 99.94%. The ConvNeXt model demonstrates exceptional recognition precision in the automatic detection of ultrasonic water meter LCD pattern elements, indicating its significant practical value and application potential.