A PyTorch-Based Deep Learning Approach for Enhanced Liquid Level Detection in Industrial Environments

The accurate detection of liquid levels is of paramount importance across various industries, including pharmaceuticals, beverages, and chemicals. Traditionally, monitoring liquid levels within containers has relied on manual procedures or basic sensor technology, which often encounters challenges related to precision and speed. However, as the field of computer vision advances, there is a growing interest in leveraging more advanced techniques to overcome these limitations and enhance liquid-level monitoring. In this context, this research proposes the utilization of PyTorch, a powerful open-source deep learning framework, to tackle the intricacies associated with liquid-level monitoring. By transitioning from traditional computer vision methods to advanced deep learning techniques facilitated by PyTorch, this study aims to significantly improve the accuracy, efficiency, and reliability of liquid-level detection across industrial applications.