Advance and Reliable Cooking Oil Frequency Usage Classification via Deep Vision Analysis of Challenging Visual Features

The classification of cooking oil usage in real-world scenarios presents significant challenges due to several varying visual conditions such as angular perspectives, blurriness, and occlusions. Traditional computer vision approaches often struggle with these challenges, leading to reduced reliability in automated systems. This study explores the effectiveness of different deep learning architectures in addressing these challenges for robust cooking oil usage classification. Several selected architectures of convolutional neural networks (CNNs) modals and our proposed modal has been evaluated to determine their performance in handling distorted, blurred, and partially obscured oil images. Through extensive experimentation, proposed model demonstrates superior performance over existing methods, achieving over 99% accuracy. These findings highlight the potential of deep vision analysis in improving classification accuracy for real-world applications, providing insights into model selection for challenging visual feature extraction.