Advancing real-time fuel classification with novel multi-scale and multi-level MHOG and light gradient boosting machine

Abstract

Accurately classifying petrol and diesel fuel using an image processing method is crucial for fuel-related industries such as petrol pumps, refineries, and fuel storage facilities. However, distinguishing between these fuels using traditional methods can be challenging due to their similar visual characteristics. This article introduces a novel multi-scale and multi-level modified histogram of oriented gradients (MHOG) feature descriptors for robust classification of fuel images. Our proposed method involves extracting distinctive features from the images using the novel multi-scale and multi-level MHOG feature descriptor. These features are then utilized to train a range of machine learning classifiers with different hyperparameter settings for an ablation study. To the best of our knowledge, this is the first ablation study for this fuel classification application. To evaluate the effectiveness of our approach, we conduct experiments on a carefully labeled dataset consisting of petrol and diesel fuel images. The results demonstrate the high accuracy of our proposed method, achieving a classification accuracy of 98% using the light gradient boosting machine (LGBM). Furthermore, our method surpasses existing state-of-the-art techniques for fuel image classification. With its superior performance, this approach holds great potential for efficient and effective fuel classification in diverse fuel-related industries.