Ferrograph image classification

TL;DR

This paper introduces a novel ferrograph image classification model that uses data augmentation, auxiliary loss functions, and multi-scale feature extraction to improve accuracy on small datasets with variable wear particle sizes.

Contribution

The study proposes a new model with data augmentation, patch permutation recognition, and multi-scale features specifically designed for ferrograph image classification.

Findings

Improved accuracy by 9% on ferrograph dataset

Enhanced performance by 20% on mini-CIFAR-10 dataset

Effective handling of small datasets and variable particle sizes

Abstract

It has been challenging to identify ferrograph images with a small dataset and various scales of wear particle. A novel model is proposed in this study to cope with these challenging problems. For the problem of insufficient samples, we first proposed a data augmentation algorithm based on the permutation of image patches. Then, an auxiliary loss function of image patch permutation recognition was proposed to identify the image generated by the data augmentation algorithm. Moreover, we designed a feature extraction loss function to force the proposed model to extract more abundant features and to reduce redundant representations. As for the challenge of large change range of wear particle size, we proposed a multi-scale feature extraction block to obtain the multi-scale representations of wear particles. We carried out experiments on a ferrograph image dataset and a mini-CIFAR-10 dataset. Experimental results show that the proposed model can improve the accuracy of the two datasets by 9% and 20% respectively compared with the baseline.