A New Benchmark Dataset for Texture Image Analysis and Surface Defect Detection

TL;DR

This paper introduces the STI dataset, a comprehensive benchmark for texture analysis and surface defect detection, featuring real-world properties like rotation, zoom, and class imbalance, to advance computer vision applications.

Contribution

The paper presents a novel, realistic benchmark dataset for texture and defect analysis, addressing real-world challenges such as rotation, zoom, and class imbalance.

Findings

Descriptors evaluated on STI dataset show its effectiveness.

STI dataset outperforms existing datasets in realism and complexity.

Benchmark facilitates improved defect detection methods.

Abstract

Texture analysis plays an important role in many image processing applications to describe the image content or objects. On the other hand, visual surface defect detection is a highly research field in the computer vision. Surface defect refers to abnormalities in the texture of the surface. So, in this paper a dual purpose benchmark dataset is proposed for texture image analysis and surface defect detection titled stone texture image (STI dataset). The proposed benchmark dataset consist of 4 different class of stone texture images. The proposed benchmark dataset have some unique properties to make it very near to real applications. Local rotation, different zoom rates, unbalanced classes, variation of textures in size are some properties of the proposed dataset. In the result part, some descriptors are applied on this dataset to evaluate the proposed STI dataset in comparison with other state-of-the-art datasets.