Wood Surface Inspection Using Structural and Conditional Statistical Features

TL;DR

This paper presents a novel machine vision approach for automatic wood surface defect detection, utilizing support region extraction and innovative statistical features to improve inspection accuracy.

Contribution

It introduces new structural and conditional statistical features based on gradient and Laplacian responses for better defect classification in wood surfaces.

Findings

High accuracy defect classification on large datasets

Effective support region extraction method

Promising results demonstrating improved inspection performance

Abstract

Surface quality is an extremely important issue for wood products in the market. Although quality inspection can be made by a human expert while manufacturing, this operation is prone to errors. One possible solution may be using standard machine vision techniques to automatically detect defects on wood surfaces. Due to the random texture on wood surfaces, this solution is also not possible most of the times. Therefore, more advanced and novel machine vision techniques are needed to automatically inspect wood surfaces. In this study, we propose such a solution based on support region extraction from the gradient magnitude and the Laplacian of Gaussian response of the wood surface image. We introduce novel structural and conditional statistical features using these support regions. Then, we classify different defect types on wood surfaces using our novel features. We tested our automated wood surface inspection system on a large data set and obtained very promising results.