Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models

TL;DR

This paper introduces AI-Reasoner, a tool that extracts morphological defect features from images and uses decision trees to explain AI model predictions, enhancing transparency and performance in industrial defect detection.

Contribution

The paper presents AI-Reasoner, a novel framework that combines morphological feature extraction with decision tree reasoning to explain and improve AI defect detection models.

Findings

Effectively explains Mask R-CNN defect predictions

Provides visual and textual interpretability

Enhances model performance through mitigation strategies

Abstract

As the use of artificial intelligent (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide transparent reasoning behind their predictions. This paper proposes the AI-Reasoner, which extracts the morphological characteristics of defects (DefChars) from images and utilises decision trees to reason with the DefChar values. Thereafter, the AI-Reasoner exports visualisations (i.e. charts) and textual explanations to provide insights into outputs made by masked-based defect detection and classification models. It also provides effective mitigation strategies to enhance data pre-processing and overall model performance. The AI-Reasoner was tested on explaining the outputs of an IE Mask R-CNN model using a set of 366 images containing defects. The results demonstrated its effectiveness in explaining the IE Mask R-CNN model's predictions. Overall, the proposed AI-Reasoner provides a solution for improving the performance of AI models in industrial applications that require defect analysis.