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

• URL: http://arxiv.org/abs/2307.11643v3
• Date: Tue, 10 Oct 2023 09:45:30 GMT
• Title: Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models
• Authors: Jiajun Zhang, Georgina Cosma, Sarah Bugby, Axel Finke and Jason Watkins
• Abstract summary: This paper proposes the AI-Reasoner, which extracts morphological characteristics of defects (DefChars) from images. 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.
• Score: 10.498224499451991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Explainable AI for Enhancing Efficiency of DL-based Channel Estimation [1.0136215038345013]
  Support of artificial intelligence based decision-making is a key element in future 6G networks. In such applications, using AI as black-box models is risky and challenging. We propose a novel-based XAI-CHEST framework that is oriented toward channel estimation in wireless communications.
  arXiv  Detail & Related papers  (2024-07-09T16:24:21Z)
• SynthTree: Co-supervised Local Model Synthesis for Explainable Prediction [15.832975722301011]
  We propose a novel method to enhance explainability with minimal accuracy loss. We have developed novel methods for estimating nodes by leveraging AI techniques. Our findings highlight the critical role that statistical methodologies can play in advancing explainable AI.
  arXiv  Detail & Related papers  (2024-06-16T14:43:01Z)
• Understanding and Evaluating Human Preferences for AI Generated Images with Instruction Tuning [58.41087653543607]
  We first establish a novel Image Quality Assessment (IQA) database for AIGIs, termed AIGCIQA2023+. This paper presents a MINT-IQA model to evaluate and explain human preferences for AIGIs from Multi-perspectives with INstruction Tuning.
  arXiv  Detail & Related papers  (2024-05-12T17:45:11Z)
• Large Multi-modality Model Assisted AI-Generated Image Quality Assessment [53.182136445844904]
  We introduce a large Multi-modality model Assisted AI-Generated Image Quality Assessment (MA-AGIQA) model. It uses semantically informed guidance to sense semantic information and extract semantic vectors through carefully designed text prompts. It achieves state-of-the-art performance, and demonstrates its superior generalization capabilities on assessing the quality of AI-generated images.
  arXiv  Detail & Related papers  (2024-04-27T02:40:36Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System [7.1083241462091165]
  We introduce an external modality-guided data mining framework, primarily rooted in optical character recognition (OCR), to extract statistical features from images. A key aspect of our approach is the alignment of external modality features, extracted using a single modality-aware model, with image features encoded by a convolutional neural network. Our methodology considerably boosts the recall rate of the defect detection model and maintains high robustness even in challenging scenarios.
  arXiv  Detail & Related papers  (2024-03-18T07:41:39Z)
• Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality Assessment [82.13830107682232]
  We propose a novel class of state-of-the-art (SOTA) generative model, which exhibits the capability to model intricate relationships. We devise a new diffusion restoration network that leverages the produced enhanced image and noise-containing images. Two visual evaluation branches are designed to comprehensively analyze the obtained high-level feature information.
  arXiv  Detail & Related papers  (2024-02-22T09:39:46Z)
• FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks [0.0]
  This paper demonstrates the vulnerability of AI models often utilized downstream tasks on recognized public genomics datasets. We undermine model robustness by deploying an attack that focuses on input transformation while mimicking the real data and confusing the model decision-making. Our empirical findings unequivocally demonstrate a decline in model performance, underscored by diminished accuracy and an upswing in false positives and false negatives.
  arXiv  Detail & Related papers  (2024-01-19T12:04:31Z)
• AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models [1.8752655643513647]
  XAI tools can increase the vulnerability of model extraction attacks, which is a concern when model owners prefer black-box access. We propose a novel retraining (learning) based model extraction attack framework against interpretable models under black-box settings. We show that AUTOLYCUS is highly effective, requiring significantly fewer queries compared to state-of-the-art attacks.
  arXiv  Detail & Related papers  (2023-02-04T13:23:39Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.