XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection
Not on the map. Why?
Abstract (arXiv)
Visual quality inspection systems, crucial in sectors like manufacturing and logistics, employ computer vision and machine learning for precise, rapid defect detection. However, their unexplained nature can hinder trust, error identification, and system improvement. This paper presents a framework to bolster visual quality inspection by using CAM-based explanations to refine semantic segmentation models. Our approach consists of 1) Model Training, 2) XAI-based Model Explanation, 3) XAI Evaluation, and 4) Annotation Augmentation for Model Enhancement, informed by explanations and expert insights. Evaluations show XAI-enhanced models surpass original DeepLabv3-ResNet101 models, especially in intricate object segmentation.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 54 references that are in the map. First posted on arXiv 18 Jan 2024.
Builds on
33 of 54 references.
References · 33
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2015Learning Deep Features for Discriminative Localizationcited by 2,430
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
- 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
- 2019Searching for MobileNetV3cited by 1,984
- 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
- 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
- 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
- 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 703
21 more
- 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 614
- 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 575
- 2022Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyondcited by 171
- 2020Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNscited by 151
- 2018Explaining Image Classifiers by Counterfactual Generationcited by 144
- 2020Restricting the Flow: Information Bottlenecks for Attributioncited by 110
- 2018Attention Branch Network: Learning of Attention Mechanism for Visual Explanationcited by 108
- 2019Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruningcited by 89
- 2020Black-box Explanation of Object Detectors via Saliency Mapscited by 74
- 2018Adversarial Examples that Fool both Computer Vision and Time-Limited Humanscited by 73
- 2020There and Back Again: Revisiting Backpropagation Saliency Methodscited by 60
- 2022Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvementcited by 48
- 2019Embedding Human Knowledge into Deep Neural Network via Attention Mapcited by 33
- 2021Adaptive wavelet distillation from neural networks through interpretationscited by 32
- 2020TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Linescited by 28
- 2020Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networkscited by 17
- 2023Towards Better Explanations for Object Detectioncited by 14
- 2023Towards Trust of Explainable AI in Thyroid Nodule Diagnosiscited by 10
- 2018Guided Zoom: Questioning Network Evidence for Fine-grained Classificationcited by 8
- 2023G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectorscited by 7
- 2022Utilizing Explainable AI for improving the Performance of Neural Networkscited by 5
Built on by
Citing papers per year: 2024: 1.
1 paper.