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Visualization for Explanation of Deep Learning-Based Defect Detection Model Using class activation map

Computers, Materials & Continua 2023年第6期75卷 4753-4766页

作者： Hyunkyu Shin Yonghan Ahn Mihwa Song Heungbae Gil Jungsik Choi Sanghyo Lee Center for AI Technology in Construction Hanyang University ERICAAnsan15588Korea School of Architecture and Architectural Engineering Hanyang University ERICAAnsan15588Korea ICT Convergence Research Division Korea Expressway Corporation Research InstituteHwaseong18489Korea Department of Architecture College of EngineeringKangwon National UniversitySamcheok25913Korea Division of Smart Convergence Engineering Hanyang University ERICAAnsan15588Korea

Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.

关键词： Defect detection visualization class activation map deep learning explanation visualizing evaluation index

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Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN

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Computer Systems Science & Engineering 2023年第1期44卷 595-612页

作者： D.Banumathy Osamah Ibrahim Khalaf Carlos Andrés Tavera Romero P.Vishnu Raja Dilip Kumar Sharma Department of Computer Science and Engineering Paavai Engineering CollegePachalNamakkal637018Tamil NaduIndia Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdadIraq COMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de Cali76001CaliColombia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia

The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer Detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less time.This paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the mammogram.When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer Detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with class activation map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological image.The test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications ***50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.

关键词： Computer-Aided Detection breast cancer detection convolutional neural network class activation map computer-aided diagnosis

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Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation

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Journal of Leather Science and Engineering 2022年第1期4卷 90-110页

作者： Praveen Kumar Moganam Denis Ashok Sathia Seelan Department of Design Automation Cyber Physical Systems LaboratorySchool of Mechanical EngineeringVellore Institute of TechnologyVellore 632014India

Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature;hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is neces-sary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classifica-tion of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented.

关键词： Convolution neural networks Machine learning classifier Leather defects Multi class classification class activation map Segmentation

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A convolutional neural network based approach to sea clutter suppression for small boat detection

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Frontiers of Information Technology & Electronic Engineering 2020年第10期21卷 1504-1520页

作者： Guan-qing LI Zhi-yong SONG Qiang FU National Key Laboratory of Science and Technology on ATR College of Electronic Science and TechnologyNational University of Defense TechnologyChangsha 410073China

Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios.In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm,to solve the problem caused by low signal-to-clutter ratios in actual situations on the sea surface.Dual activation has two steps.First,we multiply the activated weights of the last dense layer with the activated feature maps from the upsample layer.Through this,we can obtain the class activation maps(CAMs),which correspond to the positive region of the sea clutter.Second,we obtain the suppression coefficients by mapping the CAM inversely to the sea clutter spectrum.Then,we obtain the activated range-Doppler maps by multiplying the coefficients with the raw range-Doppler maps.In addition,we propose a sampling-based data augmentation method and an effective multiclass coding method to improve the prediction accuracy.Measurement on real datasets verified the effectiveness of the proposed method.

关键词： Convolutional neural networks class activation map Short-time Fourier transform Small target detection Sea clutter suppression

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Expression Analysis Based on Face Regions in Real-world Conditions

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International Journal of Automation and computing 2020年第1期17卷 96-107页

作者： Zheng Lian Ya Li Jian-Hua Tao Jian Huang Ming-Yue Niu National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences(CAS)Beijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China

Facial emotion recognition is an essential and important aspect of the field of human-machine interaction.Past research on facial emotion recognition focuses on the laboratory environment.However,it faces many challenges in real-world conditions,i.e.,illumination changes,large pose variations and partial or full occlusions.Those challenges lead to different face areas with different degrees of sharpness and completeness.Inspired by this fact,we focus on the authenticity of predictions generated by differentpairs.For example,if only the mouth areas are available and the emotion classifier predicts happiness,then there is a question of how to judge the authenticity of predictions.This problem can be converted into the contribution of different face areas to different emotions.In this paper,we divide the whole face into six areas:nose areas,mouth areas,eyes areas,nose to mouth areas,nose to eyes areas and mouth to eyes areas.To obtain more convincing results,our experiments are conducted on three different databases:facial expression recognition+(FER+),real-world affective faces database(RAF-DB)and expression in-the-wild(ExpW)dataset.Through analysis of the classification accuracy,the confusion matrix and the class activation map(CAM),we can establish convincing results.To sum up,the contributions of this paper lie in two areas:1)We visualize concerned areas of human faces in emotion recognition;2)We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis.Our findings can be combined with findings in psychology to promote the understanding of emotional expressions.

关键词： Facial emotion analysis face areas class activation map confusion matrix concerned area

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