Interpretability Analysis of Convolutional Neural Networks for Crack Detection

Interpretability Analysis of Convolutional Neural Networks for Crack Detection

Crack detection is an important task in bridge health monitoring, and related detection methods have gradually shifted from traditional manual methods to intelligent approaches with convolutional neural networks (CNNs) in recent years. Due to the opaque process of training and operating CNNs, if the...

Full description

Bibliographic Details
Main Authors:	Jie Wu, Yongjin He, Chengyu Xu, Xiaoping Jia, Yule Huang, Qianru Chen, Chuyue Huang, Armin Dadras Eslamlou, Shiping Huang
Format:	Article
Language:	English
Published:	MDPI AG 2023-12-01
Series:	Buildings
Subjects:	structural health monitoring crack detection interpretability analysis convolutional neural network
Online Access:	https://www.mdpi.com/2075-5309/13/12/3095

Similar Items

Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review
by: Armin Dadras Eslamlou, et al.
Published: (2022-11-01)

Concrete crack classification based on fourier image enhancement and convolutional neural network
by: Xiaoli Sun, et al.
Published: (2024-10-01)

Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures
by: Liang Xu, et al.
Published: (2019-08-01)

Predicting characteristics of cracks in concrete structure using convolutional neural network and image processing
by: Waqas Qayyum, et al.
Published: (2023-07-01)

Efficient Detection and Measurements of Bridge Crack Widths Based on Streamlined Convolutional Neural Network
by: Yingjun Wu, et al.
Published: (2025-01-01)

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review
by: Mohsen Azimi, et al.
Published: (2020-05-01)

Detection and recognition of bridge crack based on convolutional neural network
by: Honggong LIU, et al.
Published: (2016-10-01)

An investigation into real-time surface crack classification and measurement for structural health monitoring using transfer learning convolutional neural networks and Otsu method
by: Mazleenda Mazni, et al.
Published: (2024-04-01)

PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
by: Xiaohu Zhang, et al.
Published: (2023-08-01)

A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection
by: Xingjun Gao, et al.
Published: (2022-09-01)

Detection Based on Crack Key Point and Deep Convolutional Neural Network
by: Dejiang Wang, et al.
Published: (2021-11-01)

Crack Detection and Classification in Moroccan Pavement Using Convolutional Neural Network
by: Wafae Hammouch, et al.
Published: (2022-11-01)

Detection and Length Measurement of Cracks Captured in Low Definitions Using Convolutional Neural Networks
by: Jin-Young Kim, et al.
Published: (2023-04-01)

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks
by: Ronghua Fu, et al.
Published: (2020-04-01)

PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor
by: Xiaohu Zhang, et al.
Published: (2023-09-01)

Interpretable surrogate models to approximate the predictions of convolutional neural networks in glaucoma diagnosis
by: Jose Sigut, et al.
Published: (2023-01-01)

Automated Pavement Crack Segmentation Using U-Net-Based Convolutional Neural Network
by: Stephen L. H. Lau, et al.
Published: (2020-01-01)

Quantitative Detection of Pipeline Cracks Based on Ultrasonic Guided Waves and Convolutional Neural Network
by: Yuchi Shen, et al.
Published: (2024-02-01)

Prediction of fatigue crack growth using convolutional neural network (1st Report, Prediction for a single crack with angle)
by: Takuya TOYOSHI, et al.
Published: (2022-10-01)

Research on image recognition of cracked eggs based on convolutional neural network model
by: LI Shu, et al.
Published: (2023-12-01)

Segmentation of micro-cracks in fractured coal based on convolutional neural network
by: Zheng Jiangtao, et al.
Published: (2022-12-01)

Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks
by: Irina Razveeva, et al.
Published: (2023-12-01)

Crack Severity Classification from Timber Cross-Sectional Images Using Convolutional Neural Network
by: Shigeru Kato, et al.
Published: (2023-01-01)

Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network
by: Alexey N. Beskopylny, et al.
Published: (2023-02-01)

Prediction of fatigue crack growth using convolutional neural network (2nd Report, Prediction of crack propagation on different levels)
by: Rekisei OZAWA, et al.
Published: (2023-07-01)

Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat?
by: Hao Xu, et al.
Published: (2022-12-01)

Faster Region-Based Multi-Layer Convolutional Neural Networks for Cracked Detection in Eggshell Images
by: Muammer TÜRKOĞLU
Published: (2021-03-01)

Identification of Concrete Cracks Using Deep Learning Models: A Systematic Review
by: Mazleenda Mazni, et al.
Published: (2024-01-01)

Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks
by: Lu Deng, et al.
Published: (2020-04-01)

UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks
by: Daegyun Choi, et al.
Published: (2021-04-01)

Concrete crack analysis using a deep belief convolutional neural network
by: Ramalingam Geetha, et al.
Published: (2024-01-01)

Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network
by: Shigeru Kato, et al.
Published: (2022-08-01)

Fatigue Crack Evaluation with the Guided Wave–Convolutional Neural Network Ensemble and Differential Wavelet Spectrogram
by: Jian Chen, et al.
Published: (2021-12-01)

Interpretation of convolutional neural networks reveals crucial sequence features involving in transcription during fiber development
by: Shang Liu, et al.
Published: (2022-03-01)

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability
by: Ibrahim Alreshidi, et al.
Published: (2023-11-01)

Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis
by: Kavinda Ashan Kulasinghe Wasalamuni Dewage, et al.
Published: (2024-10-01)

An Algorithm for Crack Detection, Segmentation, and Fractal Dimension Estimation in Low-Light Environments by Fusing FFT and Convolutional Neural Network
by: Jiajie Cheng, et al.
Published: (2023-11-01)

An interpretable hierarchical semantic convolutional neural network to diagnose melanoma in skin lesions
by: Hui-Ching Wu, et al.
Published: (2023-02-01)

A method for superfine pavement crack continuity detection based on topological loss
by: Guohui Jia, et al.
Published: (2023-10-01)

Crack Detection of Concrete Pavement Based on Attention Mechanism and Lightweight DilatedConvolution
by: QU Zhong, WANG Caiyun
Published: (2023-02-01)