Feature pyramid network with self-guided attention refinement module for crack segmentation
出版年份 2022 全文链接
标题
Feature pyramid network with self-guided attention refinement module for crack segmentation
作者
关键词
-
出版物
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
Volume -, Issue -, Pages 147592172210895
出版商
SAGE Publications
发表日期
2022-05-24
DOI
10.1177/14759217221089571
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- The 1st International Project Competition for Structural Health Monitoring (IPC-SHM, 2020): A summary and benchmark problem
- (2021) Yuequan Bao et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- Pixel-level pavement crack segmentation with encoder-decoder network
- (2021) Youzhi Tang et al. MEASUREMENT
- A Crack Detection Algorithm for Concrete Pavement Based on Attention Mechanism and Multi-Features Fusion
- (2021) Zhong Qu et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional Neural Network
- (2021) Chengjia Han et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Pavement crack detection network based on pyramid structure and attention mechanism
- (2020) Xuezhi Xiang et al. IET Image Processing
- A research on an improved Unet-based concrete crack detection algorithm
- (2020) Lingxin Zhang et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- Automated pavement crack detection and segmentation based on two‐step convolutional neural network
- (2020) Jingwei Liu et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- Machine learning paradigm for structural health monitoring
- (2020) Yuequan Bao et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- The State of the Art of Data Science and Engineering in Structural Health Monitoring
- (2019) Yuequan Bao et al. Engineering
- Group Normalization
- (2019) Yuxin Wu et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
- (2019) Yi Wang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Data Augmentation Using Random Image Cropping and Patching for Deep CNNs
- (2019) Ryo Takahashi et al. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
- Investigation of fatigue and thermal cracking behavior of rejuvenated reclaimed asphalt pavement binders and mixtures
- (2018) Mohamed Elkashef et al. INTERNATIONAL JOURNAL OF FATIGUE
- Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images
- (2018) Yang Xu et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images
- (2017) Yang Xu et al. Structural Control & Health Monitoring
- Efficient pavement crack detection and classification
- (2017) A. Cubero-Fernandez et al. EURASIP Journal on Image and Video Processing
- Automatic Road Crack Detection Using Random Structured Forests
- (2016) Yong Shi et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Effects of Pavement Surface Conditions on Traffic Crash Severity
- (2015) Jaeyoung Lee et al. JOURNAL OF TRANSPORTATION ENGINEERING
- Effects of Pavement Surface Conditions on Traffic Crash Severity
- (2015) Jaeyoung Lee et al. JOURNAL OF TRANSPORTATION ENGINEERING
- Life cycle environmental benefits of pavement surface maintenance
- (2014) Luc Pellecuer et al. CANADIAN JOURNAL OF CIVIL ENGINEERING
- FoSA: F* Seed-growing Approach for crack-line detection from pavement images
- (2011) Qingquan Li et al. IMAGE AND VISION COMPUTING
- Critical Assessment of Pavement Distress Segmentation Methods
- (2009) Yi-Chang Tsai et al. JOURNAL OF TRANSPORTATION ENGINEERING
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