Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques
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[1] William T. Freeman,et al. Latent hierarchical structural learning for object detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[2] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3] Tonghua Su,et al. Faster R-CNN based autonomous navigation for vehicles in warehouse , 2017, 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).
[4] Xiaochun Luo,et al. Detecting non-hardhat-use by a deep learning method from far-field surveillance videos , 2018 .
[5] Shivprakash Iyer,et al. Segmentation of Pipe Images for Crack Detection in Buried Sewers , 2006, Comput. Aided Civ. Infrastructure Eng..
[6] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[7] Fereidoon Moghadas Nejad,et al. Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review , 2017 .
[8] Tarek Zayed,et al. Automated defect detection tool for closed circuit television (cctv) inspected sewer pipelines , 2018 .
[9] Huaizu Jiang,et al. Face Detection with the Faster R-CNN , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).
[10] Gang Li,et al. Long-distance precision inspection method for bridge cracks with image processing , 2014 .
[11] Paul W. Fieguth,et al. Morphological segmentation and classification of underground pipe images , 2006, Machine Vision and Applications.
[12] Samir R. Ibadov,et al. Algorithm for detecting violations of traffic rules based on computer vision approaches , 2017 .
[13] Paul Fieguth,et al. Automated detection of cracks in buried concrete pipe images , 2006 .
[14] Yimin D. Zhang,et al. Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).
[15] Chunsun Zhang,et al. An Unmanned Aerial Vehicle‐Based Imaging System for 3D Measurement of Unpaved Road Surface Distresses 1 , 2012, Comput. Aided Civ. Infrastructure Eng..
[16] Xu Wang,et al. Traffic Signs Detection Based on Faster R-CNN , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW).
[17] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[18] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[19] Ikhlas Abdel-Qader,et al. ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGES , 2003 .
[20] Shivprakash Iyer,et al. A robust approach for automatic detection and segmentation of cracks in underground pipeline images , 2005, Image Vis. Comput..
[21] Jin-Hee Lee,et al. ResNet-Based Vehicle Classification and Localization in Traffic Surveillance Systems , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[22] Zhao Lin,et al. A modified faster R-CNN based on CFAR algorithm for SAR ship detection , 2017, 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP).
[23] Chang-Soo Han,et al. Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel , 2007 .
[24] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[25] Hubo Cai,et al. Automatic Detection of Pavement Surface Defects Using Consumer Depth Camera , 2014 .
[26] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[27] Hyuk-Jin Yoon,et al. Analysis of Crack Image Recognition Characteristics in Concrete Structures Depending on the Illumination and Image Acquisition Distance through Outdoor Experiments , 2016, Sensors.
[28] Paul Fieguth,et al. Segmentation of buried concrete pipe images , 2006 .
[29] Kristin J. Dana,et al. Automated Crack Detection on Concrete Bridges , 2016, IEEE Transactions on Automation Science and Engineering.
[30] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[31] Takashi Matsumoto,et al. Development of an Automatic Detector of Cracks in Concrete Using Machine Learning , 2017 .
[32] Chun Liu,et al. Automatic quantification of crack patterns by image processing , 2013, Comput. Geosci..
[33] Weihong Deng,et al. Very deep convolutional neural network based image classification using small training sample size , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).
[34] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[35] Shuji Hashimoto,et al. Image‐Based Crack Detection for Real Concrete Surfaces , 2008 .
[36] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[37] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[38] Xinkai Wu,et al. Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN , 2017 .
[39] Liang Lin,et al. Is Faster R-CNN Doing Well for Pedestrian Detection? , 2016, ECCV.
[40] Yi Fang,et al. UAV Low Altitude Photogrammetry for Power Line Inspection , 2016, ISPRS Int. J. Geo Inf..
[41] Dana H. Ballard,et al. Computer Vision , 1982 .
[42] Oral Büyüköztürk,et al. Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..
[43] Mahmoud R. Halfawy,et al. Efficient Algorithm for Crack Detection in Sewer Images from Closed-Circuit Television Inspections , 2014 .
[44] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[45] Nikolaos Doulamis,et al. Deep Convolutional Neural Networks for efficient vision based tunnel inspection , 2015, 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP).
[46] David G. Lowe,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.
[47] Sidney Nascimento Givigi,et al. Automatic Crack Detection and Measurement Based on Image Analysis , 2016, IEEE Transactions on Instrumentation and Measurement.
[48] Dan Zecha,et al. A closer look: Small object detection in faster R-CNN , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).
[49] Steven C. H. Hoi,et al. Face Detection using Deep Learning: An Improved Faster RCNN Approach , 2017, Neurocomputing.
[50] Ashutosh Bagchi,et al. Image-based retrieval of concrete crack properties for bridge inspection , 2014 .
[51] Tara C. Hutchinson,et al. Image-Based Framework for Concrete Surface Crack Monitoring and Quantification , 2010 .
[52] Paul W. Fieguth,et al. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure , 2015, Adv. Eng. Informatics.
[53] Vipin Chaudhary,et al. Intervertebral disc detection in X-ray images using faster R-CNN , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[54] Andrew Zisserman,et al. Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.
[55] Xin Yang,et al. Real-time vehicle detection and tracking in video based on faster R-CNN , 2017 .
[56] Michael McGill,et al. Introduction to Modern Information Retrieval , 1983 .