Road crack detection using deep convolutional neural network

Zhang, Lei; Yang, Fan; Zhang, Yimin Daniel; Zhu, Ying

doi:10.1109/icip.2016.7533052

Cited by 1,116 publications

(706 citation statements)

References 15 publications

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Section: Introductionmentioning

confidence: 99%

“…And more re-7 cent systems, including the best systems (Wang and Lan, 2015; Oepen et al, 2016) at the recent CONLL shared tasks on PDTB-style shallow discourse parsing (Xue et al, 2015(Xue et al, , 2016, while not using a sequence model, still incorporate features about neighboring relations. Such systems have many applications, including summarization (Louis et al, 2010), information extraction (Huang and Riloff, 2012), question answering (Blair-Goldensohn, 2007), opinion analysis (Somasundaran et al, 2008), and argumentation (Zhang et al, 2016).This paper describes our experiments in annotating cross-paragraph implicit relations in the PDTB (Section 2), with the goal of producing a set of guidelines (Section 3) to annotate such relations reliably (Section 4) and produce a representative dataset annotated with complete sequences of inter-sentential relations.Our main findings from the experiments are as follows:• The ratio of cross-paragraph implicit relations between non-adjacent sentences and between adjacent sentences is almost 1 to 1 (47% vs 51% in our experiment). This is similar to the distribution of cross-paragraph explicit relations (Prasad et al, 2010).…”

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confidence: 99%

“…investigated the utility of sequential features in tweets, emphasizing a subtype of sarcasm that consists of an expression of positive emotion contrasted with a negative situation, and showed that sequential features performed much better than features that did not capture sequential information. More recent work on sarcasm has focused specifically on sarcasm identification on Twitter using neural network approaches (Poria et al, 2016;Ghosh and Veale, 2016;Zhang et al, 2016;Amir et al, 2016).Other work emphasizes features of semantic incongruity in recognizing sarcasm (Joshi et al, 2015;Reyes et al, 2012). Sarcastic RQs clearly feature semantic incongruity, in some cases by expressing the certainty of particular facts in the frame of a question, and in other cases by asking questions like "Can you read?"…”

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Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

2017

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We are excited to welcome you to this year's SIGdial Conference, the 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue. We are pleased to hold the conference in Saarbrücken on August 15-17th, co-located with SemDial 2017, and in close proximity to both INTERSPEECH 2017 and YRRSDS 2017, the Young Researchers' Roundtable on Spoken Dialog Systems.The SIGdial conference remains a premier publication venue for research in discourse and dialogue. This year, the program includes oral presentations, poster sessions, and one demo session. SIGdial 2017 also hosts three special sessions, two joint with SemDial 2017.We received 113 submissions this year. In only one previous year has there been a greater number of submissions to SIGdial. All long and short papers received at least 3 reviews. We carefully considered both the numeric ratings and the tenor of the comments in making our selections for the program. Overall, the members of the Program Committee did an excellent job in reviewing the submitted papers. We thank them for their important role in selecting the accepted papers and for helping to come up with a high quality program for the conference. We also thank Pierre Lison, Mentoring Chair for SIGdial 2017, for his dedicated work on the mentoring process. The goal of mentoring is to assist authors of papers that contain important ideas but lack clarity. In line with the SIGdial tradition, our aim has been to create a balanced program that accommodates as many favorably rated papers as possible. We accepted 29 long papers, 11 short papers and 7 demo presentations. These numbers give an overall acceptance rate of 41.6%. The rates separately for types of papers are 42% for long papers, 30% for short and 87% for demo submissions. Of the long papers, 18 were presented as oral presentations. The remaining 11 long papers and all the short papers were presented as posters, split into two poster sessions.This year SIGdial has three special sessions on topics of growing interest. The chosen sessions were (i) the Special Session on Negotiation Dialog organized by Amanda Stent, Aasish Pappu, Diane Litman and Marilyn Walker; (ii) the Second WOCHAT Special Session on Chatbots and Conversational Agents organized by Ryuichiro Higashinaka, Ron Artstein, Rafael E. Banchs, and Wolfgang Minker; and (iii) Special Session on Natural Language Generation for Dialog Systems organized by Marilyn Walker, Verena Rieser, Vera Demberg, Dietrich Klakow, Dilek Hakkani-Tur, David M. Howcroft and Shereen Oraby. These specialized topics brought diverse paper submissions to our technical program. At the conference, the special sessions also featured panel discussions and position talks, allowing for active engagement of the conference participants. This year, two of these special sessions, the one on negotiation and on conversational agents, are part of the joint SIGdial/SemDial program at the conference bringing both communities to participate in them. This year's SIGdial conference runs 3 full days compared to previous year...

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Section: Introductionmentioning

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Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

2017

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“…However, pre-processing continues to be a challenging task due to reasons such as the fact that noises and cracks have the same texture [15], the presence of traces such as tire tracks, oil spills etc. on the road surfaces [19], the fact that cracks do not have the same density, and low contrast [21] etc. The road cracks constitute the continuous and darkest region of the image [10].…”

Section: Introductionmentioning

confidence: 99%

A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation

Akarsu

Karaköse

Parlak

et al. 2016

International Journal of Applied Mathematics, Electronics and Computers

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Road defect is one of the most important factors for traffic accident. Therefore, these defects should be corrected as soon as possible. It usually occurs cracks, rutting, and potholes in road surface. These errors are based on the fact that people have recognized and fixed these errors in our day. But if these errors are not corrected in a short time, the size of the error grows day by day. There are various methods used to detect road errors in the literature. One of these methods is the use of computer vision. There are various types of roads in real life. Since the studies in the literature have been carried out only by taking into account one type of road, the accuracy rates decrease when these studies are used in different types of roads. In the study carried out, different roads have been made adaptive by the operations performed in the detection of road errors from the received images. Images taken from the camera on a vehicle are used for the study. The study applied is ensured to have high accuracy rates in different types of roads via customization. In the second stage, the image blurred by using median filter and the unprocessed images are collected, and the darkest parts of the image are brought into the forefront. The image is converted into a binary image and improved by mathematical morphological operations. As a result of the operations performed, which of the five classes including un-cracked roads, superficial crack, crocodile crack, linear crack and transverse crack the roads belong to is determined. In the study carried out, the fact that it is fast and that its accuracy rates are good indicate that it can be used in real life.

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“…Compared with LV segmentation methods, the direct LV volumes prediction methods without segmentation are popular in recent years, especially along with the wide use of deep learning technology in the field of medical images processing [9,10]. The research group of Li Shuo proposed a series of methods for direct LV function indexes prediction method based on machine learning technologies [11][12][13], for example, the method based on adapted Bayesian formulation, the method based on linear support vector machine , and the method based on multiscale deep networks and regression forests.…”

Section: Introductionmentioning

confidence: 99%