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International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 5 Issue 4, May-June 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD42401 | Volume – 5 | Issue – 4 | May-June 2021 Page 957 An In-depth Review on Bridge Crack Detection Approaches Ketan Ovhal1, Ruturaj Lokhande1, Omkar Kamble1, Prathamesh Nanaware1, Samarsingh Jadhav2 1Student, 2Professor, 1,2Department of Computer Engineering, PVPIT, Pune, Maharashtra, India ABSTRACT Bridges are mega structures that have been utilized and built for millennia. These structures are highly effective in achieving transportation and commute between two highly inaccessible destinations easily. Bridges are also highly effective in reducing traffic was by allowing the use of an alternate path for the traffic flow to be resumed. This makes them highly versatile and extremely effective in various scenarios. But as with any constructed structures, these bridges need to be evaluated for their structural integrity and surveyed for any flaws or cracks that have been emerged over time. This is usually done manually by a civil engineer which is a time-consuming process and can also introduce human error. Therefore to improve this procedure and number of related works have been analyzed extensively to achieve bridge crack detection through image processing methodologies. An effective approach has been envisioned through the use of convolutional neural networks and decision tree techniques to achieve bridge crack detection which will be further elaborated in the next edition of this research article. KEYWORDS: Convolutional Neural Networks, Decision Tree How to cite this paper: Ketan Ovhal | Ruturaj Lokhande | Omkar Kamble | Prathamesh Nanaware | Samarsingh Jadhav "An In-depth Review on Bridge Crack Detection Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4, June 2021, pp.957- 959, URL: www.ijtsrd.com/papers/ijtsrd42401.pdf Copyright © 2021 by author (s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0) I. INTRODUCTION: Bridges are effective and highly useful structures that allow for transportation and computer between two points that are not easily accessible. Bridges allow foreign keys in computer and can effectively reduce the traffic problems by creating another route for transport in the same amount of available space. This allows overlapping roads to effectively allow computers to go on their journey without any hiccups or interruptions by traffic signals or other traffic management devices. Bridges are also highly effective in bridging the gap between accessible entities wherein a road cannot be constructed between the two for allowing effective transport. Bridges are highly useful and have been constructed for millennia. The bridges also can provide a path from one place to another over extremely dangerous territories such as valleys or rivers for people to commute every day. These bridges are useful and can be used to transport goods provide railway tracks or ease traffic conditions in a meaningful way. The civil engineers design these bridges through an extensive survey of various different conditions of the environment and produce the most efficient design to help implement in a safe and structurally sound manner. There are various bridges across the world that are more than a hundred years old and are still standing and being used regularly by the people. The reason these bridges are still effectively useful is due to effective design methodologies and principles along with highly effective maintenance. As the bridges are made in extreme environments they are subjected to environmental forces which can be slowly damaging to the colossal bridge structure. The bridges need to be structurally perfect so that there is no mishap that can occur while using those bridges for transportation or commute. Engineers are effectively performing regular service on various bridges that are still standing today to determine the amount of damage that is being done onto the bridge and provide any effective repairs before any mishap occurs. As bridges are used extensively there is a need to maintain the structural performance in order to produce maximum performance from the bridges and achieve effective improvements. Usually, the process of bridge maintenance is done manually where the engineers would physically examine the structure of the bridge along with various important landmarks such as beams columns, and other rigid structures for early signs of weakness or structural loss of integrity by looking out for cracks. This is a highly laborious process and it is extremely time-consuming for the engineer to perform this survey which is highly essential for maintaining the integrity and the structural reliability of the bridge intact. Therefore there is a need to improve this procedure through the implementation of technology to achieve effective maintenance of the bridge structures. For this purpose, a number of researchers have been analyzed to achieve the prescribed approach in the implementation of image IJTSRD42401 International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD42401 | Volume – 5 | Issue – 4 | May-June 2021 Page 958 processing approaches for identifying cracks in the bridge structure. A large number of researchers have been analyzed for the purpose of achieving our goals of implementing image processing approaches for bridge crack analysis. These approaches have been significant in achieving our approach for the implementation of an effective crack detection methodology that utilizes image normalization along with convolutional neural networks and the decision tree approach. This literature survey paper dedicates section 2 for analysis of past work as a literature survey, and finally, section 3 concludes the paper with traces of future enhancement. II. RELATED WORKS V.M. Jovanovic sequential-probability-ratio-test (SPRT) is proposed and analyzed for Detector logic depends on the optimal. [1] This proposed technology is also compared with the heuristic sequential technique. Pseudo-noise waveform is generated locally due to correlation received from the waveform. The simple “generalized” up-down counter logic is approximated detector with devised. Thus this new detector is compared with some heuristic sequential which are analyzed in past. T.Thesken introduces Continuous-phase modulation (CPM) for a relatively narrow spectrum. Is environment is coherent by using an additive-white- Gaussian-noise (AWGN) for the optimal receiver. [2] They calculated the channel’s cutoff rate, Ro. Just to determine the good a channel is when encoded. Thus to find the encoded data and to check the best performance code. Thus by using random coding parameter Ro behavior of the channel with coding is analyzed on average. Gary Garrabrant presents Additive White Gaussian Noise (AWGN) channels for the non-coherent detection of orthogonal signals is implements trellis codes.[3] For a communication system, an alternative coding technique is developed by using non-coherent detection of the orthogonal signal set where the encoding can be expanded. To prove the trellis coding is successful it is compared with a non-binary block code. J.Zhang states beam bridges get cracks or girders due to overweight freight vehicles, earthquakes. To detect the crackings and faults there is a lot of research made in recent years. [4] For fault detection purposes vertical and longitudinal axial is observed by using the response signals. Thus to cracking fault detection by using traditional vertical vibration signal is very sensitive by measuring longitudinal strain. S.Zhang presents object detection thus to detect objects of present categories in the field of computer vision. Thus by using image recognition and video surveillance for accurate object detection there are various applications developed. [5] There various techniques used for crack detection such as anchor-based detectors with the development of convolutional neural network (CNN). According to statistical features of an object, the proposed paper proposes an Adaptive Training Sample Selection (ATSS). Y.Cai describes one of the diseases that is commonly found that is Cracks in tunnels, buildings, bridges, and other structures. Cracks are often generated regularly or irregularly it has to be repaired on time otherwise it will create a problem. [6] Thus the proposed paper implements a crack measurement system with the combination of a high magnification image acquisition system and laser ranging system. Thus the proposed paper implements an effective approach that is efficient and convenient. H. Cho implements the detection of cracks in concretes by using an image-based methodology. In recent years there has been lots of research made in the field of crack detection but they have to face inaccurate extraction of edge pixels. [7] Thus in proposed paper consist of five steps such as crack region search, crack width transform, hole filling, aspect ratio filtering, and relative thresholding. Thus the experiments were tested on synthetic and real images, which has resulted has consistency in measuring crack and greater accuracy. M.Lin elaborates the issue of the road such as Cracks. In recent years there is a lot of research and many funds invested for the automatic detection of cracks. Thus the proposed paper implements an accurate crack detection method. [8] The method is divided into two parts one is implemented using a hidden Markov random field model and the adaptive line detector and the second is developed by using the integrity conditional connection and by a quantitative description of crack regions. H Rowshande describes crack detection as one of the promising topics of research in recent times. Depending on the low-speed walking stick method they have used an ACFM sensor for classification and detection of RCF cracks. [9] The CTSM algorithm is the existing system that has low-level accuracy thus to overcome this shortcoming thus the proposed paper implements a method for detecting multiple RCF cracks. Thus the detected cracks can be maintained can be used to reduce inspection time. Jingyi Li develops crack detection technology by using Science and technology. Thus the proposed paper develops a crack detection system by using digital image processing. [10] This system is divided into two parts as one part is the hardware part of the detection system and the second is the software part of the crack detection system which is extracted from the acquired images to detect the crack. Thus in hardware image is collected from the car camera and signal processors. Thus the proposed paper is known as an effective approach. Yuwen Quan describes road rehabilitation road crack detection as known as important for road crack detection. [11] On gray histogram is proposed on the basis of Otsu threshold crack detection. Thus the proposed methodology upgrades the accuracy of target extraction and keeps away the prominent peaks. By using the road image processing of various crack types. There are the various steps Crack Image Pre-Processing, Crack Image Segmentation. Thus the proposed paper results provide good consistency in detecting cracks. W.Li elaborates china is known as one of the developing nations in high-speed railways. There are various challenges affected such as damage from external factors and aging of their structures. [12] Thus the proposed paper implements the crack detection method in a very complex environment. The implement is used for histogram equalization just for reducing uneven illumination for the detecting cracks and then Canny edge detection algorithm for adapting dual thresholds. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD42401 | Volume – 5 | Issue – 4 | May-June 2021 Page 959 J.Han explains in the concrete structure inspection field is known as one of the important tasks. There are much techniques implemented such as convolutional neural networks thus they can crack results and blurred crack images [13]. Thus solutions for this introduces a multi-scale feature fusion technique for crack detection. Thus the proposed architecture of CNN develops an encoder-decoder structure for image segmentation technique. Based on the quantitative measurements such as F-measure, recall, and precision. III. CONCLUSION AND FUTURE SCOPE An effective methodology for the purpose of bridge crack detection through the use of image processing techniques has been envisioned in this survey article. Bridges are highly useful structures that provide effective transportation and commute that would have not been possible without the construction of these structures. These structures are usually massive in nature and allow for large trucks and other transportation to be carried out effectively. These mega structures are built by civil engineers through analysis of various environmental conditions and a perfect plan for a bridge is generated and the bridge is built. These bridges also need to be effectively maintained and surveyed for any flaws or that have emerged over time due to this bridge is being subject to harsh environmental conditions. To improve these survey capabilities which is usually performed manually by engineer the utilization of image processing techniques has been proposed in this survey article. This server article has allowed for the creation of our approach which implements convolutional neural networks along with a decision tree to achieve effective bridge crack detection. This approach will be further detailed in the upcoming research article on this topic. REFERENCES [1] V. M. Jovanovic, "Acquisition of frequency-hopping spread spectrum signals by sequential detection," IEEE Military Communications Conference, 'Bridging the Gap. Interoperability, Survivability, Security', 1989, pp. 870-874 vol.3, doi: 10.1109/MILCOM.1989.104044. [2] T. M. Thesken and E. Biglieri, "Cutoff rate for channel having precoding, continuous-phase modulation, and differential detection," IEEE Military Communications Conference, 'Bridging the Gap. Interoperability, Survivability, Security', 1989, pp. 764-768 vol.3, doi: 10.1109/MILCOM.1989.104026. [3] G. Garrabrant and J. Ehrenberg, "Trellis coded modulation applied to noncoherent detection or orthogonal signals," IEEE Military Communications Conference, 'Bridging the Gap. Interoperability, Survivability, Security', 1989, pp. 774-778 vol.3, doi: 10.1109/MILCOM.1989.104028. [4] J. Zhang, L. Li, X. Li and Z. Mu, "Crack detection of beam-like structures under environmental ground motion excitation," 2012 IEEE International Conference on Information Science and Technology, 2012, pp. 447-450, doi: 10.1109/ICIST.2012.6221687. [5] S. Zhang, C. Chi, Y. Yao, Z. Lei and S. Z. Li, "Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 9756-9765, doi: 10.1109/CVPR42600.2020.00978. [6] Y. Cai, X. Fu, Y. Shang and J. Shi, "Methods for Long- Distance Crack Location and Detection of Concrete Bridge Structures," 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 2018, pp. 576-580, doi: 10.1109/ICIVC.2018.8492764. [7] H. Cho, H. Yoon and J. Jung, "Image-Based Crack Detection Using Crack Width Transform (CWT) Algorithm," in IEEE Access, vol. 6, pp. 60100-60114, 2018, doi: 10.1109/ACCESS.2018.2875889. [8] M. Lin, R. Zhou, Q. Yan and X. Xu, "Automatic Pavement Crack Detection Using HMRF-EM Algorithm," 2019 International Conference on Computer, Information and Telecommunication Systems (CITS), 2019, pp. 1-5, doi: 10.1109/CITS.2019.8862067. [9] H. Rowshandel, G. L. Nicholson, C. L. Davis and C. Roberts, "A combined threshold and signature match method for the automatic detection of rail RCF cracks using an ACFM sensor," 6th IET Conference on Railway Condition Monitoring (RCM 2014), 2014, pp. 1-6, doi: 10.1049/cp.2014.0998. [10] Jingyi Li, Ning Wang, Ying Liu and Yuemei Yang, "A Study of Crack Detection Algorithm," 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015, pp. 1184-1187, doi: 10.1109/IMCCC.2015.254. [11] Y. Quan, J. Sun, Y. Zhang and H. Zhang, "The Method of the Road Surface Crack Detection by the Improved Otsu Threshold," 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 2019, pp. 1615-1620, doi: 10.1109/ICMA.2019.8816422. [12] W. Li, M. Zhang, Z. Shen, W. Hu and P. Li, "Track Crack Detection Method in Complex Environment," 2018 11th International Symposium on Computational Intelligence and Design (ISCID), 2018, pp. 356-359, doi: 10.1109/ISCID.2018.00087. [13] J. H. Han, Y. C. Cho, H. G. Lee, H. S. Yang, W. J. Jeong and Y. S. Moon, "Crack Detection Method on Surface of Tunnel Lining," 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2019, pp. 1-3, doi: 10.1109/ITC-CSCC.2019.8793450.
