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Relevant bibliographies by topics / Backbone Extraction

Academic literature on the topic 'Backbone Extraction'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Backbone Extraction"

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Neal, Zachary P. "backbone: An R package to extract network backbones." PLOS ONE 17, no. 5 (May 31, 2022): e0269137. http://dx.doi.org/10.1371/journal.pone.0269137.

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Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.

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Liu, Yudong, Yongtao Wang, Siwei Wang, Tingting Liang, Qijie Zhao, Zhi Tang, and Haibin Ling. "CBNet: A Novel Composite Backbone Network Architecture for Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11653–60. http://dx.doi.org/10.1609/aaai.v34i07.6834.

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In existing CNN based detectors, the backbone network is a very important component for basic feature1 extraction, and the performance of the detectors highly depends on it. In this paper, we aim to achieve better detection performance by building a more powerful backbone from existing ones like ResNet and ResNeXt. Specifically, we propose a novel strategy for assembling multiple identical backbones by composite connections between the adjacent backbones, to form a more powerful backbone named Composite Backbone Network (CBNet). In this way, CBNet iteratively feeds the output features of the previous backbone, namely high-level features, as part of input features to the succeeding backbone, in a stage-by-stage fashion, and finally the feature maps of the last backbone (named Lead Backbone) are used for object detection. We show that CBNet can be very easily integrated into most state-of-the-art detectors and significantly improve their performances. For example, it boosts the mAP of FPN, Mask R-CNN and Cascade R-CNN on the COCO dataset by about 1.5 to 3.0 points. Moreover, experimental results show that the instance segmentation results can be improved as well. Specifically, by simply integrating the proposed CBNet into the baseline detector Cascade Mask R-CNN, we achieve a new state-of-the-art result on COCO dataset (mAP of 53.3) with a single model, which demonstrates great effectiveness of the proposed CBNet architecture. Code will be made available at https://github.com/PKUbahuangliuhe/CBNet.

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Gomes Ferreira, Carlos Henrique, Fabricio Murai, Ana P. C. Silva, Martino Trevisan, Luca Vassio, Idilio Drago, Marco Mellia, and Jussara M. Almeida. "On network backbone extraction for modeling online collective behavior." PLOS ONE 17, no. 9 (September 15, 2022): e0274218. http://dx.doi.org/10.1371/journal.pone.0274218.

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Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.

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Brauckhoff, Daniela, Xenofontas Dimitropoulos, Arno Wagner, and Kavé Salamatian. "Anomaly Extraction in Backbone Networks Using Association Rules." IEEE/ACM Transactions on Networking 20, no. 6 (December 2012): 1788–99. http://dx.doi.org/10.1109/tnet.2012.2187306.

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Cao, Jie, Cuiling Ding, and Benyun Shi. "Motif-based functional backbone extraction of complex networks." Physica A: Statistical Mechanics and its Applications 526 (July 2019): 121123. http://dx.doi.org/10.1016/j.physa.2019.121123.

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Dai, Liang, Ben Derudder, and Xingjian Liu. "Transport network backbone extraction: A comparison of techniques." Journal of Transport Geography 69 (May 2018): 271–81. http://dx.doi.org/10.1016/j.jtrangeo.2018.05.012.

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Yuan, Hanning, Yanni Han, Ning Cai, and Wei An. "A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential." Complexity 2018 (October 22, 2018): 1–8. http://dx.doi.org/10.1155/2018/8604132.

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Inspired by the theory of physics field, in this paper, we propose a novel backbone network compression algorithm based on topology potential. With consideration of the network connectivity and backbone compression precision, the method is flexible and efficient according to various network characteristics. Meanwhile, we define a metric named compression ratio to evaluate the performance of backbone networks, which provides an optimal extraction granularity based on the contributions of degree number and topology connectivity. We apply our method to the public available Internet AS network and Hep-th network, which are the public datasets in the field of complex network analysis. Furthermore, we compare the obtained results with the metrics of precision ratio and recall ratio. All these results show that our algorithm is superior to the compared methods. Moreover, we investigate the characteristics in terms of degree distribution and self-similarity of the extracted backbone. It is proven that the compressed backbone network has a lot of similarity properties to the original network in terms of power-law exponent.

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Qian, Liqiang, Zhan Bu, Mei Lu, Jie Cao, and Zhiang Wu. "Extracting Backbones from Weighted Complex Networks with Incomplete Information." Abstract and Applied Analysis 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/105385.

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The backbone is the natural abstraction of a complex network, which can help people understand a networked system in a more simplified form. Traditional backbone extraction methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency—the exhaustive search of all nodes or edges is often prohibitively expensive. In this paper, we propose a backbone extraction heuristic with incomplete information (BEHwII) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way, which only relies on the local/incomplete knowledge rather than the global view of the network. Experimental results on four real-life networks demonstrate the advantage of BEHwII over the classic disparity filter method by either effectiveness or efficiency validity.

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Li, Decai, and Xingguo Jiang. "Kinship Verification Method of Face Image Deep Feature Fusion." Academic Journal of Science and Technology 5, no. 1 (February 28, 2023): 57–62. http://dx.doi.org/10.54097/ajst.v5i1.5348.

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Kinship verification is an important and challenging problem in computer vision. How to extract discriminative features is the key to improve the accuracy of kinship verification. At present, convolutional neural networks (CNNs) for feature extraction in the field of computer vision has achieved remarkable success, making it the most scholars used to study kinship verification related issues. However, few people use the self-attention mechanism with global capture capability to build a backbone feature classification network. Therefore, this paper proposes a backbone feature extraction network model based on a non-convolution, which expands the selection range of traditional classification networks for kinship verification related issues. Specifically, the paper proposes to use Vision Transformers as the basic backbone feature extraction network, combined with CNN with local attention mechanism, to provide a unique integrated solution in kinship verification. The proposed GLANet model is used for kinship verification and can verify 11 kinship pairs. The final experimental results show that in the FIW dataset, compared with the RFIW2020 challenge leading method, the proposed method has better verification effect in kinship, and the accuracy rate can reach 79.6 %.

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Song, Huina, Han Wu, Jianhua Huang, Hua Zhong, Meilin He, Mingkun Su, Gaohang Yu, Mengyuan Wang, and Jianwu Zhang. "HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images." Electronics 11, no. 22 (November 17, 2022): 3787. http://dx.doi.org/10.3390/electronics11223787.

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Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively.

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Dissertations / Theses on the topic "Backbone Extraction"

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Hotchko, Matthew John. "Automated extraction of backbone deuteration levels from amide H/²H mass spectrometry experiments to understand EX1 protein unfolding and determine protein binding interfaces /." Diss., Connect to a 24 p. preview or request complete full text in PDF formate. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3244176.

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Book chapters on the topic "Backbone Extraction"

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Rajeh, Stephany, Marinette Savonnet, Eric Leclercq, and Hocine Cherifi. "Modularity-Based Backbone Extraction in Weighted Complex Networks." In Network Science, 67–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97240-0_6.

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Londono, Julian M., Simon A. Neild, and Jonathan E. Cooper. "Systems with Bilinear Stiffness: Extraction of Backbone Curves and Identification." In Nonlinear Dynamics, Volume 1, 307–13. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-15221-9_27.

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Xiao, Liang, Xiaosong Yuan, Zack Galbreath, and Badrinath Roysam. "Automatic and Reliable Extraction of Dendrite Backbone from Optical Microscopy Images." In Lecture Notes in Computer Science, 100–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15615-1_13.

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Ghalmane, Zakariya, Mohamed-El-Amine Brahmia, Mourad Zghal, and Hocine Cherifi. "A Stochastic Approach for Extracting Community-Based Backbones." In Complex Networks and Their Applications XI, 55–67. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21131-7_5.

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Huynh, Hoai Nguyen, and Roshini Selvakumar. "Extracting Backbone Structure of a Road Network from Raw Data." In Lecture Notes in Computer Science, 582–94. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50371-0_43.

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Xing, Lizhi, and Yu Han. "Extracting the Backbone of Global Value Chain from High-Dimensional Inter-Country Input-Output Network." In Complex Networks & Their Applications IX, 559–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65351-4_45.

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Sharma, Dilip Kumar, and A. K. Sharma. "Search Engine." In The Dark Web, 359–74. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3163-0.ch016.

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ICT plays a vital role in human development through information extraction and includes computer networks and telecommunication networks. One of the important modules of ICT is computer networks, which are the backbone of the World Wide Web (WWW). Search engines are computer programs that browse and extract information from the WWW in a systematic and automatic manner. This paper examines the three main components of search engines: Extractor, a web crawler which starts with a URL; Analyzer, an indexer that processes words on the web page and stores the resulting index in a database; and Interface Generator, a query handler that understands the need and preferences of the user. This paper concentrates on the information available on the surface web through general web pages and the hidden information behind the query interface, called deep web. This paper emphasizes the Extraction of relevant information to generate the preferred content for the user as the first result of his or her search query. This paper discusses the aspect of deep web with analysis of a few existing deep web search engines.

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Sharma, Dilip Kumar, and A. K. Sharma. "Search Engine." In ICT Influences on Human Development, Interaction, and Collaboration, 117–31. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1957-9.ch006.

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ICT plays a vital role in human development through information extraction and includes computer networks and telecommunication networks. One of the important modules of ICT is computer networks, which are the backbone of the World Wide Web (WWW). Search engines are computer programs that browse and extract information from the WWW in a systematic and automatic manner. This paper examines the three main components of search engines: Extractor, a web crawler which starts with a URL; Analyzer, an indexer that processes words on the web page and stores the resulting index in a database; and Interface Generator, a query handler that understands the need and preferences of the user. This paper concentrates on the information available on the surface web through general web pages and the hidden information behind the query interface, called deep web. This paper emphasizes the Extraction of relevant information to generate the preferred content for the user as the first result of his or her search query. This paper discusses the aspect of deep web with analysis of a few existing deep web search engines.

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Das, Rik, S. N. Singh, Mahua Banerjee, Shishir Mayank, and T. Venkata Shashank. "An Integrated Framework for Information Identification With Image Data Using Multi-Technique Feature Extraction." In Feature Dimension Reduction for Content-Based Image Identification, 1–25. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5775-3.ch001.

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Image data has portrayed immense potential as a resourceful foundation of information in current context for numerous applications including biomedicine, military, commerce, education, and web image classification and searching. The scenario has kindled the requirement for efficient content-based image identification from the archived image databases in varied industrial and educational sectors. Feature extraction has acted as the backbone to govern the success rate of content-based information identification with image data. The chapter has presented two different techniques of feature extraction from images based on image binarization and morphological operators. The multi-technique extraction with radically reduced feature size was imperative to explore the rich set of feature content in an image. The objective of this work has been to create a fusion framework for image recognition by means of late fusion with data standardization. The work has implemented a hybrid framework for query classification as a precursor for image retrieval which has been so far the first of its kind.

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Zhou, Qian, Fuxin Sun, and Junyou Zhang. "Research on Multi-Target Detection and Tracking Algorithm Based on Improved YOLOv5." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221115.

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A detection and tracking algorithm based on improved YOLOv5 is proposed for the poor recognition and tracking of obscured targets and small-sized targets. The K-means ++ algorithm is used to cluster to obtain new anchor values; the CIOU-NMS is introduced to improve the missed detection problem when the target is obscured; the CBAM is proposed to be embedded into the Backbone and Neck part to improve the feature extraction capability of the algorithm for small targets. DeepSORT is chosen as the multi-target tracker to plot the motion trajectory of the target in real time. The experimental results show that the improved algorithm has a 2.1% improvement in detection accuracy and a detection speed of 32.32/s, satisfying real-time efficient detection with better tracking.

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Conference papers on the topic "Backbone Extraction"

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Brauckhoff, Daniela, Xenofontas Dimitropoulos, Arno Wagner, and Kavè Salamatian. "Anomaly extraction in backbone networks using association rules." In the 9th ACM SIGCOMM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1644893.1644897.

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Trinh, Quoc-Huy, and Minh-Van Nguyen. "Dense-Res Net for Endoscopic Image Classification." In 2nd International Conference on Machine Learning &Trends (MLT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111108.

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We propose a method that configures Fine-tuning to a combination of backbone DenseNet and ResNet to classify eight classes showing anatomical landmarks, pathological findings, to endoscopic procedures in the GI tract. Our Technique depends on Transfer Learning which combines two backbones, DenseNet 121 and ResNet 101, to improve the performance of Feature Extraction for classifying the target class. After experiment and evaluating our work, we get accuracy with an F1 score of approximately 0.93 while training 80000 and test 4000 images.

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Bu, Zhan, Zhiang Wu, Liqiang Qian, Jie Cao, and Guandong Xu. "A backbone extraction method with Local Search for complex weighted networks." In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2014. http://dx.doi.org/10.1109/asonam.2014.6921564.

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Dzikovska, Myroslava O., and Carolyn P. Rosé. "Backbone extraction and pruning for speeding up a deep parser for dialogue systems." In the Third Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1621459.1621462.

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Yin, Meijuan, Dong Yao, Junyong Luo, Xiaonan Liu, and Jing Ma. "Network backbone anomaly detection using double random forests based on non-extensive entropy feature extraction." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6817948.

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Al-Shudeifat, Mohammad A. "Approximation of the Frequency-Energy Dependence in the Nonlinear Dynamical Systems." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60163.

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In this work, a method is introduced for extracting the approximate backbone branches of the frequency-energy plot from the numerical simulation response of the nonlinear dynamical system. The duffing oscillator is firstly considered to describe the method and later a linear oscillator (LO) coupled with a nonlinear energy sink (NES) is also considered for further demonstration. The systems of concern are numerically simulated at an arbitrary high level of initial input energy. Accordingly, the obtained responses of these systems are employed via the proposed method to extract an approximation for the fundamental backbone branches of the frequency-energy plot. The obtained backbones have been found in excellent agreement with the exact backbones of the considered systems. Even though these approximate backbones have been obtained for only one high energy level, they are still valid for any other initial energy below that level. In addition, they are not affected by the damping variations in the considered systems. Unlike other existing methods, the proposed approach is applicable to well-approximate the backbone branches of the large-scale nonlinear dynamical systems.

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Akbari, Nasrin, and Amirali Baniasadi. "LEON: Light Weight Edge Detection Network." In 10th International Conference on Computer Networks & Communications (CCNET 2023). Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130402.

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Deep Convolutional Neural Networks (CNNs) have achieved human-level performance in edge detection. However, there have not been enough studies on how to efficiently utilize the parameters of the neural network in edge detection applications. Therefore, the associated memory and energy costs remain high. In this paper, inspired by Depthwise Separable Convolutions and deformable convolutional networks (Deformable-ConvNet), we aim to address current inefficiencies in edge detection applications. To this end, we propose a new architecture, which we refer to as Lightweight Edge Detection Network (LEON ). The proposed approach is designed to integrate the advantages of the deformable unit and DepthWise Separable convolutions architecture to create a lightweight backbone employed for efficient feature extraction. As we show, we achieve state-of-the-art accuracy while significantly reducing the complexity by carefully choosing proper components for edge detection purposes. Our results on BSDS500 and NYUDv2 demonstrate that LEON outperforms the current lightweight edge detectors while requiring only 500k parameters. It is worth mentioning that we train the network from scratch without using pre- trained weights.

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Cohen, Nadav, and Izhak Bucher. "The Dynamics of a Bi-Stable Energy Harvester: Exploration via Slow-Fast Decomposition and Analytical Modeling." In ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-83013.

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The paper discusses the advantages of the bi-stable energy harvester over linear oscillators in the low frequency excitation regime. When excited by low-frequency base motions, a bistable vibration-based energy harvester’s response is characterized by a combination of a slow, and a non-stationary fast component. By decomposing the response of the bi-stable system into fast and slow components, some new physical insights into the dynamical properties of the system are obtained. Properties such as mechanical frequency up-conversion, asymmetry in the bi-stable potential of the system and extraction of the backbone curve are explored. The proposed decomposition is demonstrated and explained via numerical and experimental results. A simple, approximate analytical model, for the bi-stable oscillator is proposed and its ability to detect migration towards different vibration regimes is illustrated. An expression for the power output of the harvester is derived from the analytical solution allowing us to tune the bi-stable potential towards optimum performance. The analytical model sheds light on the occurrences of bifurcations in the response of such nonlinear systems and on the optimal values of potential barrier vs. excitation levels.

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Zhang, Mingjin, Chengyu He, Jing Zhang, Yuxiang Yang, Xiaoqi Peng, and Jie Guo. "SAR-to-Optical Image Translation via Neural Partial Differential Equations." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/229.

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Synthetic Aperture Radar (SAR) becomes prevailing in remote sensing while SAR images are challenging to interpret by human visual perception due to the active imaging mechanism and speckle noise. Recent researches on SAR-to-optical image translation provide a promising solution and have attracted increasing attentions, though still suffering from low optical image quality with geometric distortion due to the large domain gap. In this paper, we mitigate this issue from a novel perspective, i.e., neural partial differential equations (PDE). First, based on the efficient numerical scheme for solving PDE, i.e., Taylor Central Difference (TCD), we devise a basic TCD residual block to build the backbone network, which promotes the extraction of useful information in SAR images by aggregating and enhancing features from different levels. Furthermore, inspired by the Perona-Malik Diffusion (PMD), we devise a PMD neural module to implement feature diffusion through layers, aiming at removing the noises in smooth regions while preserving the geometric structures. Assembling them together, we propose a novel SAR-to-Optical image translation network named S2O-NPDE, which delivers optical images with finer structures and less noise while enjoying an explainability advantage from explicit mathematical derivation. Experiments on the popular SEN1-2 dataset show that our model outperforms state-of-the-art methods in terms of both objective metrics and visual quality.

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AHMED, HABIB, HUNG MANH LA, and ALIREZA TAVAKOLLI. "USE OF DEEP ENCODER-DECODER NETWORK FOR SUB-SURFACE INSPECTION AND EVALUATION OF BRIDGE DECKS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36334.

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The automation of various processes underlying maintenance and inspection of bridges using different robots have gained considerable attention in recent literature. For the development of effective methods to automate existing manual processes, a number of different solutions have been proposed. In this paper, the automation of rebar detection and localization will be discussed, which is one of the process for sub-surface health inspection of bridges. This study explores the utilization of Deep Encoder- Decoder Networks for the segmentation of GPR data in the form of B-scan images to extract parabolic rebar profiles. This research area is problematic, as the B-scan image data is fraught with noise, signal reflection and other artefacts that hinder the effective extraction of these rebar profiles. The data is collected in this study using Ground Penetrating Radar (GPR) sensor, which is employed in this study consist of data from 8 different bridges from different parts of the United States. A “leave-one-out” approach was used for the training and validation of the performance of the proposed system; the data from seven bridges was used for training and validation was performed on the remaining single bridge data. A number of different encoder modules have been trained and evaluated using SegNet as the backbone architecture. The performance of the proposed rebar detection and localization system has been evaluated in terms of different qualitative and quantitative metrics. On average, for the different encoder modules, the mean intersection-over-union (mIOU) values range between 60%-70%. The qualitative examination has highlighted the level of similarity between the ground truth and outputs from the different encoder modules within the SegNet framework.

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