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1. Deep Learning-Based Crack Detection Using Mask R-CNN Technique

   Tan, Chenjun; Uddin, Nasim; Mohammed, Yahya M. (August 2019, 9 th International Conference on Structural Health Monitoring of Intelligent Infrastructure)

   Cracks of civil infrastructures, including bridges, dams, roads, and skyscrapers, potentially reduce local stiffness and cause material discontinuities, so as to lose their designed functions and threaten public safety. This inevitable process signifier urgent maintenance issues. Early detection can take preventive measures to prevent damage and possible failure. With the increasing size of image data, machine/deep learning based method have become an important branch in detecting cracks from images. This study is to build an automatic crack detector using the state-of-the-art technique referred to as Mask Regional Convolution Neural Network (R-CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object localization but also for object instance segmentation of natural images. It is found that the built crack detector is able to perform highly effective and efficient automatic segmentation of a wide range of images of cracks. In addition, this proposed automatic detector could work on videos as well; indicating that this detector based on Mask R-CNN provides a robust and feasible ability on detecting cracks exist and their shapes in real time on-site. 

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2. Concrete Crack Pixel Classification Using an Encoder Decoder Based Deep Learning Architecture

   https://doi.org/10.1007/978-3-030-33720-9_46  

   Billah, Umme Hafsa; Tavakkoli, Alireza; La, Hung Manh (October 2019, The 14th International Symposium on Visual Computing (ISVC))

   Civil infrastructure inspection in hazardous areas such as underwater beams, bridge decks, etc., is a perilous task. In addition, other factors like labor intensity, time, etc. influence the inspection of infrastructures. Recent studies [11] represent that, an autonomous inspection of civil infrastructure can eradicate most of the problems stemming from manual inspection. In this paper, we address the problem of detecting cracks in the concrete surface. Most of the recent crack detection techniques use deep architecture. However, finding the exact location of crack efficiently has been a difficult problem recently. Therefore, a deep architecture is proposed in this paper, to identify the exact location of cracks. Our architecture labels each pixel as crack or non-crack, which eliminates the need for using any existing post-processing techniques in the current literature [5,11]. Moreover, acquiring enough data for learning is another challenge in concrete defect detection. According to previous studies, only 10% of an image contains edge pixels (in our case defected areas) [31]. We proposed a robust data augmentation technique to alleviate the need for collecting more crack image samples. The experimental results show that, with our method, significant accuracy can be obtained with very less sample of data. Our proposed method also outperforms the existing methods of concrete crack classification. 

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3. Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection

   https://doi.org/10.3390/s20164403  

   Billah, Umme Hafsa; La, Hung Manh; Tavakkoli, Alireza (August 2020, Sensors)

   null (Ed.)

   An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we present a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation, while addressing the effect of gradient vanishing problem. A feature silencing module is incorporated in the proposed framework, capable of eliminating non-discriminative feature maps from the network to improve performance. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than the state-of-the-art crack detection architectures. 

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4. Machine-Supported Bridge Inspection Image Documentation Using Artificial Intelligence

   https://doi.org/10.1177/03611981221135803  

   Zhang, Xin; Eric Wogen, Benjamin; Chu, Zhiwei; Dyke, Shirley J.; Poston, Randall; Hacker, Thomas; Ramirez, Julio; Liu, Xiaoyu; Iturburu, Lissette; Baah, Prince; et al (January 2023, Transportation Research Record: Journal of the Transportation Research Board)

   The purpose of a routine bridge inspection is to assess the physical and functional condition of a bridge according to a regularly scheduled interval. The Federal Highway Administration (FHWA) requires these inspections to be conducted at least every 2 years. Inspectors use simple tools and visual inspection techniques to determine the conditions of both the elements of the bridge structure and the bridge overall. While in the field, the data is collected in the form of images and notes; after the field work is complete, inspectors need to generate a report based on these data to document their findings. The report generation process includes several tasks: (1) evaluating the condition rating of each bridge element according to FHWA Recording and Coding Guide for Structure Inventory and Appraisal of the Nation’s Bridges; and (2) updating and organizing the bridge inspection images for the report. Both of tasks are time-consuming. This study focuses on assisting with the latter task by developing an artificial intelligence (AI)-based method to rapidly organize bridge inspection images and generate a report. In this paper, an image organization schema based on the FHWA Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation’s Bridges and the Manual for Bridge Element Inspection is described, and several convolutional neural network-based classifiers are trained with real inspection images collected in the field. Additionally, exchangeable image file (EXIF) information is automatically extracted to organize inspection images according to their time stamp. Finally, the Automated Bridge Image Reporting Tool (ABIRT) is described as a browser-based system built on the trained classifiers. Inspectors can directly upload images to this tool and rapidly obtain organized images and associated inspection report with the support of a computer which has an internet connection. The authors provide recommendations to inspectors for gathering future images to make the best use of this tool. 

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5. Implementing mobile eye tracking in psychological research: A practical guide

   https://doi.org/10.3758/s13428-024-02473-6  

   Fu, Xiaoxue; Franchak, John_M; MacNeill, Leigha_A; Gunther, Kelley_E; Borjon, Jeremy_I; Yurkovic-Harding, Julia; Harding, Samuel; Bradshaw, Jessica; Pérez-Edgar, Koraly_E (August 2024, Behavior Research Methods)

   Abstract Eye tracking provides direct, temporally and spatially sensitive measures of eye gaze. It can capture visual attention patterns from infancy through adulthood. However, commonly used screen-based eye tracking (SET) paradigms are limited in their depiction of how individuals process information as they interact with the environment in “real life”. Mobile eye tracking (MET) records participant-perspective gaze in the context of active behavior. Recent technological developments in MET hardware enable researchers to capture egocentric vision as early as infancy and across the lifespan. However, challenges remain in MET data collection, processing, and analysis. The present paper aims to provide an introduction and practical guide to starting researchers in the field to facilitate the use of MET in psychological research with a wide range of age groups. First, we provide a general introduction to MET. Next, we briefly review MET studies in adults and children that provide new insights into attention and its roles in cognitive and socioemotional functioning. We then discuss technical issues relating to MET data collection and provide guidelines for data quality inspection, gaze annotations, data visualization, and statistical analyses. Lastly, we conclude by discussing the future directions of MET implementation. Open-source programs for MET data quality inspection, data visualization, and analysis are shared publicly. 

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