Development of a Concrete Floating and Delamination Detection System Using Infrared Thermography

Dang

et al. 2023

Machine learning models have been developed to perform damage detection using images to improve bridge inspection efficiency. However, in damage detection using images alone, the 3D coordinates of the damage cannot be recorded. Furthermore, the accuracy of the detection depends on the quality of the images. This paper proposes a method to integrate and record the damage detected from multiple images into a 3D model using deep learning to detect the damage from bridge images and structure from motion to identify the shooting position. The proposed method reduces the variability of the detection results between images and can assess the scale of damage or, conversely, where there is no damage and the extent of inspection omissions. The proposed method has been applied to a real bridge, and it has been shown that the actual damage locations can be recorded as a 3D model.

Section: Resultsmentioning

confidence: 99%

Recording of bridge damage areas by 3D integration of multiple images and reduction of the variability in detected results

Yamane

Dang

et al. 2023

“…Object detection is also used for various purposes in civil engineering. For example, there are studies on detecting vehicles, pedestrians, and cyclists for autonomous driving (Carranza-García et al, 2022); detecting workers and heavy equipment for work zone safety (Shen et al, 2021); detecting external building objects to improve the accuracy and completeness of bridge information modeling (BIM) data (Ying et al, 2022); detecting the floating part from infrared images (Chun & Hayashi, 2021); detecting damaged parts from a 3D bridge model (Yamane et al, 2022(Yamane et al, , 2023; detecting material types of object surfaces for effective disinfection by robots (Hu & Li, 2022); and detecting unexploded ordnance materials in shallow waters to remove potentially dangerous objects (Foresti & Scagnetto, 2022).…”

Section: Introductionmentioning

confidence: 99%

Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground‐penetrating radar

Kato²

2023

Ground‐penetrating radar (GPR) is widely used to determine the location of buried pipes without excavation, and machine learning has been researched to automatically identify the location of buried pipes from the reflected wave images obtained by GPR. In object detection using machine learning, the accuracy of detection is affected by the quantity and quality of training data, so it is important to expand the training data to improve accuracy. This is especially true in the case of buried pipes that are located underground and whose existence cannot be easily confirmed. Therefore, this study developed a method for increasing training data using you only look once v5 (YOLOv5) and StyleGAN2‐ADA to automate the annotation process. Of particular importance is developing a framework for generating images by generative adversarial networks with an emphasis on images that are challenging to detect buried pipes in YOLOv5 and add them to a training dataset to repeat training recursively, which has greatly improved the detection accuracy. Specifically, F‐values of 0.915, 0.916, and 0.924 were achieved by automatically generating training images step by step from only 500, 1000, and 2000 training images, respectively. These values exceed the F‐value of 0.900, which is obtained from training by manually annotating 15,000 images, a much larger number. In addition, we applied the method to a road in Shizuoka Prefecture, Japan, and confirmed that the method can detect the location of buried pipes with high accuracy on a real road. This method can contribute to labor‐saving training data expansion, which is time‐consuming and costly in practice, and as a result, the method contributes to improving detection accuracy.

“…Liu et al, 2019;Miao et al, 2021;Sajedi & Liang, 2021; and asphalt pavements (F. Guo et al, 2021;Li et al, 2020;Maeda et al, 2018;Maeda et al, 2021) and in the detection of defects and leaks in tunnels (Huang et al, 2018;Xue & Li, 2018). Further, research has been conducted on evaluating the soundness of structures using vibration (Avci et al, 2021;Gutierrez Soto & Adeli, 2019;, 2018aShrestha & Dang, 2020) and on assessing the internal damage to concrete using nondestructive testing (Chun & Hayashi 2021;Chun, Ujike, et al, 2020;Erdal et al, 2018;Sirca et al, 2018). In addition, research has been conducted on the evaluation of the mechanical performance of corroded steel members (Chun et al, 2019;Karina et al, 2017;Luo et al, 2021;Wu et al, 2021;S.…”

Section: Introductionmentioning

confidence: 99%

“…Notable studies include research on the detection of cracks in concrete (Chun et al., 2021; Deng et al., 2020; Dung, 2019; Jang et al., 2021; Jiang & Zhang, 2020; Kong et al., 2021; Lee et al., 2019; Liu et al., 2020; Y. Liu et al., 2019; Miao et al., 2021; Sajedi & Liang, 2021; Yamane & Chun, 2020) and asphalt pavements (F. Guo et al., 2021; Li et al., 2020; J. Liu et al., 2020; Maeda et al., 2018; Maeda et al., 2021) and in the detection of defects and leaks in tunnels (Huang et al., 2018; Xue & Li, 2018). Further, research has been conducted on evaluating the soundness of structures using vibration (Avci et al., 2021; Chun, Yamane, et al., 2020; Gutierrez Soto & Adeli, 2019; Luo et al., 2020; Rafiei & Adeli, 2017, 2018a; Shrestha & Dang, 2020) and on assessing the internal damage to concrete using nondestructive testing (Chun & Hayashi 2021; Chun, Ujike, et al., 2020; Erdal et al., 2018; Sirca et al., 2018). In addition, research has been conducted on the evaluation of the mechanical performance of corroded steel members (Chun et al., 2019; Karina et al., 2017; Luo et al., 2021; Wu et al., 2021; S. Xu et al., 2019; J. Xu et al., 2020), material and mechanical properties of concrete structures (Feng et al., 2020; Le & Le, 2021; Okazaki et al., 2020; Rafiei et al., 2017), and seismic resistance and post‐earthquake disaster health (Liang, 2019; Nagatani et al., 2021; Perez‐Ramirez et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

A deep learning‐based image captioning method to automatically generate comprehensive explanations of bridge damage

Yamane

Maemura

2021

Photographs of bridges can reveal considerable technical information such as the part of the structure that is damaged and the type of damage. Maintenance and inspection engineers can benefit greatly from a technology that can automatically extract and express such information in readable sentences. This is possibly the first study on developing a deep learning model that can generate sentences describing the damage condition of a bridge from images through an image captioning method. Our study shows that by introducing an attention mechanism into the deep learning model, highly accurate descriptive sentences can be generated. In addition, often multiple forms of damage can be observed in the images of bridges; hence, our algorithm is adapted to output multiple sentences to provide a comprehensive interpretation of complex images. In our dataset, the scores of Bilingual Evaluation Understudy (BLEU)-1 to BLEU-4 were 0.782, 0.749, 0.711, and 0.693, respectively, and the percentage of correctly output explanatory sentences is 69.3%. All of these results are better than the model without the attention mechanism. The developed method makes it possible to provide user-friendly, text-based explanations of bridge damage in images, making it easier for engineers with relatively little experience and even administrative staff without extensive technical expertise to understand images of bridge damage. Future research in this field is expected to lead to the unification of field expertise with artificial intelligence (AI), which will be the foundation of the evolutionary development of bridge inspection AI.