Multi dimensional disease intelligent detection device for underwater pier column structure through machine learning

Yang, Bikai; Han, Bing; Zhao, Haiyang; Gu, Tianshu; Cai, Tianxiao

doi:10.1109/eebda53927.2022.9744889

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Te ACE algorithm is mainly divided into two steps. Te frst step is to adjust the image domain: substitute the pixel brightness value of the original underwater image I z into formula (2), and the intermediate image…”

Section: Automatic Color Equalization Algorithmmentioning

confidence: 99%

“…Te explosive growth of old bridges shows that China's bridges have generally entered into a rapid aging stage. During the service process of bridge underwater pilepier structure, it is constantly afected by several factors, such as current scouring, ship collision, and wave force, which often lead to the appearance of various surface defects, such as cracks, exposed reinforcements, holes, and swellings [2]. With the accumulation of defects, bridge collapse accidents will frequently occur in case surface defects of the bridge underwater pile-pier structure are not detected and found in time.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Surface Defects on Underwater Pile-Pier of Bridges Based on Image Fusion and Deep Learning

Jiang

Wang

et al. 2023

Structural Control and Health Monitoring

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As an important part of the bridge structure system, the underwater pile-pier structure usually occurs various defects on its surfaces due to its complex hydrological environment. The existing conventional defect detection approaches exist two aspects of problems: (1) insufficient definition and color distortion of the underwater images, and (2) low efficiency and error-prone. To solve these problems, this paper proposed the target defect detection model by integrating the image-fusion enhancement algorithm and the deep learning algorithm. Firstly, by analyzing the reasons for the degradation of the underwater images, the ACE (automatic color equalization) and CLAHE (contrast limited adaptive histogram equalization) algorithms are selected to enhance the image, respectively. Secondly, the two enhanced images are fused based on the point sharpness weight, and then the fusion results are further sharpened by the USM (unsharp mask) algorithm, thus obtaining the final fused images. Thirdly, 3,200 fused images are taken as the training set, by adopting the YOLOv3 algorithm to train the detection model, and then the training model is validated and tested by the other each 400 fused images, thus building up the target automatic detection model of underwater pile-pier surface defects. Finally, a series of comparison and discussion were conducted to validate the effectiveness of image-fusion and the robustness and effectiveness of the target detection model. The results found that the target detection model has excellent robustness against noise and effectiveness in the surface defect detection. This indicates that the image-fusion approach proposed in this paper can effectively enhance the image features, and the target detection model is feasible, robust, and effective in the automatic detection of surface defects on underwater pile-pier structures.

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Section: Automatic Color Equalization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Surface Defects on Underwater Pile-Pier of Bridges Based on Image Fusion and Deep Learning

Jiang

Wang

et al. 2023

Structural Control and Health Monitoring

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Two‐step rapid inspection of underwater concrete bridge structures combining sonar, camera, and deep learning

Sun,

Hou,

et al. 2024

Computer aided Civil Eng

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Underwater defects in piers pose potential hazards to the safety and durability of river‐crossing bridges. The concealment and difficulty in detecting underwater defects often result in their oversight. Acoustic methods face challenges in directly achieving accurate measurements of underwater defects, while optical methods are time‐consuming. This study proposes a two‐step rapid inspection method for underwater concrete bridge piers by combining acoustics and optics. The first step combines macroscopic sonar scanning with an enhanced YOLOv7 to detect and locate piers and defects. Second, the camera approaches the defects for image acquisition, and an enhanced DeepLabv3+ is used for defect identification. The results demonstrate an average mean average precision@0.5 of 95.10% for defect and pier detection, and an mean intersection over union of 0.914 for exposed reinforcement and spalling identification. The method was applied to a real river‐crossing bridge and reduced inspection time by 51.2% compared to traditional methods for assessing a row of 11 piers.

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Multi dimensional disease intelligent detection device for underwater pier column structure through machine learning

Cited by 2 publications

References 2 publications

Automatic Detection of Surface Defects on Underwater Pile-Pier of Bridges Based on Image Fusion and Deep Learning

Automatic Detection of Surface Defects on Underwater Pile-Pier of Bridges Based on Image Fusion and Deep Learning

Two‐step rapid inspection of underwater concrete bridge structures combining sonar, camera, and deep learning

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