The non-contact acoustic inspection method for concrete structures using the defect detection algorithm based on the statistic evaluation for a healthy part of concrete

2016 ◽  
Vol 140 (4) ◽  
pp. 3212-3212 ◽  
Author(s):  
Kazuko Sugimoto ◽  
Tsuneyoshi Sugimoto ◽  
Nobuaki Kosuge ◽  
Chitose Kuroda ◽  
Noriyuki Utagawa
Keyword(s):  
Defect Detection ◽  
Concrete Structures ◽  
Detection Algorithm ◽  
Inspection Method ◽  
Healthy Part ◽  
Acoustic Inspection

Defect-detection algorithm for noncontact acoustic inspection using spectrum entropy

2015 ◽  
Vol 54 (7S1) ◽  
pp. 07HC15 ◽  
Author(s):  
Kazuko Sugimoto ◽  
Ryo Akamatsu ◽  
Tsuneyoshi Sugimoto ◽  
Noriyuki Utagawa ◽  
Chitose Kuroda ◽  
...  
Keyword(s):  
Defect Detection ◽  
Detection Algorithm ◽  
Acoustic Inspection

scholarly journals Study on the Non-contact Acoustic Inspection Method for Concrete Structures by using Strong Ultrasonic Sound source

2015 ◽  
Vol 70 ◽  
pp. 398-401 ◽  
Author(s):  
Tsuneyoshi Sugimoto ◽  
Itsuki Uechi ◽  
Kazuko Sugimoto ◽  
Noriyuki Utagawa ◽  
Kageyoshi Katakura
Keyword(s):  
Sound Source ◽  
Concrete Structures ◽  
Inspection Method ◽  
Ultrasonic Sound ◽  
Acoustic Inspection

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

scholarly journals Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

2021 ◽  
Vol 11 (2) ◽  
pp. 813
Author(s):  
Shuai Teng ◽  
Zongchao Liu ◽  
Gongfa Chen ◽  
Li Cheng
Keyword(s):  
Feature Extraction ◽  
Crack Detection ◽  
Computational Cost ◽  
Concrete Structures ◽  
Detection Algorithm ◽  
Computational Time ◽  
Image Size ◽  
Important Indicator ◽  
Feature Extractor ◽  
Model Training

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

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