scholarly journals Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3973 ◽  
Author(s):  
Dan Xue ◽  
Weiqi Yuan
Keyword(s):  
Crack Detection ◽  
Gaussian Model ◽  
Detection Algorithm ◽  
Liaoning Province ◽  
Qualitative And Quantitative Analysis ◽  
Optimal Parameters ◽  
Multi Scale ◽  
Crack Morphology ◽  
Crack Widths ◽  
Partition Boundary

When detecting the cracks in the tunnel lining image, due to uneven illumination, there are generally differences in brightness and contrast between the cracked pixels and the surrounding background pixels as well as differences in the widths of the cracked pixels, which bring difficulty in detecting and extracting cracks. Therefore, this paper proposes a dynamic partitioned Gaussian crack detection algorithm based on the projection curve distribution. First, according to the distribution of the image projection curve, the background pixels are dynamically partitioned. Second, a new dynamic partitioned Gaussian (DPG) model was established, and the set rules of partition boundary conditions, partition number, and partition corresponding threshold were defined. Then, the threshold and multi-scale Gaussian factors corresponding to different crack widths were substituted into the Gaussian model to detect cracks. Finally, crack morphology and the breakpoint connection algorithm were combined to complete the crack extraction. The algorithm was tested on the lining gallery captured on the site of the Tang-Ling-Shan Tunnel in Liaoning Province, China. The optimal parameters in the algorithm were estimated through the Recall, Precision, and Time curves. From two aspects of qualitative and quantitative analysis, the experimental results demonstrate that this algorithm could effectively eliminate the effect of uneven illumination on crack detection. After detection, Recall could reach more than 96%, and after extraction, Precision was increased by more than 70%.

scholarly journals Modified Crack Detection of Sewer Conduit with Low-Resolution Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2263
Author(s):  
Byung Jik Son ◽  
Taejun Cho
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Threshold Value ◽  
Optimal Threshold ◽  
Optimal Parameters ◽  
Low Resolution ◽  
The Subject ◽  
Low Resolution Images

Imaging devices of less than 300,000 pixels are mostly used for sewage conduit exploration due to the petty nature of the survey industry in Korea. Particularly, devices of less than 100,000 pixels are still widely used, and the environment for image processing is very dim. Since the sewage conduit images covered in this study have a very low resolution (240 × 320 = 76,800 pixels), it is very difficult to detect cracks. Because most of the resolutions of the sewer conduit images are very low in Korea, this problem of low resolution was selected as the subject of this study. Cracks were detected through a total of six steps of improving the crack in Step 2, finding the optimal threshold value in Step 3, and applying an algorithm to detect cracks in Step 5. Cracks were effectively detected by the optimal parameters in Steps 2 and 3 and the user algorithm in Step 5. Despite the very low resolution, the cracked images showed a 96.4% accuracy of detection, and the non-cracked images showed 94.5% accuracy. Moreover, the analysis was excellent in quality. It is believed that the findings of this study can be effectively used for crack detection with low-resolution images.

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.

Multi-scale Object Detection Algorithm in Smart City Based on Mixed Dilated Convolution Pyramid

2021 ◽  
Author(s):  
Kangning Yin ◽  
Jie Liang ◽  
Shaoqi Hou ◽  
Rui Zhu ◽  
Guangqiang Yin ◽  
...  
Keyword(s):  
Object Detection ◽  
Smart City ◽  
Detection Algorithm ◽  
Multi Scale ◽  
Dilated Convolution

Fatigue Crack Propagation Behavior and Degradation Characteristics of STS316L by Nondestructive Evaluation

2005 ◽  
Vol 297-300 ◽  
pp. 2016-2021
Author(s):  
So Soon Park ◽  
Seok Hwan Ahn ◽  
Chang Kwon Moon ◽  
Ki Woo Nam
Keyword(s):  
Crack Detection ◽  
New Technology ◽  
Signal Amplitude ◽  
Detection Algorithm ◽  
Source Location ◽  
Patch Type ◽  
Fatigue Crack Propagation Behavior ◽  
Existing Structures ◽  
Propagation Behavior ◽  
Evaluation Algorithm

Structural health monitoring (SHM) is a new technology that has been increasingly evaluated by the industry as a potential approach to improve the cost and ease of structural inspection. Piezoelectric smart active layer (SAL) sensor was fabricated to verify the applicability of finding cracks and conducting source location in a various materials. A crack detection and source location works were done in three kinds of test condition such as aluminum plates with crack for patch type SAL sensor, a smart airplane with embedding SAL sensor, and a concrete beam with real crack for practical application. From this experimental study, the evaluation algorithm for the arrival time delay and decrease of signal amplitude was suggested in this paper. Consequently, it was found that the SAL sensor and detection algorithm developed in this study can be effectively used to detect and monitor damages in the both existing structures and new designed smart structures.

scholarly journals Pedestrian detection algorithm based on improved muti-scale feature fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012008
Author(s):  
Hui Liu ◽  
Keyang Cheng
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Data Sets ◽  
False Detection ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

Morphological edge detection algorithm based on hierarchical multi-scale

2021 ◽  
Author(s):  
Qi Tang ◽  
Yi-xuan Sun ◽  
Wen-tian Wang ◽  
Yi-zhou Jing ◽  
Chun-yan Li
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Multi Scale
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