scholarly journals Crack Identification of Drawing Parts Based on Loccal Wave Demomposition and Neural Network

2013 ◽  
Author(s):  
Zhigao Luo ◽  
Qiang Chen ◽  
Xin He
Keyword(s):  
Neural Network ◽  
Crack Identification

Crack Grid Detection and Calculation Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Liyang Xiao ◽  
Wei Li ◽  
Ju Huyan ◽  
Zhaoyun Sun ◽  
Susan Tighe
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Image Denoising ◽  
Crack Detection ◽  
Experimental Results ◽  
Crack Identification ◽  
Threshold Segmentation ◽  
Improve Image Quality

This paper aims to develop a method of crack grid detection based on convolutional neural network. First, an image denoising operation is conducted to improve image quality. Next, the processed images are divided into grids of different, and each grid is fed into a convolutional neural network for detection. The pieces of the grids with cracks are marked and then returned to the original images. Finally, on the basis of the detection results, threshold segmentation is performed only on the marked grids. Information about the crack parameters is obtained via pixel scanning and calculation, which realises complete crack detection. The experimental results show that 30×30 grids perform the best with the accuracy value of 97.33%. The advantage of automatic crack grid detection is that it can avoid fracture phenomenon in crack identification and ensure the integrity of cracks.

scholarly journals Pavement Crack Detection and Segmentation Method Based on Improved Deep Learning Fusion Model

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Xiaoran Feng ◽  
Liyang Xiao ◽  
Wei Li ◽  
Lili Pei ◽  
Zhaoyun Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Geometric Parameter ◽  
Crack Detection ◽  
Crack Identification ◽  
Single Shot ◽  
Road Maintenance ◽  
Fusion Model ◽  
Detection Model ◽  
Crack Classification

Pavement damage is the main factor affecting road performance. Pavement cracking, a common type of road damage, is a key challenge in road maintenance. In order to achieve an accurate crack classification, segmentation, and geometric parameter calculation, this paper proposes a method based on a deep convolutional neural network fusion model for pavement crack identification, which combines the advantages of the multitarget single-shot multibox detector (SSD) convolutional neural network model and the U-Net model. First, the crack classification and detection model is applied to classify the cracks and obtain the detection confidence. Next, the crack segmentation network is applied to accurately segment the pavement cracks. By improving the feature extraction structure and optimizing the hyperparameters of the model, pavement crack classification and segmentation accuracy were improved. Finally, the length and width (for linear cracks) and the area (for alligator cracks) are calculated according to the segmentation results. Test results show that the recognition accuracy of the pavement crack identification method for transverse, longitudinal, and alligator cracks is 86.8%, 87.6%, and 85.5%, respectively. It is demonstrated that the proposed method can provide the category information for pavement cracks as well as the accurate positioning and geometric parameter information, which can be used directly for evaluating the pavement condition.

scholarly journals Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network

2020 ◽  
pp. 147592172093238
Author(s):  
Muhammad Rakeh Saleem ◽  
Jong-Woong Park ◽  
Jin-Hwan Lee ◽  
Hyung-Jo Jung ◽  
Muhammad Zohaib Sarwar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Visual Inspection ◽  
Crack Detection ◽  
Damage Identification ◽  
Deep Convolutional Neural Network ◽  
Crack Identification ◽  
Image Capturing ◽  
Damage Map ◽  
Tagging System

The structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.

scholarly journals Crack Identification on the Fresh Chilli (Capsicum) Fruit Destemmed System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Quoc-Khanh Huynh ◽  
Chi-Ngon Nguyen ◽  
Hong-Phuc Vo-Nguyen ◽  
Phuong Lan Tran-Nguyen ◽  
Phan-Hung Le ◽  
...  
Keyword(s):  
Neural Network ◽  
Mekong Delta ◽  
Network Models ◽  
Application Programming Interface ◽  
Crack Identification ◽  
Neural Network Models ◽  
Accurate Identification ◽  
Identification Rate ◽  
Classification Image ◽  
Manual Sorting

Destemming fresh chilli fruit (Capsicum) in large productivity is necessary, especially in the Mekong Delta region. Several studies have been done to solve this problem with high applicability, but a certain percentage of the output consisted of cracked fruits, thus reducing the quality of the system. The manual sorting results in high costs and low quality, so it is necessary that automatic grading is performed after destemming. This research focused on developing a method to identify and classify cracked chilli fruits caused by the destemming process. The convolution neural network (CNN) model was built and trained to identify cracks; then, appropriate control signals were sent to the actuator for classification. Image processing operations are supported by the OpenCV library, while the TensorFlow data structure is used as a database and the Keras application programming interface supports the construction and training of neural network models. Experiments were carried out in both the static and working conditions, which, respectively, achieved an accurate identification rate of 97 and 95.3%. In addition, a success rate of 93% was found even when the chilli body is wrinkled due to drying after storage time at 120 hours. Practical results demonstrate that the reliability of the model was useful and acceptable.

Experimental Research on the Crack Identification of Drawing Parts Based on AE Technique

2011 ◽  
Vol 141 ◽  
pp. 275-278
Author(s):  
Zhi Gao Luo ◽  
Bao Gang Zhang ◽  
Xin He
Keyword(s):  
Neural Network ◽  
Experimental Research ◽  
Bp Neural Network ◽  
Productive Efficiency ◽  
Crack Identification ◽  
The Neural Network ◽  
Hidden Layer ◽  
The Cost ◽  
Ae Signals ◽  
Test Result

The paper performs an experimental research on the crack identification of drawing parts using AE technique. Under the platform of the AE system, the AE signals of drawing parts crack are acquired. BP neural network is designed with three layers. They are ten neurons of input layer, three neurons of output layer and thirteen neurons of hidden layer. The characteristic parameters of the crack acoustic emission are considered as the input of BP neural network to exercise the network. The test data are inputted to the neural network after it is exercised. The test result is in accord with the experiment result. The method is proper to identify the crack of drawing parts. The emergence of many inferior parts and the waste of resource can be avoided. It also can debase the cost of manufacture and improve the productive efficiency.

Sign in / Sign up

Export Citation Format

Share Document