scholarly journals Concrete Cracks Detection Using Convolutional NeuralNetwork Based on Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chao Su ◽  
Wenjun Wang
Keyword(s):  
Transfer Learning ◽  
Crack Detection ◽  
Critical Role ◽  
Concrete Surface ◽  
Surface Cracks ◽  
Generalization Capability ◽  
Detection Model ◽  
Neural Architecture ◽  
Efficient Detection

Crack plays a critical role in the field of evaluating the quality of concrete structures, which affects the safety, applicability, and durability of the structure. Due to its excellent performance in image processing, the convolutional neural network is becoming the mainstream choice to replace manual crack detection. In this paper, we improve the EfficientNetB0 to realize the detection of concrete surface cracks using the transfer learning method. The model is designed by neural architecture search technology. The weights are pretrained on the ImageNet. Supervised learning uses Adam optimizer to update network parameters. In the testing process, crack images from different locations were used to further test the generalization capability of the model. By comparing the detection results with the MobileNetV2, DenseNet201, and InceptionV3 models, the results show that our model greatly reduces the number of parameters while achieving high accuracy (0.9911) and has good generalization capability. Our model is an efficient detection model, which provides a new option for crack detection in areas with limited computing resources.

scholarly journals Computational Prediction of Disease Detection and Insect Identification using Xception model

2021 ◽  
Author(s):  
Lidia Cleetus ◽  
Raji Sukumar ◽  
Hemalatha N
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Computational Prediction ◽  
Detection Model ◽  
Insect Identification ◽  
Detection And Identification ◽  
Efficient Detection ◽  
Improved Performance ◽  
Learning Architectures ◽  
Base Data

In this paper, a detection tool has been built for the detection and identification of the diseases and pests found in the crops at its earliest stage. For this, various deep learning architectures were experimented to see which one of those would help in building a more accurate and an efficient detection model. The deep learning architectures used in this study were Convolutional Neural Network, VGG16, InceptionV3, and Xception. VGG16, InceptionV3, and Xception are categorized as the pre-trained models based on CNN architecture. They follow the concept of transfer learning. Transfer learning is a technique which makes use of the learnings of the models previously trained on a base data and applies it to the present dataset. This is an efficient technique which gives us rapid results and improved performance. Two plant datasets have been used here for disease and insects. The results of the algorithms were then compared. Most successful one has been the Xception model which obtained 82.89 for disease and 77.9 for pests.

scholarly journals A Real-Time Detection Method for Concrete Surface Cracks Based on Improved YOLOv4

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1716
Author(s):  
Gang Yao ◽  
Yujia Sun ◽  
Mingpu Wong ◽  
Xiaoning Lv
Keyword(s):  
Real Time ◽  
Loss Function ◽  
Crack Detection ◽  
Civil Engineering ◽  
Concrete Surface ◽  
Model Parameters ◽  
Surface Cracks ◽  
Engineering Structures ◽  
Real Time Detection ◽  
Improved Model

Many structures in civil engineering are symmetrical. Crack detection is a critical task in the monitoring and inspection of civil engineering structures. This study implements a lightweight neural network based on the YOLOv4 algorithm to detect concrete surface cracks. In the extraction of backbone and the design of neck and head, the symmetry concept is adopted. The model modules are improved to reduce the depth and complexity of the overall network structure. Meanwhile, the separable convolution is used to realize spatial convolution, and the SPP and PANet modules are improved to reduce the model parameters. The convolutional layer and batch normalization layer are merged to improve the model inference speed. In addition, using the focal loss function for reference, the loss function of object detection network is improved to balance the proportion of the cracks and the background samples. To comprehensively evaluate the performance of the improved method, 10,000 images (256 × 256 pixels in size) of cracks on concrete surfaces are collected to build the database. The improved YOLOv4 model achieves an mAP of 94.09% with 8.04 M and 0.64 GMacs. The results show that the improved model is satisfactory in mAP, and the model size and calculation amount are greatly reduced. This performs better in terms of real-time detection on concrete surface cracks.

scholarly journals Lightweight Neural Network for Real-Time Crack Detection on Concrete Surface in Fog

2021 ◽  
Vol 8 ◽  
Author(s):  
Gang Yao ◽  
Yujia Sun ◽  
Yang Yang ◽  
Gang Liao
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Crack Detection ◽  
Multiple Scales ◽  
Current Method ◽  
Objective Evaluation ◽  
Concrete Surface ◽  
Surface Cracks ◽  
Bilateral Constraints ◽  
Improved Model

Cracks are one of the most common factors that affect the quality of concrete surfaces, so it is necessary to detect concrete surface cracks. However, the current method of manual crack detection is labor-intensive and time-consuming. This study implements a novel lightweight neural network based on the YOLOv4 algorithm to detect cracks on a concrete surface in fog. Using the computer vision algorithm and the GhostNet Module concept for reference, the backbone network architecture of YOLOv4 is improved. The feature redundancy between networks is reduced and the entire network is compressed. The multi-scale fusion method is adopted to effectively detect cracks on concrete surfaces. In addition, the detection of concrete surface cracks is seriously affected by the frequent occurrence of fog. In view of a series of degradation phenomena in image acquisition in fog and the low accuracy of crack detection, the network model is integrated with the dark channel prior concept and the Inception module. The image crack features are extracted at multiple scales, and BReLU bilateral constraints are adopted to maintain local linearity. The improved model for crack detection in fog achieved an mAP of 96.50% with 132 M and 2.24 GMacs. The experimental results show that the detection performance of the proposed model has been improved in both subjective vision and objective evaluation metrics. This performs better in terms of detecting concrete surface cracks in fog.

Status of nondestructive control of transport function metal products at JSC EVRAZ NTMK

2020 ◽  
Vol 76 (6) ◽  
pp. 586-590
Author(s):  
S. P. Bersenev ◽  
E. M. Slobtsova
Keyword(s):  
Crack Detection ◽  
Middle Part ◽  
Control Line ◽  
Magnetic Powder ◽  
Nondestructive Control ◽  
Automated Control ◽  
Ultrasonic Control ◽  
Acoustic Contact ◽  
In Series

Achievements in the area of automated ultrasonic control of quality of rails, solid-rolled wheels and tyres, wheels magnetic powder crack detection, carried out at JSC EVRAZ NTMK. The 100% nondestructive control is accomplished by automated control in series at two ultrasonic facilities RWI-01 and four facilities УМКК-1 of magnetic powder control, installed into the exit control line in the wheel-tyre shop. Diagram of location, converters displacement and control operations in the process of control at the facility RWI-01 presented, as well as the structural diagram of the facility УМКК-1. The automated ultrasonic control of rough tyres is made in the tyres control line of the wheel-tyre shop at the facility УКБ-1Д. The facility enables to control internal defects of tyres in radial, axis and circular directions of radiation. Possibilities of the facility УКБ-1Д software were shown. Nondestructive control of railway rails is made at two facilities, comprising the automated control line of the rail and structural shop. The УКР-64Э facility of automated ultrasonic rails control is intended to reveal defects in the area of head, web and middle part of rail foot by pulse echo-method with a immersion acoustic contact. The diagram of rail P65 at the facility УКР-64Э control presented. To reveal defects of the macrostructure in the area of rail head and web by mirror-shadow method, an ultrasonic noncontact electromagnetic-acoustic facility is used. It was noted, that implementation of the 100% nondestructive control into the technology of rolled stuff production enabled to increase the quality of products supplied to customers and to increase their competiveness.

Motivating the staff of Jazan University

2013 ◽  
Vol 10 (1) ◽  
pp. 1261-1267
Author(s):  
Ali Medabesh
Keyword(s):  
Human Capital ◽  
Quality Of Service ◽  
Economic System ◽  
Endogenous Growth ◽  
Employee Satisfaction ◽  
Development Process ◽  
Empirical Investigation ◽  
Critical Role ◽  
Financial Investment

The quality of public services and the yield of organizations are not limited to the financial investment and innovation solely. Human capital plays a critical role in the growth and excellence in institutions, but its contribution remains dependent on several factors. Its role is not limited on quantitative and qualitative accumulating, because it should be coherent and integrated in the development process. The theories of endogenous growth contributed to account for the disparity in levels of development between countries, by assuming that the extent of human capital response or inversely lack of responsiveness the economic system. This inaction is usually the prime cause of the deterioration of the quality of service and lack of satisfaction of the citizens, in addition of the lack of employee satisfaction about the circumstances of his work. Hence, arose the significance of several research about the mechanisms of reducing non-enthusiasm for the job or complacency professional and indifference. Staff of Jazan University has been chosen as a context of the empirical investigation of this study. The data has been collected using a well designed questionnaire and analyzed by SPSS program.

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

scholarly journals CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1402
Author(s):  
Taehee Lee ◽  
Yeohwan Yoon ◽  
Chanjun Chun ◽  
Seungki Ryu
Keyword(s):  
Surface Crack ◽  
Autonomous Vehicles ◽  
Crack Detection ◽  
Model Performance ◽  
Semantic Segmentation ◽  
Road Surface ◽  
Image Brightness ◽  
The Road ◽  
Detection Model ◽  
Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

scholarly journals Relationship between Emotional Intelligence and Violence Exerted, Received, and Perceived in Teen Dating Relationships

2021 ◽  
Vol 18 (5) ◽  
pp. 2284
Author(s):  
Cordelia Estevez-Casellas ◽  
Mª Dolores Gómez-Medina ◽  
Esther Sitges
Keyword(s):  
Emotional Intelligence ◽  
Social Relationships ◽  
Violent Behavior ◽  
Critical Role ◽  
Dating Relationships ◽  
Significant Relation ◽  
Violent Behaviors ◽  
Teen Dating ◽  
The Relationship

Emotional intelligence plays a critical role in adolescence since it involves a change towards psychological, social, and sexual maturity; a stage in which the foundations of intimate social relationships are established. Emotional competences regulate the quality of these relationships in adolescence and can provide protection against or facilitate the use of violence within them. Based on the above, this study aims to analyze the relationship between emotional intelligence and violence exercised, received, and perceived by adolescents in dating relationships. A sample of 254 subjects (43.1% men and 56.9% women) between 12 and 18 years old was analyzed through the Emotional Intelligence Questionnaires of BarOn ICE:NA and Violence Exercised Perceived and Received by Adolescents VERA. The results of the research have shown that there is a significant and inverse relation between the dimensions of emotional intelligence and the violence exercised by adolescents in their dating relationships, and a positive and significant relation between emotional intelligence and the perception of violent behavior. For this reason, the importance of educating people about emotional intelligence from childhood within both the academic and family sphere is highlighted. This is fundamental to preventing the appearance of such violent behaviors and promoting an adequate adaptation to the environment.

Simulation and Experimental Analysis of the Infrared Detection for Surface Cracks of Metal Pipes with Penetration Treatment

2015 ◽  
Vol 719-720 ◽  
pp. 238-242
Author(s):  
Xiong Wan
Keyword(s):  
Crack Detection ◽  
Detection Sensitivity ◽  
Infrared Detection ◽  
Thermal Excitation ◽  
Surface Cracks ◽  
Pipe Model ◽  
Long Time ◽  
Metal Pipes ◽  
Transient Thermal ◽  
Simulation Parameters

Working in the corrosive environment for a long time, it is easy for metal pipes to produce stress corrosion cracks which will affect the use. An infrared detection method combining permeate treatment with heat-incentive steam is proposed to detect surface cracks, which then has been verified by simulations and experiments. For the simulation, pipe model including four cracks of different depth and width was constructed by ANSYS. Transient thermal analysis was made after convection incentive loaded on internal and external wall in the case of whether or not undergo surface infiltration processing. For the experiment, pipe including cracks were made the same as simulation parameters, then experiments were made using the thermal excitation system in two cases. Surface temperature distributions of the pipe were compared in two cases, the results of the study show that penetration treatment before heat incentive can significantly improve the surface crack detection sensitivity.

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