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.

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.

A vision-based active learning convolutional neural network model for concrete surface crack detection

2020 ◽  
Vol 23 (13) ◽  
pp. 2952-2964
Author(s):  
Zhen Wang ◽  
Guoshan Xu ◽  
Yong Ding ◽  
Bin Wu ◽  
Guoyu Lu
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Surface Crack ◽  
Crack Detection ◽  
Concrete Surface ◽  
Learning Ability ◽  
Training Method ◽  
And Training ◽  
New Framework

Concrete surface crack detection based on computer vision, specifically via a convolutional neural network, has drawn increasing attention for replacing manual visual inspection of bridges and buildings. This article proposes a new framework for this task and a sampling and training method based on active learning to treat class imbalances. In particular, the new framework includes a clear definition of two categories of samples, a relevant sliding window technique, data augmentation and annotation methods. The advantages of this framework are that data integrity can be ensured and a very large amount of annotation work can be saved. Training datasets generated with the proposed sampling and training method not only are representative of the original dataset but also highlight samples that are highly complex, yet informative. Based on the proposed framework and sampling and training strategy, AlexNet is re-tuned, validated, tested and compared with an existing network. The investigation revealed outstanding performances of the proposed framework in terms of the detection accuracy, precision and F1 measure due to its nonlinear learning ability, training dataset integrity and active learning strategy.

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 ORTHOSEG: A DEEP MULTIMODAL CONVOLUTONAL NEURAL NETWORK ARCHITECTURE FOR SEMANTIC SEGMENTATION OF ORTHOIMAGERY

2018 ◽  
Vol XLII-5 ◽  
pp. 621-628 ◽  
Author(s):  
P. Bodani ◽  
K. Shreshtha ◽  
S. Sharma
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Multiple Scales ◽  
Semantic Segmentation ◽  
Effective Field ◽  
Surface Model ◽  
Training Procedure ◽  
Feature Maps ◽  
Neural Network Architecture ◽  
Wide Range

<p><strong>Abstract.</strong> This paper addresses the task of semantic segmentation of orthoimagery using multimodal data e.g. optical RGB, infrared and digital surface model. We propose a deep convolutional neural network architecture termed OrthoSeg for semantic segmentation using multimodal, orthorectified and coregistered data. We also propose a training procedure for supervised training of OrthoSeg. The training procedure complements the inherent architectural characteristics of OrthoSeg for preventing complex co-adaptations of learned features, which may arise due to probable high dimensionality and spatial correlation in multimodal and/or multispectral coregistered data. OrthoSeg consists of parallel encoding networks for independent encoding of multimodal feature maps and a decoder designed for efficiently fusing independently encoded multimodal feature maps. A softmax layer at the end of the network uses the features generated by the decoder for pixel-wise classification. The decoder fuses feature maps from the parallel encoders locally as well as contextually at multiple scales to generate per-pixel feature maps for final pixel-wise classification resulting in segmented output. We experimentally show the merits of OrthoSeg by demonstrating state-of-the-art accuracy on the ISPRS Potsdam 2D Semantic Segmentation dataset. Adaptability is one of the key motivations behind OrthoSeg so that it serves as a useful architectural option for a wide range of problems involving the task of semantic segmentation of coregistered multimodal and/or multispectral imagery. Hence, OrthoSeg is designed to enable independent scaling of parallel encoder networks and decoder network to better match application requirements, such as the number of input channels, the effective field-of-view, and model capacity.</p>

scholarly journals Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4403
Author(s):  
Umme Hafsa Billah ◽  
Hung Manh La ◽  
Alireza Tavakkoli
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Crack Detection ◽  
Inspection System ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Neural Network Architecture ◽  
Concrete Crack ◽  
Discriminative Feature

An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we present a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation, while addressing the effect of gradient vanishing problem. A feature silencing module is incorporated in the proposed framework, capable of eliminating non-discriminative feature maps from the network to improve performance. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than the state-of-the-art crack detection architectures.

scholarly journals Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Shengyuan Li ◽  
Xuefeng Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Detection Method ◽  
Smartphone Application ◽  
Concrete Surface ◽  
Search Technique ◽  
Concrete Crack ◽  
Learning Rates ◽  
Complex Image

Crack detection is important for the inspection and evaluation during the maintenance of concrete structures. However, conventional image-based methods need extract crack features using complex image preprocessing techniques, so it can lead to challenges when concrete surface contains various types of noise due to extensively varying real-world situations such as thin cracks, rough surface, shadows, etc. To overcome these challenges, this paper proposes an image-based crack detection method using a deep convolutional neural network (CNN). A CNN is designed through modifying AlexNet and then trained and validated using a built database with 60000 images. Through comparing validation accuracy under different base learning rates, 0.01 was chosen as the best base learning rate with the highest validation accuracy of 99.06%, and its training result is used in the following testing process. The robustness and adaptability of the trained CNN are tested on 205 images with 3120 × 4160 pixel resolutions which were not used for training and validation. The trained CNN is integrated into a smartphone application to mobile more public to detect cracks in practice. The results confirm that the proposed method can indeed detect cracks in images from real concrete surfaces.

scholarly journals Deep Learning Based Feature Silencing for Accurate Concrete Crack Detection

2020 ◽  
Author(s):  
Umme Billah ◽  
Hung La ◽  
Alireza Tavakkoli
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Crack Detection ◽  
Inspection System ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Neural Network Architecture ◽  
Concrete Crack ◽  
Positive Class

An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we represent a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation from concrete image. The proposed network alleviates the effect of gradient vanishing problem present in deep neural network architectures. A feature silencing module is incorporated in the crack detection framework, for eliminating unnecessary feature maps from the network. The overall performance of the network significantly improves as a result. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than crack detection architectures present in literature.

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