NB-FCN: Real-Time Accurate Crack Detection in Inspection Videos Using Deep Fully Convolutional Network and Parametric Data Fusion

2020 ◽  
Vol 69 (8) ◽  
pp. 5325-5334 ◽  
Author(s):  
Fu-Chen Chen ◽  
Mohammad R. Jahanshahi
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Crack Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Parametric Data

Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network

2018 ◽  
Vol 33 (12) ◽  
pp. 1090-1109 ◽  
Author(s):  
Xincong Yang ◽  
Heng Li ◽  
Yantao Yu ◽  
Xiaochun Luo ◽  
Ting Huang ◽  
...  
Keyword(s):  
Crack Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals Real-time Arabic scene text detection using fully convolutional neural networks

2021 ◽  
Vol 11 (2) ◽  
pp. 1634
Author(s):  
Rajae Moumen ◽  
Raddouane Chiheb ◽  
Rdouan Faizi
Keyword(s):  
Real Time ◽  
Data Augmentation ◽  
State Of The Art ◽  
Arabic Language ◽  
Text Detection ◽  
The State ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Scene Text Detection ◽  
Scene Text

The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed.

scholarly journals INVESTIGATING FULLY CONVOLUTIONAL NETWORK TO SEMANTIC LABELLING OF BATHYMETRIC POINT CLOUD

2020 ◽  
Vol V-2-2020 ◽  
pp. 657-663
Author(s):  
S. Daniel ◽  
V. Dupont
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
3D Structure ◽  
Point Clouds ◽  
Training Data ◽  
Strong Impact ◽  
Convolutional Network ◽  
Real Time Processing ◽  
Time Processing ◽  
Fully Convolutional Network

Abstract. The benefit of autonomous vehicles in hydrography is largely based on the ability of these platforms to carry out survey campaigns in a fully autonomous manner. One solution is to have real-time processing onboard the survey vessel. To meet this real-time processing goal, deep learning based-models are favored. Although Artificial Intelligence (AI) is booming, the main studies have been devoted to optical images and more recently, to LIDAR point clouds. However, little attention has been paid to the underwater environment. In this paper, we present an investigation into the adaptation of deep neural network to multi-beam echo-sounder (MBES) point cloud in order to classify sea-bottom morphology. More precisely, the paper investigates whether fully convolutional network can be trained while using the native 3D structure of the point cloud. A preprocessing approach is provided in order to overcome the lack of adequate training data. The results reported from the test data sets show the level of complexity related to natural, underwater terrain features where a classification accuracy no better than 65% can be reached when 2 micro topographic classes are used. Point density and resolution have a strong impact on the seabed morphology thereby affecting the classification scheme.

scholarly journals Automatic Pixel-Level Crack Detection on Dam Surface Using Deep Convolutional Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2069 ◽  
Author(s):  
Chuncheng Feng ◽  
Hua Zhang ◽  
Haoran Wang ◽  
Shuang Wang ◽  
Yonglong Li
Keyword(s):  
Crack Detection ◽  
Health Assessment ◽  
Detection Methods ◽  
Convolutional Network ◽  
Test Dataset ◽  
Fully Convolutional Network ◽  
Aerial Vehicle ◽  
Deep Convolution Network ◽  
Hydropower Stations ◽  
F Measure

Crack detection on dam surfaces is an important task for safe inspection of hydropower stations. More and more object detection methods based on deep learning are being applied to crack detection. However, most of the methods can only achieve the classification and rough location of cracks. Pixel-level crack detection can provide more intuitive and accurate detection results for dam health assessment. To realize pixel-level crack detection, a method of crack detection on dam surface (CDDS) using deep convolution network is proposed. First, we use an unmanned aerial vehicle (UAV) to collect dam surface images along a predetermined trajectory. Second, raw images are cropped. Then crack regions are manually labelled on cropped images to create the crack dataset, and the architecture of CDDS network is designed. Finally, the CDDS network is trained, validated and tested using the crack dataset. To validate the performance of the CDDS network, the predicted results are compared with ResNet152-based, SegNet, UNet and fully convolutional network (FCN). In terms of crack segmentation, the recall, precision, F-measure and IoU are 80.45%, 80.31%, 79.16%, and 66.76%. The results on test dataset show that the CDDS network has better performance for crack detection of dam surfaces.

scholarly journals Real-Time Object Detection in Remote Sensing Images Based on Visual Perception and Memory Reasoning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1151 ◽  
Author(s):  
Xia Hua ◽  
Xinqing Wang ◽  
Ting Rui ◽  
Dong Wang ◽  
Faming Shao
Keyword(s):  
Remote Sensing ◽  
Visual Perception ◽  
Object Detection ◽  
Real Time ◽  
Detection Accuracy ◽  
Small Object ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Convolutional Network ◽  
Fully Convolutional Network

Aiming at the real-time detection of multiple objects and micro-objects in large-scene remote sensing images, a cascaded convolutional neural network real-time object-detection framework for remote sensing images is proposed, which integrates visual perception and convolutional memory network reasoning. The detection framework is composed of two fully convolutional networks, namely, the strengthened object self-attention pre-screening fully convolutional network (SOSA-FCN) and the object accurate detection fully convolutional network (AD-FCN). SOSA-FCN introduces a self-attention module to extract attention feature maps and constructs a depth feature pyramid to optimize the attention feature maps by combining convolutional long-term and short-term memory networks. It guides the acquisition of potential sub-regions of the object in the scene, reduces the computational complexity, and enhances the network’s ability to extract multi-scale object features. It adapts to the complex background and small object characteristics of a large-scene remote sensing image. In AD-FCN, the object mask and object orientation estimation layer are designed to achieve fine positioning of candidate frames. The performance of the proposed algorithm is compared with that of other advanced methods on NWPU_VHR-10, DOTA, UCAS-AOD, and other open datasets. The experimental results show that the proposed algorithm significantly improves the efficiency of object detection while ensuring detection accuracy and has high adaptability. It has extensive engineering application prospects.

scholarly journals Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4860
Author(s):  
Ziye Wang ◽  
Renguang Zuo ◽  
Hao Liu
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Classification Accuracy ◽  
Learning Algorithms ◽  
Geological Mapping ◽  
Fusion Technology ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Geological Features ◽  
Source Data

Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping.

scholarly journals A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Meng Meng ◽  
Kun Zhu ◽  
Keqin Chen ◽  
Hang Qu
Keyword(s):  
Large Scale ◽  
Crack Detection ◽  
Damage Identification ◽  
Low Cost ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Concrete Crack ◽  
The Common ◽  
Underwater Structures

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.

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