scholarly journals Comparison of Convolutional Sparse Coding Network and Convolutional Neural Network for Pavement Crack Classification: A Validation Study

2020 ◽  
Vol 1682 ◽  
pp. 012016
Author(s):  
Haitong Tang ◽  
Jun Shi ◽  
Xia Lu ◽  
Zhichao Yin ◽  
Lixue Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Validation Study ◽  
Sparse Coding ◽  
Crack Classification

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 Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e041139
Author(s):  
Yuexin Cai ◽  
Jin-Gang Yu ◽  
Yuebo Chen ◽  
Chu Liu ◽  
Lichao Xiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tympanic Membrane ◽  
Otitis Media ◽  
Validation Study ◽  
Model Development ◽  
Classification Model ◽  
Automated Diagnosis ◽  
Two Stage ◽  
Development And Validation

ObjectivesThis study investigated the usefulness and performance of a two-stage attention-aware convolutional neural network (CNN) for the automated diagnosis of otitis media from tympanic membrane (TM) images.DesignA classification model development and validation study in ears with otitis media based on otoscopic TM images. Two commonly used CNNs were trained and evaluated on the dataset. On the basis of a Class Activation Map (CAM), a two-stage classification pipeline was developed to improve accuracy and reliability, and simulate an expert reading the TM images.Setting and participantsThis is a retrospective study using otoendoscopic images obtained from the Department of Otorhinolaryngology in China. A dataset was generated with 6066 otoscopic images from 2022 participants comprising four kinds of TM images, that is, normal eardrum, otitis media with effusion (OME) and two stages of chronic suppurative otitis media (CSOM).ResultsThe proposed method achieved an overall accuracy of 93.4% using ResNet50 as the backbone network in a threefold cross-validation. The F1 Score of classification for normal images was 94.3%, and 96.8% for OME. There was a small difference between the active and inactive status of CSOM, achieving 91.7% and 82.4% F1 scores, respectively. The results demonstrate a classification performance equivalent to the diagnosis level of an associate professor in otolaryngology.ConclusionsCNNs provide a useful and effective tool for the automated classification of TM images. In addition, having a weakly supervised method such as CAM can help the network focus on discriminative parts of the image and improve performance with a relatively small database. This two-stage method is beneficial to improve the accuracy of diagnosis of otitis media for junior otolaryngologists and physicians in other disciplines.

Hyperspectral Image Classification Combined with Convolutional Neural Network and Sparse Coding

2020 ◽  
Vol 57 (18) ◽  
pp. 182802
Author(s):  
孙劲光 Sun Jinguang ◽  
李燕北 Li Yanbei ◽  
魏宪 Wei Xian ◽  
王万里 Wang Wanli
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Sparse Coding ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification

Landform Image Classification Based on Sparse Coding and Convolutional Neural Network

2019 ◽  
Vol 39 (4) ◽  
pp. 0410001
Author(s):  
刘芳 Liu Fang ◽  
王鑫 Wang Xin ◽  
路丽霞 Lu Lixia ◽  
黄光伟 Huang Guangwei ◽  
王洪娟 Wang Hongjuan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Sparse Coding

Recognition and retrieval of sound events using sparse coding convolutional neural network

2017 ◽  
Author(s):  
Chien-Yao Wang ◽  
Andri Santoso ◽  
Seksan Mathulaprangsan ◽  
Chin-Chin Chiang ◽  
Chung-Hsien Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sparse Coding

Deep learning–based roadway crack classification with heterogeneous image data fusion

2020 ◽  
pp. 147592172094843
Author(s):  
Shanglian Zhou ◽  
Wei Song
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Image Data ◽  
Deep Convolutional Neural Network ◽  
Network Architectures ◽  
Range Image ◽  
Crack Classification ◽  
Neural Network Architectures

By providing accurate and efficient crack detection and localization, image-based crack detection methodologies can facilitate the decision-making and rehabilitation of the roadway infrastructure. Deep convolutional neural network, as one of the most prevailing image-based methodologies on object recognition, has been extensively adopted for crack classification tasks in the recent decade. For most of the current deep convolutional neural network–based techniques, either intensity or range image data are utilized to interpret the crack presence. However, the complexities in real-world data may impair the robustness of deep convolutional neural network architecture in its ability to analyze image data with various types of disturbances, such as low contrast in intensity images and shallow cracks in range images. The detection performance under these disturbances is important to protect the investment in infrastructure, as it can reveal the trend of crack evolution and provide information at an early stage to promote precautionary measures. This article proposes novel deep convolutional neural network–based roadway classification tools and investigates their performance from the perspective of using heterogeneous image fusion. A vehicle-mounted laser imaging system is adopted for data acquisition (DAQ) on concrete roadways with a depth resolution of 0.1 mm and an accuracy of 0.4 mm. In total, four types of image data including raw intensity, raw range, filtered range, and fused raw image data are utilized to train and test the deep convolutional neural network architectures proposed in this study. The experimental cases demonstrate that the proposed data fusion approach can reduce false detections and thus results in an improvement of 4.5%, 1.2%, and 0.7% in the F-measure value, respectively, compared to utilizing the raw intensity, raw range, and filtered range image data for analysis. Furthermore, in another experimental case, two novel deep convolutional neural network architectures proposed in this study are compared to exploit the fused raw image data, and the one leading to better classification performance is determined.

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