Attention-Based Convolutional Neural Network for Pavement Crack Detection

Achieving high detection accuracy of pavement cracks with complex textures under different lighting conditions is still challenging. In this context, an encoder-decoder network-based architecture named CrackResAttentionNet was proposed in this study, and the position attention module and channel attention module were connected after each encoder to summarize remote contextual information. The experiment results demonstrated that, compared with other popular models (ENet, ExFuse, FCN, LinkNet, SegNet, and UNet), for the public dataset, CrackResAttentionNet with BCE loss function and PRelu activation function achieved the best performance in terms of precision (89.40), mean IoU (71.51), recall (81.09), and F1 (85.04). Meanwhile, for a self-developed dataset (Yantai dataset), CrackResAttentionNet with BCE loss function and PRelu activation function also had better performance in terms of precision (96.17), mean IoU (83.69), recall (93.44), and F1 (94.79). In particular, for the public dataset, the precision of BCE loss and PRelu activation function was improved by 3.21. For the Yantai dataset, the results indicated that the precision was improved by 0.99, the mean IoU was increased by 0.74, the recall was increased by 1.1, and the F1 for BCE loss and PRelu activation function was increased by 1.24.

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Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

Sensors ◽

10.3390/s21092902 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2902

Author(s):

Wenting Qiao ◽

Qiangwei Liu ◽

Xiaoguang Wu ◽

Biao Ma ◽

Gang Li

Keyword(s):

Deep Learning ◽

Crack Detection ◽

Attention Mechanism ◽

Model Parameters ◽

Detection Accuracy ◽

Safe Driving ◽

Deep Feature ◽

Feature Aggregation ◽

Pavement Crack Detection ◽

Detection Speed

Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

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Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering

Mathematical Problems in Engineering ◽

10.1155/2019/4302805 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Wei Li ◽

Ranran Deng ◽

Yingjie Zhang ◽

Zhaoyun Sun ◽

Xueli Hao ◽

...

Keyword(s):

Crack Detection ◽

Clustering Algorithm ◽

Asphalt Pavement ◽

Fruit Fly ◽

Detection Methods ◽

Detection Accuracy ◽

Density Peak ◽

3D Data ◽

Density Peak Clustering ◽

Pavement Crack Detection

Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.

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Detection of COVID-19 from Chest X-ray Images Using Deep Convolutional Neural Networks

Sensors ◽

10.3390/s21175940 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5940

Author(s):

Natheer Khasawneh ◽

Mohammad Fraiwan ◽

Luay Fraiwan ◽

Basheer Khassawneh ◽

Ali Ibnian

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Detection Accuracy ◽

Deep Convolutional Neural Networks ◽

Local Data ◽

X Ray ◽

The Public ◽

Global Pandemic ◽

Chest X Ray ◽

Public Dataset

The COVID-19 global pandemic has wreaked havoc on every aspect of our lives. More specifically, healthcare systems were greatly stretched to their limits and beyond. Advances in artificial intelligence have enabled the implementation of sophisticated applications that can meet clinical accuracy requirements. In this study, customized and pre-trained deep learning models based on convolutional neural networks were used to detect pneumonia caused by COVID-19 respiratory complications. Chest X-ray images from 368 confirmed COVID-19 patients were collected locally. In addition, data from three publicly available datasets were used. The performance was evaluated in four ways. First, the public dataset was used for training and testing. Second, data from the local and public sources were combined and used to train and test the models. Third, the public dataset was used to train the model and the local data were used for testing only. This approach adds greater credibility to the detection models and tests their ability to generalize to new data without overfitting the model to specific samples. Fourth, the combined data were used for training and the local dataset was used for testing. The results show a high detection accuracy of 98.7% with the combined dataset, and most models handled new data with an insignificant drop in accuracy.

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Pavement Crack Detection Method Based on Deep Learning Models

Wireless Communications and Mobile Computing ◽

10.1155/2021/5573590 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Guo X. Hu ◽

Bao L. Hu ◽

Zhong Yang ◽

Li Huang ◽

Ping Li

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Crack Detection ◽

Detection Method ◽

Longitudinal Crack ◽

Detection Accuracy ◽

Safe Driving ◽

Detection Model ◽

Model Based ◽

Pavement Crack Detection

Severe weather and long-term driving of vehicles lead to various cracks on asphalt pavement. If these cracks cannot be found and repaired in time, it will have a negative impact on the safe driving of vehicles. Traditional artificial detection has some problems, such as low efficiency and missing detection. The detection model based on machine learning needs artificial design of pavement crack characteristics. According to the pavement distress identification manual proposed by the Federal Highway Administration (FHWA), these categories have three different types of cracks, such as fatigue, longitudinal crack, and transverse cracks. In the face of many types of pavement cracks, it is difficult to design a general feature extraction model to extract pavement crack features, which leads to the poor effect of the automatic detection model based on machine learning. Object detection based on the deep learning model has achieved good results in many fields. As a result, those models have become possible for pavement crack detection. This paper discusses the latest YOLOv5 series detection model for pavement crack detection and is to find out an effective training and detection method. Firstly, the 3001 asphalt crack pavement images with the original size of 2976 × 3978 pixels are collected using a digital camera and are randomly divided into three types according to the severity levels of low, medium, and high. Then, for the dataset of crack pavement, YOLOv5 series models are used for training and testing. The experimental results show that the detection accuracy of the YOLOv5l model is the highest, reaching 88.1%, and the detection time of the YOLOv5s model is the shortest, only 11.1 ms for each image.

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Bridge Crack Detection Based on SSENets

Applied Sciences ◽

10.3390/app10124230 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4230 ◽

Cited By ~ 1

Author(s):

Haotian Li ◽

Hongyan Xu ◽

Xiaodong Tian ◽

Yi Wang ◽

Huaiyu Cai ◽

...

Keyword(s):

Neural Networks ◽

Computational Complexity ◽

Crack Detection ◽

Contextual Information ◽

Detection Accuracy ◽

Multi Scale ◽

Surrounding Environment ◽

Spatial Pyramid Pooling ◽

Spatial Pyramid ◽

Better Than

Bridge crack detection is essential to prevent transportation accidents. However, the surrounding environment has great interference with the detection of cracks, which makes it difficult to ensure the accuracy of the detection. In order to accurately detect bridge cracks, we proposed an end-to-end model named Skip-Squeeze-and-Excitation Networks (SSENets). It is mainly composed of the Skip-Squeeze-Excitation (SSE) module and the Atrous Spatial Pyramid Pooling (ASPP) module. The SSE module uses skip-connection strategy to enhance the gradient correlation between the shallow network and deeper network, alleviating the vanishing gradient caused by the deepening of the network. The ASPP module can extract multi-scale contextual information of images, while the depthwise separable convolution reduces computational complexity. In order to avoid destroying the topology of crack, we used atrous convolution instead of the pooling layer. The proposed SSENets achieved a detection accuracy of 97.77%, which performed better than the models we compared it with. The designed SSE module which used skip-connection strategy can be embedded in other convolutional neural networks (CNNs) to improve their performance.

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Gabor filter fusion network for pavement crack detection

Chinese Optics ◽

10.37188/co.2020-0041 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1-9

Author(s):

CHEN Xiao-Dong ◽

◽

AI Da-Hang ◽

ZHANG Jia-Chen ◽

CAI Huai-Yu ◽

...

Keyword(s):

Crack Detection ◽

Gabor Filter ◽

Pavement Crack Detection

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The Problems and Needs of Patients Diagnosed with Cancer and Their Caregivers

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010087 ◽

2020 ◽

Vol 18 (1) ◽

pp. 87

Author(s):

Anna Lewandowska ◽

Grzegorz Rudzki ◽

Tomasz Lewandowski ◽

Sławomir Rudzki

Keyword(s):

Cancer Patients ◽

Medical Information ◽

Unmet Needs ◽

Public Healthcare ◽

Literature Analysis ◽

The Public ◽

The Mean ◽

High Level ◽

Patients Experience

(1) Background: As the literature analysis shows, cancer patients experience a variety of different needs. Each patient reacts differently to the hardships of the illness. Assessment of needs allows providing more effective support, relevant to every person’s individual experience, and is necessary for setting priorities for resource allocation, for planning and conducting holistic care, i.e., care designed to improve a patient’s quality of life in a significant way. (2) Patients and Methods: A population survey was conducted between 2018 and 2020. Cancer patients, as well as their caregivers, received an invitation to take part in the research, so their problems and needs could be assessed. (3) Results: The study involved 800 patients, 78% women and 22% men. 66% of the subjects were village residents, while 34%—city residents. The mean age of patients was 62 years, SD = 11.8. The patients received proper treatment within the public healthcare. The surveyed group of caregivers was 88% women and 12% men, 36% village residents and 64% city residents. Subjects were averagely 57 years old, SD 7.8. At the time of diagnosis, the subjects most often felt anxiety, despair, depression, feelings of helplessness (46%, 95% CI: 40–48). During illness and treatment, the subjects most often felt fatigued (79%, 95% CI: 70–80). Analysis of needs showed that 93% (95% CI: 89–97) of patients experienced a certain level of need for help in one or more aspects. (4) Conclusions: Patients diagnosed with cancer have a high level of unmet needs, especially in terms of psychological support and medical information. Their caregivers also experience needs and concerns regarding the disease. Caregivers should be made aware of the health consequences of cancer and consider appropriate supportive care for their loved ones.

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An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽

10.3390/s21051581 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1581

Author(s):

Xiaolong Chen ◽

Jian Li ◽

Shuowen Huang ◽

Hao Cui ◽

Peirong Liu ◽

...

Keyword(s):

Crack Detection ◽

Detection Method ◽

Multiple Scales ◽

Point Clouds ◽

Depth Image ◽

Detection Accuracy ◽

Detection Techniques ◽

Concrete Crack ◽

3D Point Clouds ◽

2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

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Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽

10.3390/electronics10020197 ◽

2021 ◽

Vol 10 (2) ◽

pp. 197

Author(s):

Meng-ting Fang ◽

Zhong-ju Chen ◽

Krzysztof Przystupa ◽

Tao Li ◽

Michal Majka ◽

...

Keyword(s):

Detection Method ◽

Abnormal Behavior ◽

Detection Accuracy ◽

Average Precision ◽

Research Results ◽

Behavior Detection ◽

Abnormal Behavior Detection ◽

The Mean ◽

Examination Room ◽

Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

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Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽

10.3390/s21051820 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1820

Author(s):

Xiaotao Shao ◽

Qing Wang ◽

Wei Yang ◽

Yun Chen ◽

Yi Xie ◽

...

Keyword(s):

Semantic Information ◽

Detection System ◽

Pedestrian Detection ◽

Detection Accuracy ◽

The Public ◽

Scale Feature ◽

Detection Algorithms ◽

Multi Scale ◽

Art Works ◽

Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

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