A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

The compaction quality of the subgrade is directly related to the service life of the road. Effective control of the subgrade construction process is the key to ensuring the compaction quality of the subgrade. Therefore, real-time, comprehensive, rapid and accurate prediction of construction compaction quality through informatization detection method is an important guarantee for speeding up construction progress and ensuring subgrade compaction quality. Based on the function of the system, this paper puts forward the principle of system development and the development mode used in system development, and displays the development system in real-time to achieve the whole process control of subgrade construction quality.

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Real-Time Line Detection Method for Camera Images of the Rescue Robot

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.130.2039 ◽

2010 ◽

Vol 130 (11) ◽

pp. 2039-2046

Author(s):

Munetoshi Numada ◽

Masaru Shimizu ◽

Takuma Funahashi ◽

Hiroyasu Koshimizu

Keyword(s):

Real Time ◽

Detection Method ◽

Line Detection ◽

Time Line ◽

Rescue Robot

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A real-time abnormal operation pattern detection method for building energy systems based on association rule bases

Building Simulation ◽

10.1007/s12273-021-0791-x ◽

2021 ◽

Author(s):

Chaobo Zhang ◽

Yang Zhao ◽

Yangze Zhou ◽

Xuejun Zhang ◽

Tingting Li

Keyword(s):

Real Time ◽

Association Rule ◽

Detection Method ◽

Energy Systems ◽

Building Energy ◽

Pattern Detection ◽

Operation Pattern ◽

Building Energy Systems ◽

Rule Bases ◽

Abnormal Operation

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Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

Advances in Structural Engineering ◽

10.1177/1369433220986637 ◽

2021 ◽

pp. 136943322098663

Author(s):

Diana Andrushia A ◽

Anand N ◽

Eva Lubloy ◽

Prince Arulraj G

Keyword(s):

Deep Learning ◽

Crack Detection ◽

Detection Method ◽

Concrete Structures ◽

Vital Role ◽

Subjective Knowledge ◽

Thermal Cracks ◽

Reinforced Cement ◽

Novel Method ◽

Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

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A Real-time CSI-based Passive Intrusion Detection Method

2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) ◽

10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00163 ◽

2020 ◽

Author(s):

Xingang Wang ◽

Yufei Wang ◽

Dong Wang

Keyword(s):

Intrusion Detection ◽

Real Time ◽

Detection Method

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A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2357 ◽

2020 ◽

Vol 53 (2) ◽

pp. 15374-15379

Author(s):

Hu He ◽

Xiaoyong Zhang ◽

Fu Jiang ◽

Chenglong Wang ◽

Yingze Yang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Detection Method ◽

Driver Fatigue ◽

Two Stage ◽

Fatigue Detection

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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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A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

Remote Sensing ◽

10.3390/rs13091619 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1619

Author(s):

Bin Yan ◽

Pan Fan ◽

Xiaoyan Lei ◽

Zhijie Liu ◽

Fuzeng Yang

Keyword(s):

Real Time ◽

Apple Tree ◽

Detection Method ◽

Recognition Algorithm ◽

Medium Size ◽

Feature Maps ◽

Detection Algorithms ◽

Fusion Mode ◽

Visual Attention Mechanism ◽

Tree Image

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

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