Elevator Door State Detection Method Based on Semantic Segmentation

2021 ◽  
Author(s):  
Yanting Huang ◽  
Qi Wen ◽  
Shuai Dong
Keyword(s):  
Detection Method ◽  
Semantic Segmentation ◽  
State Detection ◽  
Elevator Door

scholarly journals Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 717 ◽  
Author(s):  
Gang Li ◽  
Biao Ma ◽  
Shuanhai He ◽  
Xueli Ren ◽  
Qiangwei Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Width ◽  
Crack Detection ◽  
Detection Method ◽  
Learning Algorithm ◽  
Semantic Segmentation ◽  
Fully Convolutional Networks ◽  
Deep Learning Algorithm ◽  
Mean Width

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496   ×   496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

scholarly journals Research on Micro-EDM Discharge State Detection Technology Based on Inter Electrode Impedance Variation Characteristics

2021 ◽  
Author(s):  
Rui Chen ◽  
Bo Hu ◽  
Yongbin Zhang ◽  
Guangmin Liu ◽  
Yue Dai ◽  
...  
Keyword(s):  
Duty Cycle ◽  
Pulse Width ◽  
Detection Method ◽  
Electrode Impedance ◽  
Micro Edm ◽  
Impedance Variation ◽  
State Detection ◽  
Variation Characteristics ◽  
Edm Process

Abstract Aiming at the problem that it is difficult to detect the discharge state of narrow pulse width and small duty cycle pulse, this paper studies the variation characteristics of inter electrode impedance in micro-EDM process, puts forward a detection method to distinguish different discharge states in micro-EDM based on the change of electrical signal caused by the change characteristics. The influence of pulse width and duty cycle on the quality of micro-EDM is analyzed. The surface machining quality of workpiece can be improved obviously by compressing the pulse width of pulse power supply. And designs a discharge state detection system based on inter electrode impedance variation characteristics. Experiments verify the detection method, and test the machining effect of the control strategy through the trial machining experiment, which ensures that the micro-EDM process is efficient and stable.

scholarly journals A Lane Detection Method Based on Semantic Segmentation

2020 ◽  
Vol 122 (3) ◽  
pp. 1039-1053
Author(s):  
Ling Ding ◽  
Huyin Zhang ◽  
Jinsheng Xiao ◽  
Cheng Shu ◽  
Shejie Lu
Keyword(s):  
Detection Method ◽  
Semantic Segmentation ◽  
Lane Detection

scholarly journals Sliding Dispersion Entropy-Based Fault State Detection for Diaphragm Pump Parts

Coatings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1536
Author(s):  
Chengjiang Zhou ◽  
Yunhua Jia ◽  
Haicheng Bai ◽  
Ling Xing ◽  
Yang Yang
Keyword(s):  
Detection Method ◽  
Check Valve ◽  
Sliding Window ◽  
The State ◽  
Sigmoid Function ◽  
Noise Performance ◽  
Signal Distribution ◽  
Mechanical Parts ◽  
State Detection ◽  
Fault State

Aiming at the disadvantages of low trend, poor characterization performance, and poor anti-noise performance of traditional degradation features such as dispersion entropy (DE), a fault detection method based on sliding dispersion entropy (SDE) is proposed. Firstly, a sliding window is added to the signal before extracting the DE feature, and the root mean square of the signal inside the sliding window is used to replace the signal in the window to realize down sampling, which enhances the trend of DE. Secondly, the hyperbolic tangent sigmoid function (TANSIG) is introduced to map the signals to different categories when extracting the DE feature, which is more in line with the signal distribution of mechanical parts and the monotonicity of the degradation feature is improved. For noisy signal, the introduction of locally weighted scatterplot smoothing (LOWESS) can remove the burrs and fluctuations of the SDE curve, and the anti-noise performance of SDE is improved. Finally, the SDE state warning line is constructed based on the 2σ criterion, which can determine the fault warning point in time and effectively. The state detection results of bearing and check valve show that the proposed SDE improves the trend, monotonicity, and robustness of the state tracking curve, and provides a new method for fault state detection of mechanical parts.

scholarly journals An Efficient Pedestrian Detection Method Based on YOLOv2

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhongmin Liu ◽  
Zhicai Chen ◽  
Zhanming Li ◽  
Wenjin Hu
Keyword(s):  
Detection Method ◽  
Size Range ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Detection Model ◽  
Great Progress ◽  
Good Trade ◽  
Speed And Accuracy

In recent years, techniques based on the deep detection model have achieved overwhelming improvements in the accuracy of detection, which makes them being the most adapted for the applications, such as pedestrian detection. However, speed and accuracy are a pair of contradictions that always exist and have long puzzled researchers. How to achieve the good trade-off between them is a problem we must consider while designing the detectors. To this end, we employ the general detector YOLOv2, a state-of-the-art method in the general detection tasks, in the pedestrian detection. Then we modify the network parameters and structures, according to the characteristics of the pedestrians, making this method more suitable for detecting pedestrians. Experimental results in INRIA pedestrian detection dataset show that it has a fairly high detection speed with a small precision gap compared with the state-of-the-art pedestrian detection methods. Furthermore, we add weak semantic segmentation networks after shared convolution layers to illuminate pedestrians and employ a scale-aware structure in our model according to the characteristics of the wide size range in Caltech pedestrian detection dataset, which make great progress under the original improvement.

scholarly journals Ship Detection in Optical Satellite Images via Directional Bounding Boxes Based on Ship Center and Orientation Prediction

2019 ◽  
Vol 11 (18) ◽  
pp. 2173 ◽  
Author(s):  
Jinlei Ma ◽  
Zhiqiang Zhou ◽  
Bo Wang ◽  
Hua Zong ◽  
Fei Wu
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Detection Method ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Optical Remote Sensing ◽  
Ship Detection ◽  
Bounding Boxes ◽  
Typical Object ◽  
Optical Satellite Images

To accurately detect ships of arbitrary orientation in optical remote sensing images, we propose a two-stage CNN-based ship-detection method based on the ship center and orientation prediction. Center region prediction network and ship orientation classification network are constructed to generate rotated region proposals, and then we can predict rotated bounding boxes from rotated region proposals to locate arbitrary-oriented ships more accurately. The two networks share the same deconvolutional layers to perform semantic segmentation for the prediction of center regions and orientations of ships, respectively. They can provide the potential center points of the ships helping to determine the more confident locations of the region proposals, as well as the ship orientation information, which is beneficial to the more reliable predetermination of rotated region proposals. Classification and regression are then performed for the final ship localization. Compared with other typical object detection methods for natural images and ship-detection methods, our method can more accurately detect multiple ships in the high-resolution remote sensing image, irrespective of the ship orientations and a situation in which the ships are docked very closely. Experiments have demonstrated the promising improvement of ship-detection performance.

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