A robust abnormal detection method for complex structures in UAV images

2018 ◽  
Author(s):  
Yu Zhao ◽  
Jun-ichiro Watanabe
Keyword(s):  
Detection Method ◽  
Complex Structures ◽  
Abnormal Detection ◽  
Uav Images

A Damage Detection Method: CMSE Combined with Niche GA

2007 ◽  
Vol 334-335 ◽  
pp. 929-932 ◽  
Author(s):  
Xu Ge ◽  
Yun Ju Yan ◽  
Huan Guo Chen
Keyword(s):  
Composite Material ◽  
Damage Detection ◽  
Composite Structures ◽  
Structural Damage ◽  
Strain Energy ◽  
Detection Method ◽  
Detection Methods ◽  
Damage Localization ◽  
Complex Structures ◽  
Modal Strain Energy

The paper presents an effective damage detection method of complex composite structures. It can be carried out through the experimental modal analysis of the damaged structure. The method using the improved Cross Modal Strain Energy (CMSE) technique and Niche GA has many advantages compared with other damage detection methods. The CMSE method can use any modes of the structure and the modes don’t need to be normalized or consistent in scale. The Niche GA improves the efficiency of the calculation and enhances the capacity of identifying structural damage localization. The model is the composite material airfoil case. The numerical results show that the method proposed in this paper is successful for damage detection of complex structures.

scholarly journals INCORRECT MATCH DETECTION METHOD FOR ARCTIC SEA-ICE RECONSTRUCTION USING UAV IMAGES

2018 ◽  
Vol XLII-2 ◽  
pp. 501-505 ◽  
Author(s):  
J.-I. Kim ◽  
H.-C. Kim
Keyword(s):  
Sea Ice ◽  
Detection Method ◽  
Arctic Sea Ice ◽  
Surface Model ◽  
Target Area ◽  
Incorrect Match ◽  
Arctic Sea ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Key Indicators

Shapes and surface roughness, which are considered as key indicators in understanding Arctic sea-ice, can be measured from the digital surface model (DSM) of the target area. Unmanned aerial vehicle (UAV) flying at low altitudes enables theoretically accurate DSM generation. However, the characteristics of sea-ice with textureless surface and incessant motion make image matching difficult for DSM generation. In this paper, we propose a method for effectively detecting incorrect matches before correcting a sea-ice DSM derived from UAV images. The proposed method variably adjusts the size of search window to analyze the matching results of DSM generated and distinguishes incorrect matches. Experimental results showed that the sea-ice DSM produced large errors along the textureless surfaces, and that the incorrect matches could be effectively detected by the proposed method.

scholarly journals A Trajectory Abnormal Detection Method Based on Segmentation and Clustering

2021 ◽  
Vol 2010 (1) ◽  
pp. 012188
Author(s):  
Wanting Qin ◽  
Jun Tang ◽  
Cong Lu ◽  
Songyang Lao
Keyword(s):  
Detection Method ◽  
Abnormal Detection

scholarly journals The Detection Method of Fire Abnormal Based on Directional Drilling in Complex Conditions of Mine

2018 ◽  
Vol 38 ◽  
pp. 01007
Author(s):  
Duan Huijun ◽  
Hao Shijun ◽  
Feng Jie
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Technical Support ◽  
Fire Prevention ◽  
Directional Drilling ◽  
Detection Problem ◽  
Abnormal Detection ◽  
Drilling Technology ◽  
Measurement While Drilling ◽  
Open Branch

In the light of more and more urgent hidden fire abnormal detection problem in complex conditions of mine, a method which is used directional drilling technology is put forward. The method can avoid the obstacles in mine, and complete the fire abnormal detection. This paper based on analyzing the trajectory control of directional drilling, measurement while drilling and the characteristic of open branch process, the project of the directional drilling is formulated combination with a complex condition mine, and the detection of fire abnormal is implemented. This method can provide technical support for fire prevention, which also can provide a new way for fire anomaly detection in the similar mine.

scholarly journals A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

Sensors ◽  
2016 ◽  
Vol 16 (8) ◽  
pp. 1325 ◽  
Author(s):  
Yongzheng Xu ◽  
Guizhen Yu ◽  
Yunpeng Wang ◽  
Xinkai Wu ◽  
Yalong Ma
Keyword(s):  
Detection Method ◽  
Vehicle Detection ◽  
Hybrid Vehicle ◽  
Uav Images
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