High-Precision Atmospheric Disturbance Detection Method for Moving Objects

2021 ◽  
Vol 41 (2) ◽  
pp. 0228002
Author(s):  
张月 Zhang Yue ◽  
王旭 Wang Xu ◽  
苏云 Su Yun ◽  
张学敏 Zhang Xuemin ◽  
邬志强 Wu Zhiqiang ◽  
...  
Keyword(s):  
High Precision ◽  
Moving Objects ◽  
Detection Method ◽  
Atmospheric Disturbance ◽  
Disturbance Detection

scholarly journals Towards a Fully Automated 3D Reconstruction System Based on LiDAR and GNSS in Challenging Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1981
Author(s):  
Ruike Ren ◽  
Hao Fu ◽  
Hanzhang Xue ◽  
Zhenping Sun ◽  
Kai Ding ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Precision ◽  
Moving Objects ◽  
Autonomous Driving ◽  
Satellite System ◽  
Factor Graph ◽  
Mapping System ◽  
Global Navigation ◽  
Real World Datasets ◽  
Global Navigation Satellite

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision.

High precision detection method of safety helmet based on convolution neural network

2021 ◽  
Vol 36 (7) ◽  
pp. 1018-1026
Author(s):  
Tian-yu LI ◽  
◽  
Dong LI ◽  
Ming-ju CHEN ◽  
Hao WU ◽  
...  
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Detection Method ◽  
Convolution Neural Network

scholarly journals Moving Objects Detection & Recognition using Hybrid Canny Edge Detection Algorithm in Digital Image Processing

2019 ◽  
Vol 8 (9S3) ◽  
pp. 56-60
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Moving Objects Detection ◽  
Canny Edge ◽  
Objects Detection ◽  
Canny Edge Detection Algorithm ◽  
Day By Day

Recognition and detection of an object in the watched scenes is a characteristic organic capacity. Animals and human being play out this easily in day by day life to move without crashes, to discover sustenance, dodge dangers, etc. Be that as it may, comparable PC techniques and calculations for scene examination are not all that direct, in spite of their exceptional advancement. Object detection is the process in which finding or recognizing cases of articles (for instance faces, mutts or structures) in computerized pictures or recordings. This is the fundamental task in computer. For detecting the instance of an object and to pictures having a place with an article classification object detection method usually used learning algorithm and extracted features. This paper proposed a method for moving object detection and vehicle detection.

scholarly journals Improved High Precision Aircraft Target Detection Method of YOLT

2021 ◽  
Vol 1955 (1) ◽  
pp. 012028
Author(s):  
Jiaxing Mao ◽  
Xinyu Zhang ◽  
Yang Ji ◽  
Zhen Zhang ◽  
Zihao Guo
Keyword(s):  
Target Detection ◽  
High Precision ◽  
Detection Method

Disturbance Detection Method of Power Quality Based on Mathematical Morphology and Lifting Wavelet

2014 ◽  
Vol 644-650 ◽  
pp. 1205-1208 ◽  
Author(s):  
Xiao Fei Wang ◽  
Xia Chen ◽  
Ning Xia Yang ◽  
Xiao Jing Zhang ◽  
Ya Chao Hu
Keyword(s):  
Power Quality ◽  
Mathematical Morphology ◽  
Impulse Noise ◽  
Detection Method ◽  
Random Noise ◽  
Quality Detection ◽  
Lifting Wavelet ◽  
Simulation Results ◽  
Disturbance Detection ◽  
Detection Principle

Considering in the progress of the power quality detection for disturbance signal, detected signals are often has the problem of random noise and impulse noise, in this paper, by using traditional wavelet de-noising, put forward a detection method of disturbance signal based on mathematical morphology and lifting wavelet, discussed its detection principle and implementation methods. Simulation results show that the method de-noising effect is obvious, can effectively improve the accuracy of the disturbance signal judgment.

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