Accuracy Analysis of Centroid Detection Method Used to Star Sensor

2012 ◽  
Vol 468-471 ◽  
pp. 401-404 ◽  
Author(s):  
Qi Li ◽  
Wei Xu ◽  
Zhi Hai Xu ◽  
Hua Jun Feng
Keyword(s):  
Simulation Experiment ◽  
Detection Method ◽  
Detection Algorithm ◽  
Motion Blur ◽  
Detection Accuracy ◽  
Star Sensor ◽  
Detection Algorithms ◽  
Satellite Attitude ◽  
Centroid Detection ◽  
Acceptable Parameter

Star sensor is important equipment for measuring satellite attitude and motion, and star centroid detection accuracy is the basis of the overall accuracy of star sensor. In star sensors, slightly- defocus method is often adopted to acquire dispersive light spots so as to facilitate centroid detection, and certain motion blur can also be introduced because of the motion of satellites. In this paper, we analyzed several commonly-used centroid detection algorithms by using simulation experiment to study the influence of defocus and motion parameters on the accuracy of centroid detection algorithm and provided acceptable parameter value ranges.

Robot Collision Detection Without External Sensors Based on Time-Series Analysis

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Tie Zhang ◽  
Peizhong Ge ◽  
Yanbiao Zou ◽  
Yingwu He
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Collision Detection ◽  
Degrees Of Freedom ◽  
Detection Method ◽  
Industrial Robot ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
External Torque ◽  
Series Analysis

Abstract To ensure the human safety in the process of human–robot cooperation, this paper proposes a robot collision detection method without external sensors based on time-series analysis (TSA). In the investigation, first, based on the characteristics of the external torque of the robot, the internal variation of the external torque sequence during the movement of the robot is analyzed. Next, a time-series model of the external torque is constructed, which is used to predict the external torque according to the historical motion information of the robot and generate a dynamic threshold. Then, the detailed process of time-series analysis for collision detection is described. Finally, the real-machine experiment scheme of the proposed real-time collision detection algorithm is designed and is used to perform experiments with a six degrees-of-freedom (6DOF) articulated industrial robot. The results show that the proposed method helps to obtain a detection accuracy of 100%; and that, as compared with the existing collision detection method based on a fixed symmetric threshold, the proposed method based on TSA possesses smaller detection delay and is more feasible in eliminating the sensitivity difference of collision detection in different directions.

scholarly journals A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Liying Wang ◽  
Lei Shi ◽  
Liancheng Xu ◽  
Peiyu Liu ◽  
Lindong Zhang ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Computational Cost ◽  
Optimization Method ◽  
Detection Algorithm ◽  
Threshold Function ◽  
Detection Algorithms ◽  
Original Dataset ◽  
Data Clusters ◽  
High Computational Cost

Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough room for further improvements. To solve the above problems, our paper proposes a parameter-free outlier detection algorithm based on dataset optimization method. Firstly, we propose a dataset optimization method (DOM), which initializes the original dataset in which density is greater than a specific threshold. In this method, we propose the concepts of partition function (P) and threshold function (T). Secondly, we establish a parameter-free outlier detection method. Similarly, we propose the concept of the number of residual neighbors, as the number of residual neighbors and the size of data clusters are used as the basis of outlier detection to obtain a more accurate outlier set. Finally, extensive experiments are carried out on a variety of datasets and experimental results show that our method performs well in terms of the efficiency of outlier detection and time complexity.

scholarly journals TWO-LEAD JOINT DETECTION ALGORITHM OF QRS COMPLEXES IN ECG SIGNAL

2021 ◽  
Author(s):  
WANSONG XU ◽  
TIANWU CHEN ◽  
FANYU DU
Keyword(s):  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Joint Detection ◽  
Ecg Signals ◽  
Detection Algorithms ◽  
Threshold Adjustment ◽  
Current Algorithm ◽  
Electrocardiogram Ecg

Objective: The detection of QRS complexes is an important part of computer-aided analysis of electrocardiogram (ECG). However, most of the existing detection algorithms are mainly for single-lead ECG signals, which requires high quality of signal. If the signal quality decreases suddenly due to some interference, then the current algorithm is easy to cause misjudgment or missed detection. To improve the detection ability of QRS complexes under sudden interference, we study the QRS complexes information on multiple leads in-depth, and propose a two-lead joint detection algorithm of QRS complexes. Methods: Firstly, the suspected QRS complexes are screened on the main lead. For the suspected QRS complexes with low confidence and the complexes that may be missed, further accurate detection and joint judgment shall be carried out at the corresponding position of the auxiliary lead. At the same time, the adaptive threshold adjustment algorithm and backtracking mechanism are used to modify the detection results. Results: The proposed detection algorithm is validated using 48 ECG records of the MIT-BIH arrhythmia database, and achieves average detection accuracy of 99.71%, sensitivity of 99.88% and positive predictivity of 99.81%. Conclusion: The proposed algorithm has high accuracy, which can effectively deal with the sudden interference of ECG signal. Meanwhile, the algorithm requires small amount of computation, and can be embedded into hardware for real-time detection.

scholarly journals Accurate and Lightweight RailNet for Real-Time Rail Line Detection

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2038
Author(s):  
Zhen Tao ◽  
Shiwei Ren ◽  
Yueting Shi ◽  
Xiaohua Wang ◽  
Weijiang Wang
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Social Progress ◽  
Line Detection ◽  
Detection Accuracy ◽  
Railway Transportation ◽  
Deep Convolutional Neural Networks ◽  
Detection Algorithms ◽  
Rail Line ◽  
Binary Segmentation

Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, which takes full advantage of the powerful advanced semantic information extraction capabilities of deep convolutional neural networks to obtain high-level features of rail lines. The Segmentation Soul (SS) module is creatively added to the RailNet structure, which improves segmentation performance without any additional inference time. The Depth Wise Convolution (DWconv) is introduced in the RailNet to reduce the number of network parameters and eventually ensure real-time detection. Afterward, according to the binary segmentation maps of RailNet output, we propose the rail line fitting algorithm based on sliding window detection and apply the inverse perspective transformation. Thus the polynomial functions and curvature of the rail lines are calculated, and rail lines are identified in the original images. Furthermore, we collect a real-world rail lines dataset, named RAWRail. The proposed algorithm has been fully validated on the RAWRail dataset, running at 74 FPS, and the accuracy reaches 98.6%, which is superior to the current rail line detection algorithms and shows powerful potential in real applications.

scholarly journals An Improved Object Detection Method using Feature Map Refinement and Anchor Optimization

2021 ◽  
Vol 15 ◽  
pp. 541-549
Author(s):  
Yuxia Wang ◽  
Wenzhu Yang ◽  
Tongtong Yuan ◽  
Qian Li
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Search Space ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Semantic Features ◽  
Feature Map ◽  
Input Size ◽  
Fusion Operation ◽  
Different Levels

Lower detection accuracy and insufficient detection ability for small objects are the main problems of the region-free object detection algorithm. Aiming at solving the abovementioned problems, an improved object detection method using feature map refinement and anchor optimization is proposed. Firstly, the reverse fusion operation is performed on each of the object detection layer, which can provide the lower layers with more semantic information by the fusion of detection features at different levels. Secondly, the self-attention module is used to refine each detection feature map, calibrates the features between channels, and enhances the expression ability of local features. In addition, the anchor optimization model is introduced on each feature layer associated with anchors, and the anchors with higher probability of containing an object and more closely match the location and size of the object are obtained. In this model, semantic features are used to confirm and remove negative anchors to reduce search space of the objects, and preliminary adjustments are made to the locations and sizes of anchors. Comprehensive experimental results on PASCAL VOC detection dataset demonstrate the effectiveness of the proposed method. In particular, with VGG-16 and lower dimension 300×300 input size, the proposed method achieves a mAP of 79.1% on VOC 2007 test set with an inference speed of 24.7 milliseconds per image.

scholarly journals A Vertex Concavity-Convexity Detection Method for Three-Dimensional Spatial Objects Based on Geometric Algebra

2020 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Pengcheng Yin ◽  
Jiyi Zhang ◽  
Xiying Sun ◽  
Di Hu ◽  
Zhifeng Shi ◽  
...  
Keyword(s):  
Geometric Algebra ◽  
Detection Method ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Basic Algorithm ◽  
Outer Product ◽  
Spatial Objects ◽  
Detection Algorithms ◽  
Product Operation ◽  
Object Based

Vertex concavity-convexity detection for spatial objects is a basic algorithm of computer graphics, as well as the foundation for the implementation of other graphics algorithms. In recent years, the importance of the vertex concavity-convexity detection algorithm for three-dimensional (3D) spatial objects has been increasingly highlighted, with the development of 3D modeling, artificial intelligence, and other graphics technologies. Nonetheless, the currently available vertex concavity-convexity detection algorithms mostly use two-dimensional (2D) polygons, with limited research on vertex concavity-convexity detection algorithms for 3D polyhedrons. This study investigates the correlation between the outer product and the topology of the spatial object based on the unique characteristic that the outer product operation in the geometric algebra has unified and definitive geometric implications in space, and with varied dimensionality. Moreover, a multi-dimensional unified vertex concavity-convexity detection algorithm framework for spatial objects is proposed, and this framework is capable of detecting vertex concavity-convexity for both 2D simple polygons and 3D simple polyhedrons.

An Effective Method for Defects Detection in Radiographic Images of Welds Based on Edge Detection and Morphology

2013 ◽  
Vol 290 ◽  
pp. 71-77
Author(s):  
Wen Ming Guo ◽  
Yan Qin Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Radiographic Images ◽  
X Ray ◽  
Weld Defects ◽  
Detection Algorithms ◽  
Detection Quality

In the current industrial production, as steel weld X-ray images are low contrasted and noisy, the efficiency and precision can’t be both ensured. This paper has studied three different edge detection algorithms and found the most suitable one to detect weld defects. Combined with this edge detection algorithm, we proposed a new weld defects detection method. This method uses defect features to find the defects in edge images with morphological processing. Compared to the traditional methods, the method has ensured detection quality of weld defects detection.

scholarly journals A New Semantic-Based Tool Detection Method for Robots

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Wenbai Chen ◽  
Chao He ◽  
Chen W.Z. ◽  
Chen Q.L. ◽  
Wu P.L.
Keyword(s):  
Detection Method ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Recognition Ability ◽  
Home Helper ◽  
Point Detection ◽  
Functional Components ◽  
Instance Segmentation ◽  
Better Than

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms.

scholarly journals Research on insulator defect detection algorithm of transmission line based on CenterNet

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255135
Author(s):  
Chunming Wu ◽  
Xin Ma ◽  
Xiangxu Kong ◽  
Haichao Zhu
Keyword(s):  
Transmission Line ◽  
Defect Detection ◽  
Detection Method ◽  
Detection Efficiency ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Detection Algorithm ◽  
Electric Power System ◽  
Stable Operation ◽  
Detection Accuracy

The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.

scholarly journals DEFECTS DETECTION METHOD BASED ON K-MEANS WITH PRIOR KNOWLEDGE FOR BIOMASS PARTICLES

2020 ◽  
Vol 61 (2) ◽  
pp. 225-232 ◽  
Author(s):  
Wei Wang ◽  
Yuan Juan Gong
Keyword(s):  
Prior Knowledge ◽  
Detection Method ◽  
Vision System ◽  
Detection Algorithm ◽  
Biomass Energy ◽  
Detection Accuracy ◽  
Detection Time ◽  
Region Detection ◽  
High Detection ◽  
Biomass Particles

Biomass particle is one of the most important solid briquette fuels for agricultural and forestry biomass energy. Temperature, pressure, moisture and discharge holes are important factors to control biomass particle forming. The inappropriate setting of the parameters or blocking of the discharge hole will lead to the defects of the biomass particles, such as too short or poor roundness or pits or cracks. In order to detect these defects automatically, this paper proposes a method based on K-Means with prior knowledge. Firstly, the inner boundary tracking region detection algorithm and filling algorithm are combined to extract the regions in the backlight image. The regions are divided into debris, independent biomass particle regions and adhesive biomass particle regions. Secondly, K-Means with prior knowledge is used to segment the adhesive regions to get the independent biomass particle regions. Finally, the features of the biomass particles are extracted to judge the type of defects. The proposed method has been tested on images acquired from the vision system of the ring roller pellet mill. Experimental results show the efficiency of the proposed method in high detection accuracy and short detection time.

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