scholarly journals Research on a Sliding Detection Method for an Elevator Traction Wheel Based on Machine Vision

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1158
Author(s):  
Jiayan Chen ◽  
Limeng Jing ◽  
Tao Hong ◽  
Hui Liu ◽  
Adam Glowacz
Keyword(s):  
Machine Vision ◽  
Detection Method ◽  
Region Of Interest ◽  
Absolute Error ◽  
Wire Rope ◽  
Geometric Transformation ◽  
Signal Processing Algorithm ◽  
Centroid Method ◽  
Object Distance ◽  
The Wire

To solve the problem that the elevator traction wheel slippage is difficult to detect quantitatively, a slippage detection method is proposed based on machine vision. The slip between the traction wheel and the wire rope will occur during the round-trip operation of the elevator, the displacement distance between the traction wheel and the wire rope in the circumferential direction is obtained through the image signal processing algorithm and related data analysis. First, the ROI (region of interest) of the collected original image is selected to reduce redundant information. Then, a nonlinear geometric transformation is carried out to transform the image into the target image with an equal object distance. Finally, the centroid method is used to obtain the slippage between the traction wheel and the wire rope. The field test results show that the absolute error of the system developed in this paper is 0.74 mm and the relative error is 2%, the extending uncertainty of the slip detection results is (33.8 ± 0.69) mm, the confidence probability is p = 0.95, and the degree of freedom is v = 8, which can meet accuracy requirements of elevator maintenance.

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

Detection Method of Optical Fiber Connector Internal Parts Based on Machine Vision

2014 ◽  
Vol 568-570 ◽  
pp. 483-488 ◽  
Author(s):  
Bao Hua Shi ◽  
Ya Hui Wei
Keyword(s):  
Image Analysis ◽  
Machine Vision ◽  
Optical Fiber ◽  
Least Squares ◽  
Detection Method ◽  
Least Squares Method ◽  
Detection Threshold ◽  
Absolute Error ◽  
Optical Fiber Connector ◽  
Outside Diameter

Technology of machine vision is used to measure the inside and outside diameter and concentricity of the optical fiber connector internal parts without contact. The image is got by million-pixel industrial camera. Then the image gets pretreatment, such as, grayscale transformation, binarization, smoothing, etc. Appropriate detection threshold is found by the image analysis. The edge of parts is found by the circular probe method. Inside and outside diameter and concentricity of parts are obtained by using the edge of the data through the least squares method. Experiment of 6.4 mm diameter parts, absolute error is less than one pixel. The largest error is less than 0.05 mm compared with the manual measurements and can meet the measurement requirements.

scholarly journals Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Jiwei Fan ◽  
Xiaogang Yang ◽  
Ruitao Lu ◽  
Xueli Xie ◽  
Weipeng Li
Keyword(s):  
Deep Learning ◽  
Autonomous Navigation ◽  
Detection Method ◽  
Active Role ◽  
Absolute Error ◽  
Face Mask ◽  
Learning Technology ◽  
Flight System ◽  
Crowd Density ◽  
The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

Arrangement Characteristics and Wear Distribution of Wire Rope in Parallel Grooved Multi-Layer Winding

2013 ◽  
Vol 423-426 ◽  
pp. 842-845 ◽  
Author(s):  
Zhi Hui Hu ◽  
Yong Hu ◽  
Ji Quan Hu
Keyword(s):  
Experimental Study ◽  
Wire Rope ◽  
Wire Ropes ◽  
The Wire ◽  
Wear Damage

Based on the analysis of multi-layer winding arrangement characteristic of the wire rope in Lebus drum, the experimental study is carried on wear distribution of the wire rope in parallel grooved multi-layer winding. The result shows that, the wire rope is arranged regularly in each drum area in parallel grooved multi-layer winding; the wear of wire ropes in crossover zone is more serious than that of the parallel zone; in the same-layer wire rope winding in crossover zone, the wear damage during the wire rope winding in crossover zone at the end of each-layer drum is the most serious.

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

scholarly journals Aplikasi Machine Vision pada Hexacopter untuk Deteksi Survival Kits di Bidang Mitigasi Bencana

2020 ◽  
Vol 12 (2) ◽  
pp. 72-79
Author(s):  
Ismawan Noor Ikhsan ◽  
Son Ali Akbar
Keyword(s):  
Machine Vision ◽  
Color Space ◽  
Region Of Interest ◽  
Contour Detection ◽  
Segmentation Method ◽  
Smart System ◽  
Hsv Color Space ◽  
Aerial Vehicle ◽  
Detection Evaluation ◽  
Flying Robots

Hexacopter belongs to one of flying robots that is used to carry out a special mission such as retrieving and delivering survival kits object. Thus, it should be built by smart system to determine the object accurately. However, there was an interference from other object that made it difficult to recognize the survival kits object. Therefore, the development of machine vision with the integration of the hexacopter control system is expected to improve the object recognition process. This study intends to develop a survival kit detection using the image processing method, which involved 1) segmentation on the Hue, Saturation, Value (HSV) color space, 2) contour detection, and 3) Region of Interest (ROI) selected detection. The evaluation of the segmentation method performances was done through the three-part experiments (i.e., the similar shape, variety of a color object, and an object shape). The result of survival kits object detection evaluation obtained an accuracy of 90.33%, precision of 99.63%, and recall of 91.24%. According to the performances obtained in this study, the development of machine vision systems on Unmanned Aerial Vehicle (UAV) has a high accuracy for the object survival kits detection even with another object interference.

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