scholarly journals Inspection Method of Rope Arrangement in the Ultra-Deep Mine Hoist Based on Optical Projection and Machine Vision

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1769
Author(s):  
Lixiang Shi ◽  
Jianping Tan ◽  
Shaohua Xue ◽  
Jiwei Deng
Keyword(s):  
Machine Vision ◽  
Spatial Clustering ◽  
Unsupervised Classification ◽  
Classification Problem ◽  
Detection Algorithm ◽  
Inspection Method ◽  
Deep Mine ◽  
Curve Detection ◽  
Mine Hoist ◽  
Speed Accuracy

Due to the importance of safety detection of the drum’s rope arrangement in the ultra-deep mine hoist and the current situation whereby the speed, accuracy and robustness of rope routing detection are not up to the requirements, a novel machine-vision-detection method based on the projection of the drum’s edge is designed in this paper. (1) The appropriate position of the point source corresponding to different reels is standardized to obtain better projection images. (2) The corresponding image processing and edge curve detection algorithm are designed according to the characteristics of rope arrangement projection. (3) The Gaussian filtering algorithm is improved to adapt to the situation that the curve contains wavelet peak noise when extracting the eigenvalues of the edge curve. (4) The DBSCAN (density-based spatial clustering of applications with noise) method is used to solve the unsupervised classification problem of eigenvalues of rope arrangement, and the distance threshold is calculated according to the characteristics of this kind of data. Finally, we can judge whether there is a rope arranging fault just through one frame and output the location and number of the fault. The accuracy and robustness of the method are verified both in the laboratory and the ultra-deep mine simulation experimental platform. In addition, the detection speed can reach 300 fps under the premise of stable detection.

A template-free machine vision-based crop row detection algorithm

2020 ◽  
Vol 22 (1) ◽  
pp. 124-153
Author(s):  
Saba Rabab ◽  
Pieter Badenhorst ◽  
Yi-Ping Phoebe Chen ◽  
Hans D. Daetwyler
Keyword(s):  
Machine Vision ◽  
Detection Algorithm ◽  
Template Free ◽  
Free Machine

Detection Algorithm for Crop Multi-Centerlines Based on Machine Vision

2008 ◽  
Vol 51 (3) ◽  
pp. 1089-1097 ◽  
Author(s):  
H. Zhang ◽  
B. Chen ◽  
L. Zhang
Keyword(s):  
Machine Vision ◽  
Detection Algorithm

Detection and Tracking Cows by Computer Vision and Image Classification Methods

2021 ◽  
Vol 13 (1) ◽  
pp. 1-45
Author(s):  
Terry Gao
Keyword(s):  
Feature Fusion ◽  
Binary Classification ◽  
Classification Problem ◽  
Detection Algorithm ◽  
Video Sequences ◽  
Image Block ◽  
Body Contour ◽  
Adaboost Algorithm ◽  
Target Model ◽  
Detection And Tracking

In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is trained by Gentle Adaboost algorithm. Experiments show that the method has good detection performance when the target has deformation or the contrast between target and background is low. Compared with the general target detection algorithm, this method reduces the miss rate and the detection precision is improved. Detection rate can reach 97.3%. In traction phase, the popular compressive tracking (CT) algorithm is proposed. The learning rate is changed through adaptively calculating the pap distance of image block. Moreover, the update for target model is stopped to avoid introducing error and noise when the classification response values are negative. The experiment results show that the improved tracking algorithm can effectively solve the target model update by mistaken when there are large covers or the attitude is changed frequently. For the detection and tracking of cow body, a detection and tracking framework for the image of cow is built and the detector is combined with the tracking framework. The algorithm test for some video sequences under the complex environment indicates the detection algorithm based on improved compressed perception shows good tracking effect in the changing and complicated background.

Human Detection Algorithm Optimization in Machine Vision

2020 ◽  
Vol 57 (10) ◽  
pp. 101006
Author(s):  
何倩倩 He Qianqian ◽  
张荣芬 Zhang Rongfen ◽  
刘宇红 Liu Yuhong
Keyword(s):  
Machine Vision ◽  
Detection Algorithm ◽  
Human Detection ◽  
Algorithm Optimization

Machine-Vision Based Defect Detection Algorithm for Packaging Bags

2019 ◽  
Vol 56 (9) ◽  
pp. 091501 ◽  
Author(s):  
李丹 Li Dan ◽  
白国君 Bai Guojun ◽  
金媛媛 Jin Yuanyuan ◽  
童艳 Tong Yan
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Detection Algorithm

INSPECTION AND QUALITY CHECKING OF CERAMIC CUP USING MACHINE VISION TECHNIQUE: DESIGN AND ANALYSIS

2017 ◽  
Vol 79 (5-2) ◽  
Author(s):  
Nursabillilah Mohd Ali ◽  
Mohd Safirin Karis ◽  
Siti Azura Ahmad Tarusan ◽  
Gao-Jie Wong ◽  
Mohd Shahrieel Mohd Aras ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Machine Vision ◽  
Image Sensor ◽  
Histogram Method ◽  
Inspection Method ◽  
Inspection Process ◽  
Set Up ◽  
Vision Application ◽  
Vision Technique

The development of inspection and quality checking using machine vision technique are discussed where the design of the algorithm mainly to detect the sign of defect when a sample product is used for inspection purposes. There are several constraints that a machine need to be improved based on technology used in vision application. CMOS image sensor as well as programming language and open source computer vision library were used in designing the inspection method. Experimental set-up was conducted to test the proposed technique for evaluate the effectiveness process. The experimental results were obtained and represented in graphical and image processing form. Besides, analysis and discussion were made according to obtained results. The proposed technique is able to perform the inspection process using good and defect ceramic cup based on detection technique. Moreover, based on the analysis gathered, the proposed technique able to differentiate between good and defect ceramic cup. The result shows that there is a difference frequency by 236 which is 2% of total value in pixels frequency. The frequency indicated as pixel frequency of image using histogram method based on scaled value of image.

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