scholarly journals Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning

2021 ◽  
Vol 11 (13) ◽  
pp. 6059
Author(s):  
Dahua Li ◽  
Weixuan Li ◽  
Xiao Yu ◽  
Qiang Gao ◽  
Yu Song
Keyword(s):  
Detection Algorithm ◽  
Concentric Circle ◽  
Support Line ◽  
Circle Detection ◽  
Multi Scale ◽  
Gauge Reading ◽  
Color Restoration ◽  
Automatic Reading ◽  
Inspection Robots ◽  
Dial Gauge

With the development of science and technology, inspection robots have attracted more and more attention, and research on the automatic reading of pointer instruments through inspection robots has become particularly valuable. Aiming at the problems of uneven illumination, complex dial background and damping fluid interference of the collected instrument images, this paper proposes a dial gauge reading algorithm based on coordinate positioning. First, the multi-scale retinex with color restoration (MSRCR) is applied to improve the uneven illumination of the image. Second, a circle detection algorithm based on the arc-support line segment is proposed to detect the disc to obtain the coordinate of the center and radius of the circle. Then, a pointerless template is used to obtain the pointer, and the concentric circle algorithm is applied to locate the refined pointer. Finally, the automatic reading is calculated using the relative position of the pointer and the zero scale. The experimental results prove that the proposed algorithm can accurately locate the center of the circle and the pointer and obtain readings automatically.

A Modified Multi Scale Retinex with Color Restoration Algorithm for Automatic Enhancement of Landsat-5 Remote Sensing Image

2011 ◽  
Vol 341-342 ◽  
pp. 893-897
Author(s):  
Gui Zhou Wang ◽  
Guo Jin He
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
Color Constancy ◽  
Human Perception ◽  
Histogram Equalization ◽  
Image Processing Algorithm ◽  
Multi Scale ◽  
Dynamic Range Compression ◽  
Restoration Algorithm ◽  
Color Restoration

The retinex is a human perception based image processing algorithm which provides color constancy and dynamic range compression. The multi scale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. But the MSRCR results suffer from lower global brightness and partial color distortion. In order to improve the MSRCR method, this paper presents a modified MSRCR algorithm to Landsat-5 image enhancement considering percent liner stretch and histogram adjustment. Finally, the effect of modified MSRCR method on Landsat-5 image enhancement is analyzed and the comparison with other color adjustment methods such as gamma correction and histogram equalization is reported in the experimental results.

Multi-scale Object Detection Algorithm in Smart City Based on Mixed Dilated Convolution Pyramid

2021 ◽  
Author(s):  
Kangning Yin ◽  
Jie Liang ◽  
Shaoqi Hou ◽  
Rui Zhu ◽  
Guangqiang Yin ◽  
...  
Keyword(s):  
Object Detection ◽  
Smart City ◽  
Detection Algorithm ◽  
Multi Scale ◽  
Dilated Convolution

scholarly journals A Novel Circle Detection Algorithm for Iris Segmentation

2013 ◽  
Vol 16 (12) ◽  
pp. 1385-1392
Author(s):  
Woong-Bae Yoon ◽  
Tae-Yun Kim ◽  
Ji-Eun Oh ◽  
Kwang Gi Kim
Keyword(s):  
Detection Algorithm ◽  
Circle Detection ◽  
Iris Segmentation

scholarly journals Pedestrian detection algorithm based on improved muti-scale feature fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012008
Author(s):  
Hui Liu ◽  
Keyang Cheng
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Data Sets ◽  
False Detection ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

Morphological edge detection algorithm based on hierarchical multi-scale

2021 ◽  
Author(s):  
Qi Tang ◽  
Yi-xuan Sun ◽  
Wen-tian Wang ◽  
Yi-zhou Jing ◽  
Chun-yan Li
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Multi Scale
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