scholarly journals A fast X-corner detection method based on block-search strategy

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983414
Author(s):  
Cai Meng ◽  
Qi Wang ◽  
Lingjie Wu ◽  
Shaoya Guan ◽  
Yao Wu ◽  
...  
Keyword(s):  
Search Strategy ◽  
Detection Method ◽  
Corner Detection ◽  
Image Size ◽  
Image Block ◽  
Fast Detection ◽  
Sampling Window ◽  
Sampling Windows ◽  
Searching Method ◽  
Detection Speed

In order to detect X-corner (or X-point) features more accurately and apace, this article presents a novel and fast detection method based on block-by-block search strategy. Unlike general pixel-by-pixel searching method, the sampling window is first moved along the image block-by-block to find the X-corner candidates rapidly keeping in view the four-step and min-step-distance constraints. During the motion, some overlap is kept between the adjacent sampling windows in order to ensure that all X-corners could have a chance to reside inside, avoiding the possibility of that some X-corners may locate on the edge. Moreover, labeling technology is adopted to prevent duplicate candidates. After the collection of X-corner candidates, the neighborhood variance and centrosymmetry constraints are used to exclude outliers, and the intersection lines is calculated as the sub-pixel position of true X-corner. The experimental results using synthetic and real images show that the presented method approximately takes just about 13 ms to detect 52 X-corners in an image size of 1024 × 768 on a computer having Intel Core i3 CPU at 3.6 GHz and 4GB RAM. The proposed method has faster detection speed compared with the latest methods such as ChESS, SC, and Micron Tracker system while possessing the same or higher detection precision.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

Fast Non-Destructive Detection of Wool Content in Blended Fabrics Based on Near-Infrared Spectroscopy Technology

2015 ◽  
Vol 671 ◽  
pp. 356-362 ◽  
Author(s):  
Zhi Feng Chen ◽  
Yuan Quan Hong ◽  
Chang Jiang Wan ◽  
Lian Ying Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Detection Method ◽  
Quantitative Model ◽  
National Standard ◽  
Fast Detection ◽  
Significant Difference ◽  
Validation Set ◽  
Non Destructive

A fast non-destructive method of detection of wool content in blended fabrics was studied based on Near Infrared spectroscopy technology in order to avoid the time-consuming, tedious work and the destruction of samples in the traditional inspection. 621 wool/nylon, wool/polyester and wool/nylon/polyester blended fabrics were taken as research objects. To get the wool content, we established the wool near-infrared quantitative model by partial least squares (PLS) method after analyzing the color and composition of the samples. For verifying the validity and practicability of the model, 100 samples were chosen as an independent validation set. The variance analysis shows that there is no significant difference between Near Infrared fast detection method and national standard method (GB/T2910-2009),which indicates that this method is expected to be a means of fast non-destructive detection and will have extensive application future in the field of wool content detection.

scholarly journals Efficient 3D stereo vision stabilization for multi-camera viewpoints

2019 ◽  
Vol 8 (3) ◽  
pp. 882-889
Author(s):  
Sharif Shah Newaj Bhuiyan ◽  
Othman O. Khalifa
Keyword(s):  
Stereo Vision ◽  
Linear Order ◽  
Spanning Tree ◽  
Detection Method ◽  
Corner Detection ◽  
Geometric Transformation ◽  
Image Stabilization ◽  
Tree Algorithm ◽  
Stabilization Process ◽  
Key Points

In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method. Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.

Multi-feature SUSAN corner detection method

2005 ◽  
Author(s):  
Xun Wang ◽  
Jianqiu Jin ◽  
Yun Ling ◽  
Zhaoyi Jiang
Keyword(s):  
Detection Method ◽  
Corner Detection

Rapid Detection of Chloramphenicol Residues in Fermented Foods Based on UPLC-DAD

2021 ◽  
Vol 7 (6) ◽  
pp. 6303-6316
Author(s):  
Weixi Gao ◽  
Yan Zhuang
Keyword(s):  
Rapid Detection ◽  
Detection Method ◽  
Original Method ◽  
Fermented Food ◽  
Fermented Foods ◽  
Test Paper ◽  
Set Up ◽  
Test Process ◽  
Detection Reagent ◽  
Detection Speed

in the detection of chloramphenicol residues in fermented food, there are often problems of slow detection speed. Using UPLC-DAD method, a rapid detection method of chloramphenicol residues in fermented food based on UPLC-DAD method is designed. According to the characteristics of chloramphenicol, set up the detection reagent, select the detection equipment, and form the detection laboratory. It is usingUPLC-DAD method to design the test paper, using the set test reagent to deal with the sample to be tested, according to the design results of the test process, combining the reagent with the sample, to determine its specificity. Chloramphenicol residue was detected by test paper. So far, the rapid detection method of chloramphenicol residues in fermented food based on UPLC-DAD method has been designed. Compared with the original detection method, the detection speed of the detection method designed in this paper is significantly higher than the original method. In conclusion, the rapid detection method of chloramphenicol residues in fermented food based on UPLC-DAD method is effective.

scholarly journals ISSD: Improved SSD for Insulator and Spacer Online Detection Based on UAV System

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6961
Author(s):  
Xuan Liu ◽  
Yong Li ◽  
Feng Shuang ◽  
Fang Gao ◽  
Xiang Zhou ◽  
...  
Keyword(s):  
Capture Rate ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Single Shot ◽  
Online Detection ◽  
Feature Maps ◽  
Fast Detection ◽  
Model Size ◽  
Small Model ◽  
Detection Speed

In power inspection tasks, the insulator and spacer are important inspection objects. UAV (unmanned aerial vehicle) power inspection is becoming more and more popular. However, due to the limited computing resources carried by a UAV, a lighter model with small model size, high detection accuracy, and fast detection speed is needed to achieve online detection. In order to realize the online detection of power inspection objects, we propose an improved SSD (single shot multibox detector) insulator and spacer detection algorithm using the power inspection images collected by a UAV. In the proposed algorithm, the lightweight network MnasNet is used as the feature extraction network to generate feature maps. Then, two multiscale feature fusion methods are used to fuse multiple feature maps. Lastly, a power inspection object dataset containing insulators and spacers based on aerial images is built, and the performance of the proposed algorithm is tested on real aerial images and videos. Experimental results show that the proposed algorithm can efficiently detect insulators and spacers. Compared with existing algorithms, the proposed algorithm has the advantages of small model size and fast detection speed. The detection accuracy can achieve 93.8%. The detection time of a single image on TX2 (NVIDIA Jetson TX2) is 154 ms and the capture rate on TX2 is 8.27 fps, which allows realizing online detection.

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