scholarly journals Trichomonas Detection in Leucorrhea Based on VIBE Method

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Xiaohui Du ◽  
Lin Liu ◽  
Xiangzhou Wang ◽  
Jing Zhang ◽  
Guangming Ni ◽  
...  
Keyword(s):  
Detection System ◽  
Ground Truth ◽  
Correct Classification ◽  
Improved Method ◽  
False Detection ◽  
Still Images ◽  
Model Update ◽  
Motion Characteristic ◽  
Automatic Detection System ◽  
Vibe Algorithm

Trichomonas examination is one of the important items in the leucorrhea routine detection. And it cannot be recognized by still images because of the unstable morphology and unfixed focal location caused by motion characteristic. We proposed an improved VIBE algorithm. 6 videos (totally 1414 frames) are collected for testing. In order to compare the effects of the algorithms, we segment each frame artificially as ground truth. Experiments show that percentage of correct classification (PCC) achieves 88%. The proposed improved method can effectively suppress the false detection caused by the formed components such as epithelial cells in the leucorrhea microscopic image and the missed detection caused by the background model update during the movement. At the same time, improvements can effectively suppress smear and ghost areas. The algorithm proposed in this paper can be integrated into the leucorrhea automatic detection system.

Survey on Safety Devices Using IoT

2020 ◽  
pp. 450-453
Keyword(s):  
Traffic Congestion ◽  
Detection System ◽  
Text Message ◽  
Emergency Situation ◽  
Heart Beat ◽  
Advanced Technology ◽  
Night Time ◽  
Remote Areas ◽  
Alert Message ◽  
Automatic Detection System

Traffic congestion is becoming a huge problem, which is arising due to vehicle failure or accidents. Transportation and use of advanced technology has great importance in society and that has made many of our lives much easier. By automatic accident detection and alerting GSM & GPS based technology can be used to overcome these problems. Where as in case of Child and Women there are very few efficient security and safety measures adopted. Now in India the safety for women has become a major issue while travelling. Nowadays women think twice before taking any steps out of their homes, especially in the night time. Hence, this is unfortunately, the sad reality of our country and also due to various crimes like child abuse, rape, dowry deaths, trafficking and many more. At the time of women facing unsecured situations, there is a need to ensure safety while travelling. Hence automatic detection system needs to be established where one can send alert message to the police station or the relatives which detects the current location of the required ones by use of such technologies the women and children can get protection. Mainly in remote areas children use bicycles as means of transport from several years and nowadays, despite due to the large vailability of new and faster means, the bicycle users is not decreased. Despites the cyclists find difficult to travel within them and other vehicles find difficult to find them during night time. In case of any emergency situation faced at unknown remote areas the cyclist can send their location to required ones to help them. In this paper, report the survey on the existing mechanism for detecting locations, and sending signals and to collect parameters such as temperature of the human body, heart beat etc. using sensors. With the help of GPS and GSM we can track the location of the child, women or vehicle. Hence, by these we can save the life of person’s being injured in various locations by sending a text message using IOT technologies

scholarly journals Research on automatic detection system of encoder accuracy based on PID algorithm

2021 ◽  
Vol 1754 (1) ◽  
pp. 012233
Author(s):  
Han Hou ◽  
Guohua Cao ◽  
Hongchang Ding ◽  
Changfu Zhao ◽  
Aijia Wang
Keyword(s):  
Detection System ◽  
Automatic Detection ◽  
Pid Algorithm ◽  
Automatic Detection System

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

Deep learning for automated detection and numbering of permanent teeth on panoramic images

2021 ◽  
Author(s):  
Mohamed Estai ◽  
Marc Tennant ◽  
Dieter Gebauer ◽  
Andrew Brostek ◽  
Janardhan Vignarajan ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Detection System ◽  
Ground Truth ◽  
Automated Detection ◽  
Permanent Teeth ◽  
Automated Method ◽  
Step Procedure ◽  
Panoramic Images ◽  
Tooth Number

Objective: This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs). Methods: In total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (i.e. sensitivity) were used as metrics to evaluate the performance of resultant CNNs. Results: The ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98. Conclusion: The resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine.

An ECG Automatic Detection System with Baseline Drift Removal Based on SG Filter

2021 ◽  
Author(s):  
Jiangbo Wei ◽  
Chenghao Zhang ◽  
Jiaji Ma ◽  
Zhihang Li ◽  
Maliang Liu
Keyword(s):  
Detection System ◽  
Automatic Detection ◽  
Baseline Drift ◽  
Automatic Detection System
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