An Automatic Detection Algorithm of Metro Passenger Boarding and Alighting Based on Deep Learning and Optical Flow

2021 ◽  
pp. 1-1
Author(s):  
Quanli Liu ◽  
Qiang Guo ◽  
Wei Wang ◽  
Yuanqing Zhang ◽  
Qiang Kang
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Automatic Detection Algorithm

scholarly journals Deep Learning–based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs

Radiology ◽  
2020 ◽  
Vol 296 (3) ◽  
pp. 652-661 ◽  
Author(s):  
Sowon Jang ◽  
Hwayoung Song ◽  
Yoon Joo Shin ◽  
Junghoon Kim ◽  
Jihang Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Chest Radiographs ◽  
Lung Cancers ◽  
Automatic Detection Algorithm

Automatic Detection Algorithm for Controlled Items Based on Deep Learning

2019 ◽  
Vol 56 (18) ◽  
pp. 180402 ◽  
Author(s):  
吉祥凌 Xiangling Ji ◽  
吴军 Jun Wu ◽  
易见兵 Jianbing Yi ◽  
张晓光 Xiaoguang Zhang
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Automatic Detection Algorithm

scholarly journals Deep Learning-Based Congestion Detection at Urban Intersections

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2052
Author(s):  
Xinghai Yang ◽  
Fengjiao Wang ◽  
Zhiquan Bai ◽  
Feifei Xun ◽  
Yulin Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Traffic Congestion ◽  
Detection Algorithm ◽  
Input Image ◽  
Vehicle Speed ◽  
Position Information ◽  
Traffic State ◽  
State Discrimination ◽  
Discrimination Method

In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements.

Automatic Detection of Corrugation: Preliminary Results in the Dutch Network Using Axle Box Acceleration Measurements

2015 ◽  
Author(s):  
Shaoguang Li ◽  
Alfredo Núñez ◽  
Zili Li ◽  
Rolf Dollevoet
Keyword(s):  
Automatic Detection ◽  
Detection Algorithm ◽  
System Response ◽  
Growth Mechanisms ◽  
Preliminary Results ◽  
Rail Network ◽  
Track System ◽  
Good Potential ◽  
Automatic Detection Algorithm ◽  
Wheel Track

Short pitch corrugation is commonly seen in all kinds of tracks. There is not yet a conclusive explanation in the literature for its initiation and growth mechanisms. In this paper, we use an axle box acceleration (ABA) measurement system to detect corrugation. ABA can be easily implemented in operational trains, providing direct and reliable health monitoring of the track. We have extended a detection algorithm for rail surface local short wavelength defects to also detect short pitch corrugation, which is a continuous defect over the track. A 3D transient FE wheel-track model is employed to find theoretical signature tunes of the wheel-track system response when passing over a short pitch corrugation. Numerical simulations agree with ABA measurement obtained in the Dutch rail network. Based on the signature tune identified, an automatic detection algorithm is developed. Preliminary results with the algorithm are discussed. Field observations show a good potential of the detection algorithm to be used by inframanagers, to detect and monitor corrugation.

scholarly journals EP-1592: Automatic detection algorithm for MLC position using a single EPID image in a daily QA program

2016 ◽  
Vol 119 ◽  
pp. S740
Author(s):  
P. Colleoni ◽  
A. Gambirasio ◽  
C. Bianchi ◽  
M. Fortunato ◽  
S. Andreoli
Keyword(s):  
Automatic Detection ◽  
Detection Algorithm ◽  
Automatic Detection Algorithm
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