scholarly journals Enhancing Railway Maintenance Safety Using Open-Source Computer Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Donghee Shin ◽  
Jangwon Jin ◽  
Jooyoung Kim
Keyword(s):  
Computer Vision ◽  
Social Issue ◽  
Monitoring System ◽  
High Speed ◽  
Image Data ◽  
Detection Algorithm ◽  
Prevention System ◽  
Maintenance Work ◽  
Tunnel Monitoring ◽  
Railway Maintenance

As high-speed railways continue to be constructed, more maintenance work is needed to ensure smooth operation. However, this leads to frequent accidents involving maintenance workers at the tracks. Although the number of such accidents is decreasing, there is an increase in the number of casualties. When a maintenance worker is hit by a train, it invariably results in a fatality; this is a serious social issue. To address this problem, this study utilized the tunnel monitoring system installed on trains to prevent railway accidents. This was achieved by using a system that uses image data from the tunnel monitoring system to recognize railway signs and railway tracks and detect maintenance workers on the tracks. Images of railway signs, tracks, and maintenance workers on the tracks were recorded through image data. The Computer Vision OpenCV library was utilized to extract the image data. A recognition and detection algorithm for railway signs, tracks, and maintenance workers was constructed to improve the accuracy of the developed prevention system.

scholarly journals An FPGA-Based Ultra-High-Speed Object Detection Algorithm with Multi-Frame Information Fusion

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3707 ◽  
Author(s):  
Xianlei Long ◽  
Shenhua Hu ◽  
Yiming Hu ◽  
Qingyi Gu ◽  
Idaku Ishii
Keyword(s):  
Object Detection ◽  
Information Fusion ◽  
High Speed ◽  
Clock Cycle ◽  
Image Data ◽  
Detection Algorithm ◽  
Support Vector ◽  
Fusion Model ◽  
Ultra High Speed ◽  
Field Programmable

An ultra-high-speed algorithm based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) for hardware implementation at 10,000 frames per second (FPS) under complex backgrounds is proposed for object detection. The algorithm is implemented on the field-programmable gate array (FPGA) in the high-speed-vision platform, in which 64 pixels are input per clock cycle. The high pixel parallelism of the vision platform limits its performance, as it is difficult to reduce the strides between detection windows below 16 pixels, thus introduce non-negligible deviation of object detection. In addition, limited by the transmission bandwidth, only one frame in every four frames can be transmitted to PC for post-processing, that is, 75% image information is wasted. To overcome the mentioned problem, a multi-frame information fusion model is proposed in this paper. Image data and synchronization signals are first regenerated according to image frame numbers. The maximum HOG feature value and corresponding coordinates of each frame are stored in the bottom of the image with that of adjacent frames’. The compensated ones will be obtained through information fusion with the confidence of continuous frames. Several experiments are conducted to demonstrate the performance of the proposed algorithm. As the evaluation result shows, the deviation is reduced with our proposed method compared with the existing one.

Application-oriented robotic vision – a review

Robotica ◽  
1984 ◽  
Vol 2 (1) ◽  
pp. 3-15 ◽  
Author(s):  
R. A. Jarvis
Keyword(s):  
Computer Vision ◽  
High Speed ◽  
Image Data ◽  
Vision Systems ◽  
Levels Of Abstraction ◽  
Biological Vision ◽  
Robotic Vision ◽  
Machine Perception ◽  
Wide Range ◽  
Industrial Operations

SUMMARYComputer Vision is essentially concerned with emulating the process of seeing, naturally manifested in various higher biological systems,1–4 on a computational apparatus, and is consequently part of the Artificial Intelligence field within the sub-category of Machine perception. Seeing has to do with making sense of image data acquired through an optical system and subsequently dealt with at increasing levels of abstraction and association with known facts about the world. The spectrum of interest in Computer Vision ranges from attempting to answer basic questions concerning the functionality of biological vision systems, particularly human, at one end, all the way to enhancing the reliability, speed and cost effectiveness of specific industrial operations, particularly component inspection and vision driven robotic manipulation. The main bulk of interest is in the middle, where the quest for generality pushes interest towards biological vision systems with their demonstrated effectiveness in a wide range of environments, some hostile, whilst the need for economic viability and timeliness in relation to particular application pushes interest towards finding workable algorithms which function reliably at high speed on affordable apparatus.This paper is addressed, in somewhat tutorial style, at clarifying, by examples of work in the area, the issues surrounding application oriented robotic vision systems, their assumptions, strengths, weaknesses and degree of generality, and at the same time putting them in the context of the overall field of Computer Vision. In addition, the paper points to directions of development which promise to provide powerful industrial vision tools at an acceptable price.

High-speed brightfield 3-D imaging and 3-D image analysis of thick and overlapped cell clusters in cytological preparations

1994 ◽  
Vol 52 ◽  
pp. 218-219
Author(s):  
Robert W. Mackin
Keyword(s):  
Image Analysis ◽  
High Speed ◽  
Three Dimensional ◽  
Image Data ◽  
Ccd Camera ◽  
Objective Lens ◽  
Array Processor ◽  
Vaginal Smears ◽  
Low Contrast ◽  
Computer Controlled

This paper presents two advances towards the automated three-dimensional (3-D) analysis of thick and heavily-overlapped regions in cytological preparations such as cervical/vaginal smears. First, a high speed 3-D brightfield microscope has been developed, allowing the acquisition of image data at speeds approaching 30 optical slices per second. Second, algorithms have been developed to detect and segment nuclei in spite of the extremely high image variability and low contrast typical of such regions. The analysis of such regions is inherently a 3-D problem that cannot be solved reliably with conventional 2-D imaging and image analysis methods.High-Speed 3-D imaging of the specimen is accomplished by moving the specimen axially relative to the objective lens of a standard microscope (Zeiss) at a speed of 30 steps per second, where the stepsize is adjustable from 0.2 - 5μm. The specimen is mounted on a computer-controlled, piezoelectric microstage (Burleigh PZS-100, 68/μm displacement). At each step, an optical slice is acquired using a CCD camera (SONY XC-11/71 IP, Dalsa CA-D1-0256, and CA-D2-0512 have been used) connected to a 4-node array processor system based on the Intel i860 chip.

Application of Mobile Internet to Railway Maintenance Work

2007 ◽  
Vol 48 (4) ◽  
pp. 202-206
Author(s):  
Norio SATO ◽  
Michiko NOZUE ◽  
Miki MIYASHITA ◽  
Makoto KIKUCHI
Keyword(s):  
Mobile Internet ◽  
Maintenance Work ◽  
Railway Maintenance

Patient Monitoring System using Computer Vision for Emotional Recognition and Vital Signs Detection

2020 ◽  
Author(s):  
Ahmad Anwar Zainuddin ◽  
Sakthyvell Superamaniam ◽  
Andrea Christella Andrew ◽  
Ramanand Muraleedharan ◽  
John Rakshys ◽  
...  
Keyword(s):  
Computer Vision ◽  
Monitoring System ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Emotional Recognition ◽  
Patient Monitoring System

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

scholarly journals High-Speed Target Detection Algorithm Based on Sparse Fourier Transform

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 37828-37836 ◽  
Author(s):  
Cunsuo Pang ◽  
Shengheng Liu ◽  
Yan Han
Keyword(s):  
Fourier Transform ◽  
Target Detection ◽  
High Speed ◽  
Detection Algorithm
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