scholarly journals Real-time object detection technology in railway operations

2021 ◽  
pp. 154-162
Author(s):  
Rock K C Ho ◽  
Zhangyu Wang ◽  
Simon S C Tang ◽  
Qiang Zhang
Keyword(s):  
Object Detection ◽  
Real Time ◽  
High Performance ◽  
New Technology ◽  
Practical Application ◽  
Data Set ◽  
Detection Technology ◽  
Railway Industry ◽  
Extreme Scenario ◽  
Typical Scenario

Development of new technology to enhance train operability, in particular during manual driving by real-time object detection on track, is one of the rising trends in the railway industry. The function of object detection can provide train operators with reminder alerts whenever there is an object detected close to a train, e.g. a defined distance from the train. In this paper, a two-stage vision-based method is proposed to achieve this goal. At first, the Targets Generation Stage focuses on extracting all potential targets by identifying the centre points of targets. Meanwhile, the Targets Reconfirmation Stage is further adopted to re-analyse the potential targets from the previous stage to filter out incorrect potential targets in the output. The experiment and evaluation result shows that the proposed method achieved an Average Precision (AP) of 0.876 and 0.526 respectively under typical scenario sub-groups and extreme scenario sub-groups of the data set collected from a real railway environment at the methodological level. Furthermore, at the application level, high performance with the False Alarm Rate (FAR) of 0.01% and Missed Detection Rate (MDR) of 0.94%, which is capable of practical application, was achieved during the operation in the Tsuen Wan Line (TWL) in Hong Kong.

scholarly journals Design of a Scalable and Fast YOLO for Edge-Computing Devices

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6779
Author(s):  
Byung-Gil Han ◽  
Joon-Goo Lee ◽  
Kil-Taek Lim ◽  
Doo-Hyun Choi
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Processing Speed ◽  
Edge Computing ◽  
Light Weight ◽  
Detection Technology ◽  
Accuracy Performance ◽  
Speed And Accuracy

With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% [email protected] in the MS COCO dataset than YOLOv4-tiny model.

scholarly journals Monitoring and characterization of vibration and shock conditions aboard high-performance marine craft

2018 ◽  
Vol 233 (4) ◽  
pp. 1068-1081
Author(s):  
Manudul Pahansen de Alwis ◽  
Karl Garme
Keyword(s):  
Real Time ◽  
High Speed ◽  
High Performance ◽  
Severity Index ◽  
Irregular Waves ◽  
Vibration Exposure ◽  
Data Set ◽  
Real Time Analysis ◽  
Impact Acceleration ◽  
The Impact

The stochastic environmental conditions together with craft design and operational characteristics make it difficult to predict the vibration environments aboard high-performance marine craft, particularly the risk of impact acceleration events and the shock component of the exposure often being associated with structural failure and human injuries. The different timescales and the magnitudes involved complicate the real-time analysis of vibration and shock conditions aboard these craft. The article introduces a new measure, severity index, indicating the risk of severe impact acceleration, and proposes a method for real-time feedback on the severity of impact exposure together with accumulated vibration exposure. The method analyzes the immediate 60 s of vibration exposure history and computes the severity of impact exposure as for the present state based on severity index. The severity index probes the characteristic of the present acceleration stochastic process, that is, the risk of an upcoming heavy impact, and serves as an alert to the crew. The accumulated vibration exposure, important for mapping and logging the crew exposure, is determined by the ISO 2631:1997 vibration dose value. The severity due to the impact and accumulated vibration exposure is communicated to the crew every second as a color-coded indicator: green, yellow and red, representing low, medium and high, based on defined impact and dose limits. The severity index and feedback method are developed and validated by a data set of 27 three-hour simulations of a planning craft in irregular waves and verified for its feasibility in real-world applications by full-scale acceleration data recorded aboard high-speed planing craft in operation.

Visualising large-scale geodynamic simulations: How to Dive into Earth's Mantle with Virtual Reality

2020 ◽  
Author(s):  
Markus Wiedemann ◽  
Bernhard S.A. Schuberth ◽  
Lorenzo Colli ◽  
Hans-Peter Bunge ◽  
Dieter Kranzlmüller
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Large Data ◽  
Optimization Techniques ◽  
Physical Parameters ◽  
Mantle Flow ◽  
Data Set ◽  
Precise Knowledge

<p>Precise knowledge of the forces acting at the base of tectonic plates is of fundamental importance, but models of mantle dynamics are still often qualitative in nature to date. One particular problem is that we cannot access the deep interior of our planet and can therefore not make direct in situ measurements of the relevant physical parameters. Fortunately, modern software and powerful high-performance computing infrastructures allow us to generate complex three-dimensional models of the time evolution of mantle flow through large-scale numerical simulations.</p><p>In this project, we aim at visualizing the resulting convective patterns that occur thousands of kilometres below our feet and to make them "accessible" using high-end virtual reality techniques.</p><p>Models with several hundred million grid cells are nowadays possible using the modern supercomputing facilities, such as those available at the Leibniz Supercomputing Centre. These models provide quantitative estimates on the inaccessible parameters, such as buoyancy and temperature, as well as predictions of the associated gravity field and seismic wavefield that can be tested against Earth observations.</p><p>3-D visualizations of the computed physical parameters allow us to inspect the models such as if one were actually travelling down into the Earth. This way, convective processes that occur thousands of kilometres below our feet are virtually accessible by combining the simulations with high-end VR techniques.</p><p>The large data set used here poses severe challenges for real time visualization, because it cannot fit into graphics memory, while requiring rendering with strict deadlines. This raises the necessity to balance the amount of displayed data versus the time needed for rendering it.</p><p>As a solution, we introduce a rendering framework and describe our workflow that allows us to visualize this geoscientific dataset. Our example exceeds 16 TByte in size, which is beyond the capabilities of most visualization tools. To display this dataset in real-time, we reduce and declutter the dataset through isosurfacing and mesh optimization techniques.</p><p>Our rendering framework relies on multithreading and data decoupling mechanisms that allow to upload data to graphics memory while maintaining high frame rates. The final visualization application can be executed in a CAVE installation as well as on head mounted displays such as the HTC Vive or Oculus Rift. The latter devices will allow for viewing our example on-site at the EGU conference.</p>

scholarly journals An advanced implementation idea for detecting real-time object occupancy

2020 ◽  
Author(s):  
Claremary James ◽  
Varghese James
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Natural Disaster ◽  
Motion Detector ◽  
Human Presence ◽  
Detection Technology ◽  
Occupancy Detection ◽  
Infrared Rays ◽  
Per Se ◽  
Lost Object

Object detection is a title that has earned significances over several fields which have always benefitted socially during circumstances, namely incidents involving human endangerment such as natural disaster where threat may occur in the form of an earthquake, human entrapment underneath rubbles per se. The usage of PIR (Passive Infrared Rays) motion detector to detect humans, objects and other living beings through their movement, has proven the ability in handling situations where such detection is the best chance. However, this approach is not utilized in every situation. In the proposed research paper, an object occupancy detection technology notion is detailed which will describe the function to detect the occupancy or presence of human in an area, specifically transport vehicles that will help in determining passengers inside and to find lost objects as well. The motive behind raising this technological need is to aid or assist in occurrence wherein facing difficulty to find an object being lost or misplaced in a space, as well to detect the humans occupied. This assistance shall ease the detection of occupancy and identifying the lost object. The comprehended object occupancy idea is utilized to recognize the humans and object detection. The implementation idea shall facilitate the utilization of PIR-based motion detector sensor to recognize human presence as well as SlimYOLOv3 framework to identify objects. Circumstances where the occupancy of humans are counted and object to be identified are the main output.

scholarly journals A modified YOLOv3 detection method for vision-based water surface garbage capture robot

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142093271
Author(s):  
Xiali Li ◽  
Manjun Tian ◽  
Shihan Kong ◽  
Licheng Wu ◽  
Junzhi Yu
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Water Surface ◽  
High Speed ◽  
Detection Method ◽  
Training Data ◽  
Aquatic Environments ◽  
Valuable Insight ◽  
Data Set ◽  
Detection Algorithms

To tackle the water surface pollution problem, a vision-based water surface garbage capture robot has been developed in our lab. In this article, we present a modified you only look once v3-based garbage detection method, allowing real-time and high-precision object detection in dynamic aquatic environments. More specifically, to improve the real-time detection performance, the detection scales of you only look once v3 are simplified from 3 to 2. Besides, to guarantee the accuracy of detection, the anchor boxes of our training data set are reclustered for replacing some of the original you only look once v3 prior anchor boxes that are not appropriate to our data set. By virtue of the proposed detection method, the capture robot has the capability of cleaning floating garbage in the field. Experimental results demonstrate that both detection speed and accuracy of the modified you only look once v3 are better than those of other object detection algorithms. The obtained results provide valuable insight into the high-speed detection and grasping of dynamic objects in complex aquatic environments autonomously and intelligently.

scholarly journals EAWNet: An Edge Attention-Wise Objector for Real-Time Visual Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhichao Zhang ◽  
Hui Chen ◽  
Xiaoqing Yin ◽  
Jinsheng Deng
Keyword(s):  
Internet Of Things ◽  
Object Detection ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Multiple Scales ◽  
Hot Spot ◽  
Training Data ◽  
Long Distance ◽  
Size Limitation

With the upgrading of the high-performance image processing platform and visual internet of things sensors, VIOT is widely used in intelligent transportation, autopilot, military reconnaissance, public safety, and other fields. However, the outdoor visual internet of things system is very sensitive to the weather and unbalanced scale of latent object. The performance of supervised learning is often limited by the disturbance of abnormal data. It is difficult to collect all classes from limited historical instances. Therefore, in terms of the anomaly detection images, fast and accurate artificial intelligence-based object detection technology has become a research hot spot in the field of intelligent vision internet of things. To this end, we propose an efficient and accurate deep learning framework for real-time and dense object detection in VIOT named the Edge Attention-wise Convolutional Neural Network (EAWNet) with three main features. First, it can identify remote aerial and daily scenery objects fast and accurately in terms of an unbalanced category. Second, edge prior and rotated anchor are adopted to enhance the efficiency of detection in edge computing internet. Third, our EAWNet network uses an edge sensing object structure, makes full use of an attention mechanism to dynamically screen different kinds of objects, and performs target recognition on multiple scales. The edge recovery effect and target detection performance for long-distance aerial objects were significantly improved. We explore the efficiency of various architectures and fine tune the training process using various backbone and data enhancement strategies to increase the variety of the training data and overcome the size limitation of input images. Extensive experiments and comprehensive evaluation on COCO and large-scale DOTA datasets proved the effectiveness of this framework that achieved the most advanced performance in real-time VIOT object detection.

scholarly journals Efficient Object Detection Model for Real-time UAV Application

2021 ◽  
Vol 14 (1) ◽  
pp. 45
Author(s):  
Subrahmanyam Vaddi ◽  
Dongyoun Kim ◽  
Chandan Kumar ◽  
Shafqat Shad ◽  
Ali Jannesari
Keyword(s):  
Object Detection ◽  
Real Time ◽  
High Performance ◽  
Class Imbalance ◽  
Detection Techniques ◽  
Detection Model ◽  
Deep Feature ◽  
Proposed Model ◽  
Negative Effect ◽  
Feature Pyramid

Unmanned Aerial Vehicles (UAVs) equipped with vision capabilities have become popular in recent years. Many applications have especially been employed object detection techniques extracted from the information captured by an onboard camera. However, object detection on UAVs requires high performance, which has a negative effect on the result. In this article, we propose a deep feature pyramid architecture with a modified focal loss function, which enables it to reduce the class imbalance. Moreover, the proposed method employed an end to end object detection model running on the UAV platform for real-time application. To evaluate the proposed architecture, we combined our model with Resnet and MobileNet as a backend network, and we compared it with RetinaNet and HAL-RetinaNet. Our model produced a performance of 30.6 mAP with an inference time of 14 fps. This result shows that our proposed model outperformed RetinaNet by 6.2 mAP.

scholarly journals POLLEN BEARING HONEY BEE DETECTION IN HIVE ENTRANCE VIDEO RECORDED BY REMOTE EMBEDDED SYSTEM FOR POLLINATION MONITORING

2016 ◽  
Vol III-7 ◽  
pp. 51-57 ◽  
Author(s):  
Z. Babic ◽  
R. Pilipovic ◽  
V. Risojevic ◽  
G. Mirjanic
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Honey Bee ◽  
Honey Bees ◽  
High Performance ◽  
Video Transmission ◽  
Surveillance Video ◽  
Classification Rate ◽  
Data Set ◽  
Non Invasive

Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.

Real-Time Pedestrian Detection Using Convolutional Neural Networks

2018 ◽  
Vol 32 (11) ◽  
pp. 1856014 ◽  
Author(s):  
Ping Kuang ◽  
Tingsong Ma ◽  
Fan Li ◽  
Ziwei Chen
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Original Network ◽  
Data Set ◽  
Great Opportunity ◽  
Detection Technology ◽  
Proposed Model ◽  
Secure Environment ◽  
Definition Of ◽  
Better Than

Pedestrian detection provides manager of a smart city with a great opportunity to manage their city effectively and automatically. Specifically, pedestrian detection technology can improve our secure environment and make our traffic more efficient. In this paper, all of our work both modification and improvement are made based on YOLO, which is a real-time Convolutional Neural Network detector. In our work, we extend YOLO’s original network structure, and also give a new definition of loss function to boost the performance for pedestrian detection, especially when the targets are small, and that is exactly what YOLO is not good at. In our experiment, the proposed model is tested on INRIA, UCF YouTube Action Data Set and Caltech Pedestrian Detection Benchmark. Experimental results indicate that after our modification and improvement, the revised YOLO network outperforms the original version and also is better than other solutions.

scholarly journals Development of Variants of High-Performance Self-Compacting Concrete with Improved Resistance to the Attack of Sulfates

2021 ◽  
Vol 11 (13) ◽  
pp. 5945
Author(s):  
Ivailo Terzijski ◽  
Dalibor Kocáb ◽  
Petr Štěpánek ◽  
Jiří Strnad ◽  
František Girgle ◽  
...  
Keyword(s):  
High Performance ◽  
Graphical Method ◽  
New Technology ◽  
Practical Application ◽  
Negative Effects ◽  
Self Compacting Concrete ◽  
Concrete Technology ◽  
Sewer Pipes ◽  
Aqueous Environments ◽  
Analytical Work

This paper presents experimental and analytical work of which the main objective was to support the introduction of a new technology for the production of sewer pipes. In this technology, the pipes produced consist of two differently produced parts. The direct part uses conventional vibro press compacted concrete. In the curved part, on the other hand, self-compacting concrete technology is used. The cooperating company, Prefa Brno a.s., defined possible negative effects on concrete of sewer pipes. The task of the research team and now the author’s team was to propose a procedure for the development of suitable self-compacting concrete variants and subsequently the design of a methodology to verify their durability in aqueous environments containing sulfates. To increase the efficiency of the development, the model mortar method was used in the experimental work. That is, instead of the original concrete, a model mortar derived from it was tested. The principle and procedure of derivation of model mortars are described in the paper. In total, eight variants of model mortars were tested, and at least three of them fulfilled the requirements. An optional but beneficial part of the carried out work was the derivation and practical application of the time-anchored-triangles-of-cracking graphical method developed during the research. This method is used to quickly compare the degree of attack of different silicate composites tested in a common bath inducing type III corrosion.

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