scholarly journals Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles

2021 ◽  
Vol 13 (21) ◽  
pp. 4401
Author(s):  
Gen Zheng ◽  
Jianhu Zhao ◽  
Shaobo Li ◽  
Jie Feng
Keyword(s):  
Standard Deviation ◽  
Object Detection ◽  
Real Time ◽  
Synthesis Method ◽  
Vertical Direction ◽  
Mean Deviation ◽  
The Real ◽  
Detection Algorithms ◽  
Underwater Pipeline ◽  
Time Requirements

With the increasing number of underwater pipeline investigation activities, the research on automatic pipeline detection is of great significance. At this stage, object detection algorithms based on Deep Learning (DL) are widely used due to their abilities to deal with various complex scenarios. However, DL algorithms require massive representative samples, which are difficult to obtain for pipeline detection with sub-bottom profiler (SBP) data. In this paper, a zero-shot pipeline detection method is proposed. First, an efficient sample synthesis method based on SBP imaging principles is proposed to generate samples. Then, the generated samples are used to train the YOLOv5s network and a pipeline detection strategy is developed to meet the real-time requirements. Finally, the trained model is tested with the measured data. In the experiment, the trained model achieved a [email protected] of 0.962, and the mean deviation of the predicted pipeline position is 0.23 pixels with a standard deviation of 1.94 pixels in the horizontal direction and 0.34 pixels with a standard deviation of 2.69 pixels in the vertical direction. In addition, the object detection speed also met the real-time requirements. The above results show that the proposed method has the potential to completely replace the manual interpretation and has very high application value.

A Survey of Real-Time Industrial Wireless LAN Research

2015 ◽  
Vol 738-739 ◽  
pp. 1105-1110 ◽  
Author(s):  
Yuan Qing Qin ◽  
Ying Jie Cheng ◽  
Chun Jie Zhou
Keyword(s):  
Real Time ◽  
Future Development ◽  
Wireless Lan ◽  
State Of The Art ◽  
The State ◽  
Local Networks ◽  
The Real ◽  
Time Requirements ◽  
And Performance ◽  
Fundamental Mechanism

This paper mainly surveys the state-of-the-art on real-time communicaton in industrial wireless local networks(WLANs), and also identifys the suitable approaches to deal with the real-time requirements in future. Firstly, this paper summarizes the features of industrial WLANs and the challenges it encounters. Then according to the real-time problems of industrial WLAN, the fundamental mechanism of each recent representative resolution is analyzed in detail. Meanwhile, the characteristics and performance of these resolutions are adequately compared. Finally, this paper concludes the current of the research and discusses the future development of industrial WLANs.

Field Assessment of Camera Based Drilling Dynamics

2021 ◽  
Author(s):  
Alexis Koulidis ◽  
Mohamed Abdullatif ◽  
Ahmed Galal Abdel-Kader ◽  
Mohammed-ilies Ayachi ◽  
Shehab Ahmed ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Real Time ◽  
Tracking Algorithm ◽  
Interest Points ◽  
Stick Slip ◽  
Detection Algorithms ◽  
Point Tracking ◽  
Surface Data ◽  
Field Assessment

Abstract Surface data measurement and analysis are an established mean of detecting drillstring low-frequency torsional vibration or stick-slip. The industry has also developed models that link surface torque and downhole drill bit rotational speed. Cameras provide an alternative noninvasive approach to existing wired/wireless sensors used to gather such surface data. The results of a preliminary field assessment of drilling dynamics utilizing camera-based drillstring monitoring are presented in this work. Detection and timing of events from the video are performed using computer vision techniques and object detection algorithms. A real-time interest point tracker utilizing homography estimation and sparse optical flow point tracking is deployed. We use a fully convolutional deep neural network trained to detect interest points and compute their accompanying descriptors. The detected points and descriptors are matched across video sequences and used for drillstring rotation detection and speed estimation. When the drillstring's vibration is invisible to the naked eye, the point tracking algorithm is preceded with a motion amplification function based on another deep convolutional neural network. We have clearly demonstrated the potential of camera-based noninvasive approaches to surface drillstring dynamics data acquisition and analysis. Through the application of real-time object detection algorithms on rig video feed, surface events were detected and timed. We were also able to estimate drillstring rotary speed and motion profile. Torsional drillstring modes can be identified and correlated with drilling parameters and bottomhole assembly design. A novel vibration array sensing approach based on a multi-point tracking algorithm is also proposed. A vibration threshold setting was utilized to enable an additional motion amplification function providing seamless assessment for multi-scale vibration measurement. Cameras were typically devices to acquire images/videos for offline automated assessment (recently) or online manual monitoring (mainly), this work has shown how fog/edge computing makes it possible for these cameras to be "conscious" and "intelligent," hence play a critical role in automation/digitalization of drilling rigs. We showcase their preliminary application as drilling dynamics and rig operations sensors in this work. Cameras are an ideal sensor for a drilling environment since they can be installed anywhere on a rig to perform large-scale live video analytics on drilling processes.

Real-time engine model development based on time complexity analysis

2021 ◽  
pp. 146808742110397
Author(s):  
Haotian Chen ◽  
Kun Zhang ◽  
Kangyao Deng ◽  
Yi Cui
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Complexity Analysis ◽  
High Accuracy ◽  
The Real ◽  
Cycle Simulation ◽  
Crank Angle ◽  
Time Requirements ◽  
Improved Model ◽  
Simulation Time

Real-time simulation models play an important role in the development of engine control systems. The mean value model (MVM) meets real-time requirements but has limited accuracy. By contrast, a crank-angle resolved model, such as the filling -and-empty model, can be used to simulate engine performance with high accuracy but cannot meet real-time requirements. Time complexity analysis is used to develop a real-time crank-angle resolved model with high accuracy in this study. A method used in computer science, program static analysis, is used to theoretically determine the computational time for a multicylinder engine filling-and-empty (crank-angle resolved) model. Then, a prediction formula for the engine cycle simulation time is obtained and verified by a program run test. The influence of the time step, program structure, algorithm and hardware on the cycle simulation time are analyzed systematically. The multicylinder phase shift method and a fast calculation method for the turbocharger characteristics are used to improve the crank-angle resolved filling-and-empty model to meet real-time requirements. The improved model meets the real-time requirement, and the real-time factor is improved by 3.04 times. A performance simulation for a high-power medium-speed diesel engine shows that the improved model has a max error of 5.76% and a real-time factor of 3.93, which meets the requirement for a hardware-in-the-loop (HIL) simulation during control system development.

Prediction of Crime Scene Objects Using Deep Learning

2021 ◽  
Vol 9 (VIII) ◽  
pp. 857-864
Author(s):  
Vibhavari B Rao
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Computer Architecture ◽  
Real Time ◽  
Surveillance System ◽  
Crime Scene ◽  
Detection Model ◽  
Detection Algorithms ◽  
Performance Metric ◽  
Smart Surveillance

The crime rates today can inevitably put a civilian's life in danger. While consistent efforts are being made to alleviate crime, there is also a dire need to create a smart and proactive surveillance system. Our project implements a smart surveillance system that would alert the authorities in real-time when a crime is being committed. During armed robberies and hostage situations, most often, the police cannot reach the place on time to prevent it from happening, owing to the lag in communication between the informants of the crime scene and the police. We propose an object detection model that implements deep learning algorithms to detect objects of violence such as pistols, knives, rifles from video surveillance footage, and in turn send real-time alerts to the authorities. There are a number of object detection algorithms being developed, each being evaluated under the performance metric mAP. On implementing Faster R-CNN with ResNet 101 architecture we found the mAP score to be about 91%. However, the downside to this is the excessive training and inferencing time it incurs. On the other hand, YOLOv5 architecture resulted in a model that performed very well in terms of speed. Its training speed was found to be 0.012 s / image during training but naturally, the accuracy was not as high as Faster R-CNN. With good computer architecture, it can run at about 40 fps. Thus, there is a tradeoff between speed and accuracy and it's important to strike a balance. We use transfer learning to improve accuracy by training the model on our custom dataset. This project can be deployed on any generic CCTV camera by setting up a live RTSP (real-time streaming protocol) and streaming the footage on a laptop or desktop where the deep learning model is being run.

Performance Evaluation of Real-Time Object Detection Algorithms

2019 ◽  
Author(s):  
Sanun Srisuk ◽  
Chanwit Suwannapong ◽  
Songrit Kitisriworapan ◽  
Apiwut Kaewsong ◽  
Surachai Ongkittikul
Keyword(s):  
Performance Evaluation ◽  
Object Detection ◽  
Real Time ◽  
Detection Algorithms

Fast and Parallel Summed Area Table for Fabric Defect Detection

2016 ◽  
Vol 30 (09) ◽  
pp. 1660004 ◽  
Author(s):  
Khaled Ragab
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Gpu Computing ◽  
Processing Unit ◽  
Detection Algorithms ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Time Requirements ◽  
Robustness To Noise ◽  
Graphical Processing

Automating fabric defect detection has a significant role in fabric industries. However, the existing fabric defect detection algorithms lack the real-time performance that is required in real applications due to their high demanding computation. To ensure real time, high accuracy and reliable fabric defect detection this paper developed a fast and parallel normalized cross-correlation algorithm based on summed-area table technique called PFDD-SAT. To meet real-time requirements, extensive use of the NVIDIA CUDA framework for Graphical Processing Unit (GPU) computing is made. The detailed implementation steps of the PFDD-SAT are illustrated in this paper. Several experiments have been carried out to evaluate the detection time and accuracy and then the robustness to illumination and Gaussian noises. The results show that the PFDD-SAT has robustness to noise and speeds the defect detection process more than 200 times than normal required time and that greatly met the needs for real-time automatic fabric defect detection.

Design and Implement of the Car Reversing Automatic Anti-Collision Control System

2014 ◽  
Vol 494-495 ◽  
pp. 206-209
Author(s):  
Xue Feng Yang ◽  
Li Gang Chen ◽  
Xian Feng Zhong
Keyword(s):  
Real Time ◽  
Measurement Errors ◽  
Data Exchange ◽  
High Reliability ◽  
Distance Measurement ◽  
The Real ◽  
Control Units ◽  
Ultrasonic Ranging ◽  
Time Requirements ◽  
Image Collection

There are considerable difference between the actual distance and that measured by infrared or ultrasonic ranging. The car reversing isnt intelligent enough. In order to solve the issues, this paper design an automatic car reversing auxiliary systems based on monocular sight. The system hardware mainly consists of image collection module, embedded micro-controller, and electronic braking module. On the basis of the distance measurement algorithm based on monocular sight, the real-time distance to the front vehicle can be measured and can be auxiliary controlled via the data exchange among vehicle electrical control units. The vehicle dynamic driving experiment verifies the high reliability of the vehicle automatic reversing auxiliary system based on monocular sight. The distance measurement errors are less than 2% when the distance to the front barriers is in the range of 20m~70m. The system can satisfy the real-time requirements for the vehicle intelligent auxiliary braking.

A Data Parallel Implementation Scheme of Geometric Operations

2014 ◽  
Vol 519-520 ◽  
pp. 719-723
Author(s):  
Guang Wang
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Digital Image Processing ◽  
Digital Image ◽  
Parallel Implementation ◽  
Computation Complexity ◽  
The Real ◽  
Data Parallel ◽  
Implementation Scheme ◽  
Time Requirements

A data parallel implementation of geometric operations is proposed and conclusions are proved. It shows that the computation complexity of data parallel implementation scheme presented in this paper is Ο(M+N). It can be used to improve the efficiency of geometric operations and can easily meet the real time requirements of the digital image processing.

scholarly journals Execution of Median Filter Built on FPGA for Trimming Noise Meeting the Real Time Requirements

2019 ◽  
Vol 8 (4) ◽  
pp. 7855-7858
Keyword(s):  
Real Time ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Median Filter ◽  
Sliding Window ◽  
Spatial Filter ◽  
Effective Technique ◽  
The Real ◽  
Time Requirements ◽  
Suppression Process

As images plays a vital in all aspects, there is a need to met the real time requirements in processing the image. Major challenges raised in processing the image is noise. The utmost typical difficult is effective denoising creation as well as quick functioning in the processing of digital image noise suppression process for the need of real time consequences to afford image with high quality this project was introduced. Generally filters plays a major role to remove the impulse noise in acquired images. The filter named sliding window spatial filter which is familiar as median filter is effective technique to eradicate impulse noise from the devoleped image. But in real time, it is very difficult to execute. To overcome this, FPGA methodology is introduced to fulfills the support besides the optimization of major constraints like area, speed, power. In addition to this, it assures technical sustenance of eradicating noise in image as per requirements in real time. Regarding the design and structure appearances in FPGA, Xilinx software is used for simulation and code has been written in Verilog language.

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.

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