scholarly journals Algorithm of fire detection for multi-sensor system

2021 ◽  
Vol 48 (3) ◽  
pp. 59-67
Author(s):  
R. A. Bagutdinov ◽  
M. F. Stepanov
Keyword(s):  
Fire Detection ◽  
Software Tool ◽  
Detection Algorithm ◽  
Software Implementation ◽  
Sources Of Information ◽  
Multisensor Systems ◽  
Detection Algorithms ◽  
Monitoring Process ◽  
Scientific Goal ◽  
First Time

Objective. The paper proposes a fire detection algorithm for a multisensor system. Due to the difficult conditions in the field, for the first time, watch rescue operations are difficult and often endanger the lives of rescuers. The main scientific goal is for the system to work autonomously on certain segments of the monitoring process, while the three agents, to varying degrees, must interact with each other based on communication and decision-making algorithms.Method. There are many algorithms for image processing, but algorithms that use several sources of information are not sufficiently developed and described. The focus is on fire detection algorithms. The algorithm was developed using NI Vision Assistant, a software tool for rapid prototyping and testing of imaging applications.Result. In addition to the software implementation in C, which NI Vision Assistant generates by default, the paper presents a Python implementation of the algorithm.Conclusion. The results of the work can be used to develop multisensor systems for monitoring hard-to-reach areas.

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

scholarly journals Identification and Explanation of Challenging Conditions for Camera-Based Object Detection of Automated Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3699 ◽  
Author(s):  
Thomas Ponn ◽  
Thomas Kröger ◽  
Frank Diermeyer
Keyword(s):  
Artificial Intelligence ◽  
Object Detection ◽  
Influence Factors ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Automated Vehicles ◽  
Detection Algorithms ◽  
Type Approval ◽  
Market Launch ◽  
First Time

For a safe market launch of automated vehicles, the risks of the overall system as well as the sub-components must be efficiently identified and evaluated. This also includes camera-based object detection using artificial intelligence algorithms. It is trivial and explainable that due to the principle of the camera, performance depends highly on the environmental conditions and can be poor, for example in heavy fog. However, there are other factors influencing the performance of camera-based object detection, which will be comprehensively investigated for the first time in this paper. Furthermore, a precise modeling of the detection performance and the explanation of individual detection results is not possible due to the artificial intelligence based algorithms used. Therefore, a modeling approach based on the investigated influence factors is proposed and the newly developed SHapley Additive exPlanations (SHAP) approach is adopted to analyze and explain the detection performance of different object detection algorithms. The results show that many influence factors such as the relative rotation of an object towards the camera or the position of an object on the image have basically the same influence on the detection performance regardless of the detection algorithm used. In particular, the revealed weaknesses of the tested object detectors can be used to derive challenging and critical scenarios for the testing and type approval of automated vehicles.

Image fire detection algorithm based on support vector machine

2010 ◽  
Vol 30 (4) ◽  
pp. 1129-1131
Author(s):  
Na-juan YANG ◽  
Hui-qin WANG ◽  
Zong-fang MA
Keyword(s):  
Support Vector Machine ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaheen Syed ◽  
Bente Morseth ◽  
Laila A. Hopstock ◽  
Alexander Horsch
Keyword(s):  
Threshold Value ◽  
Detection Algorithm ◽  
Classification Model ◽  
Type I ◽  
Accelerometer Data ◽  
Deep Convolutional Neural Networks ◽  
Major Drawback ◽  
Wear Time ◽  
Detection Algorithms ◽  
Type Ii Errors

AbstractTo date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.

Sources of Information About Gestational Weight Gain, Diet, and Exercise Among Brazilian Immigrant Women Living in the United States: A Cross-Sectional Study

2021 ◽  
pp. 1-41
Author(s):  
Ana Cristina Lindsay ◽  
Qun Le ◽  
Denise Lima Nogueira ◽  
Márcia M. T. Machado ◽  
Mary L. Greaney
Keyword(s):  
United States ◽  
Weight Gain ◽  
Gestational Weight Gain ◽  
The United States ◽  
The Internet ◽  
Sources Of Information ◽  
Cross Sectional ◽  
Gestational Weight ◽  
Diet And Exercise ◽  
First Time

Abstract Objectives: The objective of this study was to assess sources of information about gestational weight gain (GWG), diet, and exercise among first-time pregnant Brazilian women in the United States (US). Design: Cross-sectional survey. Setting: Massachusetts, United States. Participants: First-time pregnant Brazilian women. Results: Eighty-six women, the majority of whom were immigrants (96.5%) classified as having low-acculturation levels (68%), participated in the study. Approximately two-thirds of respondents had sought information about GWG (72.1%), diet (79.1%), and exercise (74.4%) via the internet. Women classified as having low acculturation levels were more likely to seek information about GWG via the internet (OR = 7.55; 95% CI: 1.41, 40.26) than those with high acculturation levels after adjusting for age and receiving information about GWG from healthcare provider (doctor or midwife). Moreover, many respondents reported seeking information about GWG (67%), diet (71%), and exercise (52%) from family and friends. Women who self-identified as being overweight pre-pregnancy were less likely to seek information about diet (OR = 0.32; 95% CI: 0.11, 0.93) and exercise (OR = 0.33; 95% CI: 0.11, 0.96) from family and friends than those who self-identified being normal weight pre-pregnancy. Conclusions: This is the first study to assess sources of information about GWG, diet, and exercise among pregnant Brazilian immigrants in the US. Findings have implications for the design of interventions and suggest the potential of mHealth intervention as low-cost, easy access option for delivering culturally and linguistically tailored evidence-based information about GWG incorporating behavioral change practices to this growing immigrant group.

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

An Image Change Detection Algorithm Based on Multi-Feature Self-Attention Fusion Mechanism UNet Network

2021 ◽  
Author(s):  
Gulnaz Alimjan ◽  
Yiliyaer Jiaermuhamaiti ◽  
Huxidan Jumahong ◽  
Shuangling Zhu ◽  
Pazilat Nurmamat
Keyword(s):  
Change Detection ◽  
Detection Algorithm ◽  
Convolutional Network ◽  
Detection Algorithms ◽  
Multiple Dimensions ◽  
Change Detection Algorithms ◽  
Image Change Detection ◽  
Control Loss ◽  
The Relationship ◽  
Entire Network

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model’s execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach.

scholarly journals A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface

2021 ◽  
Vol 2083 (3) ◽  
pp. 032090
Author(s):  
Changli Mai ◽  
Bijian Jian ◽  
Yongfa Ling
Keyword(s):  
Water Surface ◽  
Center Of Mass ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Random Fluctuation ◽  
False Detection ◽  
Detection Algorithms ◽  
Regional Center ◽  
Underwater Environment ◽  
Active Imaging

Abstract Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.

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