Rapid Development of a Gunfire Detection Algorithm Using an Imagery Database

2006 ◽  
Author(s):  
William Seisler ◽  
Neil Terry ◽  
Elmer Williams
Keyword(s):  
Rapid Development ◽  
Detection Algorithm

Trust-based federated learning for network anomaly detection

2021 ◽  
pp. 1-11
Author(s):  
Naiyue Chen ◽  
Yi Jin ◽  
Yinglong Li ◽  
Luxin Cai
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Private Information ◽  
Information Disclosure ◽  
Rapid Development ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Local Data ◽  
Daily Work ◽  
Network Anomaly Detection

With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to the problems of excessive data and long response time for network anomaly detection, we propose a trust-based Federated learning anomaly detection algorithm. We use the edge nodes to train the local data model, and upload the machine learning parameters to the central node. Meanwhile, according to the performance of edge nodes training, we set different weights to match the processing capacity of each terminal which will obtain faster convergence speed and better attack classification accuracy. The user’s private information will only be processed locally and will not be uploaded to the central server, which can reduce the risk of information disclosure. Finally, we compare the basic federated learning model and TFCNN algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show that the TFCNN algorithm can improve accuracy and communication efficiency.

scholarly journals The 3D Instance Segmentation Network for Synapse Reconstruction from Serial Electron Microscopy Images

2021 ◽  
Author(s):  
Jing Liu ◽  
Bei Hong ◽  
Qiwei Xie ◽  
Chao Liu ◽  
Hua Han
Keyword(s):  
Electron Microscopy ◽  
Large Scale ◽  
Rapid Development ◽  
Detection Algorithm ◽  
Vital Role ◽  
Accurate Solution ◽  
Connected Components ◽  
Binary Segmentation ◽  
Public Datasets ◽  
Instance Segmentation

Abstract Background The synapse is the key part where neurons communicate with each other. Synaptic plasticity plays a vital role in study and memory. Due to the rapid development of electron microscopy (EM) technology, imaging synapses at nanometer scale possible become possible. However, the automation and effectiveness of the synapse detection algorithm have not yet been satisfactory. The most commonly used method is a two-step solution, where first binary segmentation masks are obtained and then reconstruction results are generated by finding connected components. Results In this paper, a novel 3D instance segmentation network which can predict the synapses end to end was proposed. Then, it was also proved that the network can exploit features consistent with the biological structures of synapses by visualizing the network layer. Furthermore, our method was evaluated on two public datasets, and experimental results demonstrated the effectiveness of our proposed method. Conclusion The proposed method provided a fast and accurate solution to detecting synapses from serial section EM images. Besides, a block-wise inference strategy which adapts well to large scale EM images was introduced, and it can also help neuroscientists achieve labor-free analysis and quantification of synapses.

Research on detection algorithm of lithium battery surface defects based on embedded machine vision

2021 ◽  
pp. 1-9
Author(s):  
Yonggang Chen ◽  
Yufeng Shu ◽  
Xiaomian Li ◽  
Changwei Xiong ◽  
Shenyi Cao ◽  
...  
Keyword(s):  
Lithium Battery ◽  
Surface Defects ◽  
Visual Detection ◽  
Rapid Development ◽  
Detection Algorithm ◽  
Large Error ◽  
Detection Algorithms ◽  
Manual Inspection ◽  
Low Efficiency ◽  
Vision Detection

In the production process of lithium battery, the quality inspection requirements of lithium battery are very high. At present, most of the work is done manually. Aiming at the problem of large manual inspection workload and large error, the robot visual inspection technology is applied to the production of lithium battery. In recent years, with the rapid development and progress of science and technology, the rapid development of visual detection hardware and algorithms, making it possible to screen defective products through visual detection algorithms. This paper takes lithium battery as the research object, and studies its vision detection algorithm. As a common commodity, the quality of lithium battery is the key for users to choose. With the increasing requirements of users for battery quality, how to produce high-quality battery is the key problem to be solved by manufacturers. However, at present, the defects of battery surface are mostly carried out manually. There are low efficiency and low detection rate in the process of manual detection. In this paper, the visual detection algorithm is studied to detect the defects such as pits, rust marks and broken skin on the surface of lithium battery, specifically to design the imaging experimental platform of lithium battery; use different lighting schemes to design different battery positioning and extraction algorithms; use Hough detection method to locate the battery surface, and design the battery defect algorithm for this, and compare the algorithm through experiments.

scholarly journals Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 5389-5401
Author(s):  
Hou Jiang ◽  
Ling Yao ◽  
Ning Lu ◽  
Jun Qin ◽  
Tang Liu ◽  
...  
Keyword(s):  
Rapid Development ◽  
Model Performance ◽  
Detection Algorithm ◽  
Fine Tuning ◽  
Aerial Images ◽  
Photovoltaic Panel ◽  
Fine Grained ◽  
Automatic Information ◽  
Deep Networks ◽  
Direct Cross

Abstract. In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Accurate localized PV information, including location and size, is the basis for PV regulation and potential assessment of the energy sector. Automatic information extraction based on deep learning requires high-quality labeled samples that should be collected at multiple spatial resolutions and under different backgrounds due to the diversity and variable scale of PVs. We established a PV dataset using satellite and aerial images with spatial resolutions of 0.8, 0.3, and 0.1 m, which focus on concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs, respectively. The dataset contains 3716 samples of PVs installed on shrub land, grassland, cropland, saline–alkali land, and water surfaces, as well as flat concrete, steel tile, and brick roofs. The dataset is used to examine the model performance of different deep networks on PV segmentation. On average, an intersection over union (IoU) greater than 85 % is achieved. In addition, our experiments show that direct cross application between samples with different resolutions is not feasible and that fine-tuning of the pre-trained deep networks using target samples is necessary. The dataset can support more work on PV technology for greater value, such as developing a PV detection algorithm, simulating PV conversion efficiency, and estimating regional PV potential. The dataset is available from Zenodo on the following website: https://doi.org/10.5281/zenodo.5171712 (Jiang et al., 2021).

scholarly journals Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery

2021 ◽  
Author(s):  
Hou Jiang ◽  
Ling Yao ◽  
Ning Lu ◽  
Jun Qin ◽  
Tang Liu ◽  
...  
Keyword(s):  
Rapid Development ◽  
Model Performance ◽  
Detection Algorithm ◽  
Fine Tuning ◽  
Aerial Images ◽  
Photovoltaic Panel ◽  
Fine Grained ◽  
Automatic Information ◽  
Deep Networks ◽  
Direct Cross

Abstract. In the context of global carbon emission reduction, solar photovoltaics (PV) is experiencing rapid development. Accurate localized PV information, including location and size, is the basis for PV regulation and potential assessment of energy sector. Automatic information extraction based on deep learning requires high-quality labelled samples that should be collected at multiple spatial resolutions and under different backgrounds due to the diversity and variable scale of PV. We established a PV dataset using satellite and aerial images with spatial resolutions of 0.8 m, 0.3 m and 0.1 m, which focus on concentrated PV, distributed ground PV and fine-grained rooftop PV, respectively. The dataset contains 3716 samples of PVs installed on shrub land, grassland, cropland, saline-alkali, and water surface, as well as flat concrete, steel tile, and brick roofs. We used this dataset to examine the model performance of different deep networks on PV segmentation, and on average an intersection over union (IoU) greater than 85 % was achieved. In addition, our experiments show that direct cross application between samples with different resolutions is not feasible, and fine-tuning of the pre-trained deep networks using target samples is necessary. The dataset can support more works on PVs for greater value, such as, developing PV detection algorithm, simulating PV conversion efficiency, and estimating regional PV potential. The dataset is available from Zenodo on the following website: https://doi.org/10.5281/zenodo.5171712 (Jiang et al. 2021).

scholarly journals Target Positioning and Grasping of NAO Robot Based on Monocular Stereo Vision

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Hongbin Chen
Keyword(s):  
Stereo Vision ◽  
Target Recognition ◽  
Rapid Development ◽  
The Elderly ◽  
Detection Algorithm ◽  
Monocular Vision ◽  
External Information ◽  
Nao Robot ◽  
Area Of Interest ◽  
Domestic Robots

With the continuous advancement of science and technology and the rapid development of robotics, it has become an inevitable trend for domestic robots to enter thousands of households. In order to solve the inconvenience problem of the elderly and people with special needs, because the elderly and other people in need may need the help of domestic robots due to inconvenient legs and feet, the research of the robot target position based on monocular stereo vision and the understanding of the robot NAO are very important. Research and experiments are carried out on the target recognition and positioning in the process of NAO robot grasping. This paper proposes a recognition algorithm corresponding to quantitative component statistical information. First, extract the area of interest that contains the purpose from the image. After that, to eliminate interference in various fields and achieve target recognition, the robot cameras have almost no common field of view and can only use one camera at the same time. Therefore, this article uses the monocular vision principle to locate the target, and the detection algorithm is based on the structure of the robot head material, establishes the relationship between the height change of the machine head and the tilt angle, and improves the monocular vision NAO robot detection algorithm. According to experiments, the accuracy of the robot at close range can be controlled below 1 cm. This article completes the robot’s grasping and transmission of the target. About 80% of the external information that humans can perceive comes from vision. In addition, there are advantages such as high efficiency and good stability.

scholarly journals An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos

2019 ◽  
Vol 11 (10) ◽  
pp. 1241 ◽  
Author(s):  
Jing Li ◽  
Shuo Chen ◽  
Fangbing Zhang ◽  
Erkang Li ◽  
Tao Yang ◽  
...  
Keyword(s):  
Rapid Development ◽  
Detection Algorithm ◽  
Surveillance Systems ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Aerial Surveillance ◽  
Current Frame ◽  
Ground Speed ◽  
Optical Flow Method ◽  
Adaptive Framework

With the rapid development of unmanned aerial vehicles (UAVs), UAV-based intelligent airborne surveillance systems represented by real-time ground vehicle speed estimation have attracted wide attention from researchers. However, there are still many challenges in extracting speed information from UAV videos, including the dynamic moving background, small target size, complicated environment, and diverse scenes. In this paper, we propose a novel adaptive framework for multi-vehicle ground speed estimation in airborne videos. Firstly, we build a traffic dataset based on UAV. Then, we use the deep learning detection algorithm to detect the vehicle in the UAV field of view and obtain the trajectory in the image through the tracking-by-detection algorithm. Thereafter, we present a motion compensation method based on homography. This method obtains matching feature points by an optical flow method and eliminates the influence of the detected target to accurately calculate the homography matrix to determine the real motion trajectory in the current frame. Finally, vehicle speed is estimated based on the mapping relationship between the pixel distance and the actual distance. The method regards the actual size of the car as prior information and adaptively recovers the pixel scale by estimating the vehicle size in the image; it then calculates the vehicle speed. In order to evaluate the performance of the proposed system, we carry out a large number of experiments on the AirSim Simulation platform as well as real UAV aerial surveillance experiments. Through quantitative and qualitative analysis of the simulation results and real experiments, we verify that the proposed system has a unique ability to detect, track, and estimate the speed of ground vehicles simultaneously even with a single downward-looking camera. Additionally, the system can obtain effective and accurate speed estimation results, even in various complex scenes.

scholarly journals Network Intrusion Detection Algorithm Combined with Group Convolution Network and Snapshot Ensemble

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1814
Author(s):  
Aili Wang ◽  
Wenya Wang ◽  
Huaming Zhou ◽  
Jian Zhang
Keyword(s):  
Intrusion Detection ◽  
Rapid Development ◽  
Class Imbalance ◽  
Ensemble Methods ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Integration Model ◽  
Generalization Ability ◽  
Network Intrusion ◽  
Training Cost

In order to adapt to the rapid development of network technology and network security detection in different scenarios, the generalization ability of the classifier needs to be further improved and has the ability to detect unknown attacks. However, the generalization ability of a single classifier is limited to dealing with class imbalance, and the previous ensemble methods inevitably increase the training cost. Therefore, in this paper, a novel network intrusion detection algorithm combined with group convolution is proposed to improve the generalization performance of the model. The basic classifier uses group convolution with symmetric structure instead of ordinary convolution neural network, which is trained by the cyclic cosine annealing learning rate. Through snapshot ensemble, the generalization ability of the integration model is improved without increasing the training cost. The effectiveness of this method is proved on NSL-KDD and UNSW-NB15 datasets compared to six other ensemble methods, the classification accuracy can achieve 85.82% and 80.38%, respectively.

The Collaboration Abstraction Layer for Distributed CAD Development

2003 ◽  
Author(s):  
H. Zhou ◽  
Y. Q. Lu ◽  
W. D. Li ◽  
S. Lin ◽  
J. Y. H. Fuh ◽  
...  
Keyword(s):  
Collaborative Design ◽  
Rapid Development ◽  
Detection Algorithm ◽  
Collaborative Management ◽  
Management Service ◽  
Software Module ◽  
Constraint Checking ◽  
Abstraction Layer ◽  
3D Streaming ◽  
Streaming Service

In order to speed up the development of distributed CAD (DCAD) software applications and offer the end-users a friendly environment for collaborative design, Collaboration Abstraction Layer (CAL) is proposed. CAL aims to develop a pluggable software module that can be embedded into standalone CAD applications. Through summarizing and abstracting out the common characteristics of distributed CAD software, a set of foundation/helper classes for the important collaborative functionalities are enclosed in CAL, which include a 3D streaming service, a collaborative design management service, a constraint checking/solving service and a file versioning/baseline service. The 3D streaming service incorporates a geometrical simplification algorithm that supports selective refinement on level of details (LOD) model and a compact data structure represented in an XML format. The collaborative management service effectively schedules and manages a co-design job. The constraint checking/solving service, which composes of a design task dispatch interface, a collision detection algorithm, and an assembly constraint algorithm, coordinates designing and assembling based on constraints. The CAD file versioning/baseline service is to manage the history record of the CAD files and the milestones in the collaborative development process. By simulating the real collaborative design process, CAL designs a new collaboration mechanism which is different from most collaboration products in market. For the future potential development, CAL is built on an open-sourced software toolkit. It is coded to interfaces and kernel libraries so as to provide an immutable API for commonly used collaborative CAD functions. CAL enables rapid development of DCAD software, and minimizes application complexity by packaging the needed technology. Moreover, CAL is intending to be a partner to the current CAD software, not competitor, making it an ideal tool for future distributed CAD system development.

Recaptured Image Forensics Algorithm Based on Image Texture Feature

2019 ◽  
Vol 34 (03) ◽  
pp. 2054011
Author(s):  
Yanjun Sun ◽  
Xuanjing Shen ◽  
Changming Liu ◽  
Yongzhe Zhao
Keyword(s):  
Digital Image ◽  
Rapid Development ◽  
Image Forensics ◽  
Detection Algorithm ◽  
Digital Image Forensics ◽  
Image Texture ◽  
Support Vector ◽  
Gray Level ◽  
Identification Rate ◽  
Occurrence Matrix

With the rapid development of digital phones, the digital image forensics system in current times has had a great impact. It will lead to a serious threat for us, and especially the emergence of the recaptured image makes the existing digital image forensics algorithm invalid. So, it needs an effective image detection algorithm for us to identify recaptured images. In this paper, a new detection algorithm of the recaptured image is presented based on gray level co-occurrence matrix by analyzing the differences between the real and recaptured images. In order to analyze the differences, a new image evaluation model was put forward in this paper, which is called image variance ratio. Firstly, the algorithm proposed extracted high-frequency and low-frequency information of images by wavelet transform, based on which we calculated the relative gray level co-occurrence matrices. Secondly, the features of gray level co-occurrence matrix were extracted. At last, the recaptured image was classified by the support vector machine according to the features. The experimental results showed the algorithm proposed can not only effectively identify the recaptured image obtained from different media but also have better identification rate.

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