scholarly journals SEMI-AUTOMATED CRICKET BROADCAST HIGHLIGHT GENERATION

2020 ◽  
Author(s):  
Sahil Ramlogan ◽  
Akash Pooransingh ◽  
Daniel Ringis
Keyword(s):  
Detection Rate ◽  
Extraction Process ◽  
False Detection Rate ◽  
False Detection ◽  
Text Extraction ◽  
Visual Result ◽  
Search Field ◽  
Visual Results

This paper provides an improvement to automated cricket highlight identification from full broadcasts. Further refinement is provided through semi- automated user verification. This visual result is synchronized with an automated text extraction process. The visual result was successfully extracted at a rate of 97.5% using the ORB function. With user validation a false detection rate of 0% was realized. The metadata from the commentary was then successfully extracted and combined with the visual results to allow for easy searching of the highlight video using any search field.

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

scholarly journals A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Li ◽  
Yao Liu ◽  
Zhengfan Zhao ◽  
Yue Zhang ◽  
Li He
Keyword(s):  
Detection Rate ◽  
Detection Method ◽  
Training Model ◽  
Learning Approach ◽  
Flow State ◽  
False Detection Rate ◽  
False Detection ◽  
Road Vehicles ◽  
Traffic Video ◽  
Flow States

Vehicle detection is expected to be robust and efficient in various scenes. We propose a multivehicle detection method, which consists of YOLO under the Darknet framework. We also improve the YOLO-voc structure according to the change of the target scene and traffic flow. The classification training model is obtained based on ImageNet and the parameters are fine-tuned according to the training results and the vehicle characteristics. Finally, we obtain an effective YOLO-vocRV network for road vehicles detection. In order to verify the performance of our method, the experiment is carried out on different vehicle flow states and compared with the classical YOLO-voc, YOLO 9000, and YOLO v3. The experimental results show that our method achieves the detection rate of 98.6% in free flow state, 97.8% in synchronous flow state, and 96.3% in blocking flow state, respectively. In addition, our proposed method has less false detection rate than previous works and shows good robustness.

HE-YOLO: Aerial Target Detection Based On Improved YOLOv3

2021 ◽  
Author(s):  
Yuqing Zhao ◽  
Jinlu Jia ◽  
Di Liu ◽  
Yurong Qian
Keyword(s):  
Target Detection ◽  
Loss Function ◽  
Detection Rate ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Small Scale ◽  
Detection Accuracy ◽  
False Detection Rate ◽  
False Detection ◽  
Bounding Box

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability.

A Approach to Change Detection for HR Image

2014 ◽  
Vol 971-973 ◽  
pp. 1449-1453
Author(s):  
Zuo Wei Huang ◽  
Shu Guang Wu ◽  
Tao Xin Zhang
Keyword(s):  
Change Detection ◽  
Detection Rate ◽  
False Detection Rate ◽  
False Detection ◽  
High Detection Rate ◽  
High Detection ◽  
Self Adaptation ◽  
Ica Algorithm ◽  
Different Characteristics ◽  
Better Than

Hyperspectral remote sensing is the multi-dimensional information obtaining technology,which combines target detection and spectral imaging technology together, In order to accord with the condition of hyperspectral imagery，the paper developed an optimized ICA algorithm for change detection to describe the statistical distribution of the data. By processing these abundance maps, change of different classes of objects can be obtained..A approach is capable of self-adaptation, and can be applied to hyperspectral images with different characteristics. Experiment results demonstrate that the ICA-based hyperspectral change detection performs better than other traditional methods with a high detection rate and a low false detection rate.

scholarly journals ORIGIN: Blind detection of faint emission line galaxies in MUSE datacubes

2020 ◽  
Vol 635 ◽  
pp. A194 ◽  
Author(s):  
David Mary ◽  
Roland Bacon ◽  
Simon Conseil ◽  
Laure Piqueras ◽  
Antony Schutz
Keyword(s):  
Emission Line ◽  
Detection Rate ◽  
Data Cube ◽  
Ratio Test ◽  
Blind Detection ◽  
False Detection Rate ◽  
False Detection ◽  
Local Maxima ◽  
Lyman Alpha Emitters ◽  
Integral Field Spectrograph

Context. One of the major science cases of the Multi Unit Spectroscopic Explorer (MUSE) integral field spectrograph is the detection of Lyman-alpha emitters at high redshifts. The on-going and planned deep fields observations will allow for one large sample of these sources. An efficient tool to perform blind detection of faint emitters in MUSE datacubes is a prerequisite of such an endeavor. Aims. Several line detection algorithms exist but their performance during the deepest MUSE exposures is hard to quantify, in particular with respect to their actual false detection rate, or purity. The aim of this work is to design and validate an algorithm that efficiently detects faint spatial-spectral emission signatures, while allowing for a stable false detection rate over the data cube and providing in the same time an automated and reliable estimation of the purity. Methods. The algorithm implements (i) a nuisance removal part based on a continuum subtraction combining a discrete cosine transform and an iterative principal component analysis, (ii) a detection part based on the local maxima of generalized likelihood ratio test statistics obtained for a set of spatial-spectral profiles of emission line emitters and (iii) a purity estimation part, where the proportion of true emission lines is estimated from the data itself: the distribution of the local maxima in the “noise only” configuration is estimated from that of the local minima. Results. Results on simulated data cubes providing ground truth show that the method reaches its aims in terms of purity and completeness. When applied to the deep 30 h exposure MUSE datacube in the Hubble Ultra Deep Field, the algorithms allows for the confirmed detection of 133 intermediate redshifts galaxies and 248 Lyα emitters, including 86 sources with no Hubble Space Telescope counterpart. Conclusions. The algorithm fulfills its aims in terms of detection power and reliability. It is consequently implemented as a Python package whose code and documentation are available on GitHub and readthedocs.

Automated Detection of Chemical Vapors by Pattern Recognition Analysis of Passive Multispectral Infrared Remote Sensing Imaging Data

2002 ◽  
Vol 56 (8) ◽  
pp. 1082-1093 ◽  
Author(s):  
Lin Zhang ◽  
Gary W. Small
Keyword(s):  
Pattern Recognition ◽  
Detection Rate ◽  
Clustering Algorithm ◽  
Multispectral Imaging ◽  
Planck Equation ◽  
Performance Criteria ◽  
Classification Rule ◽  
False Detection Rate ◽  
Imaging Data ◽  
False Detection

Pattern recognition methods are developed for the automated interpretation of passive multispectral imaging data collected from an airborne platform. Through the use of an infrared line scanner equipped with 14 spectral bandpass filters, passive infrared images are collected of an ammonia plant within a nitrogen fertilizer facility. Piecewise linear discriminant analysis is used to implement an automated algorithm for the detection of scene pixels that correspond to chemical vapor signatures. A separate classifier is used to detect the presence of hot carbon dioxide (CO2) within the images. In the assembly of training and prediction data for the development of both classifiers, the K-means clustering algorithm is used together with knowledge of the site to assign pixels to the plume/nonplume and CO2/non-CO2 categories. The effects of temperature variation within the imaged scene are removed from the data through the use of an algorithm for separating the contributions of temperature and emissivity to the Planck equation. Averaged across four data runs containing a total of 3.5 million pixels, the resulting discriminants are observed to detect approximately 91% of the plume pixels while achieving a false detection rate of less than 0.01%. The corresponding performance criteria for the CO2 classifier are a successful detection of approximately 94% of the pixels with a CO2 signature and a false detection rate of less than 0.7%. The robustness of the CO2 classifier is further enhanced through the adoption of a probability-based classification rule.

Using Dictionary Pair Learning for Seizure Detection

2019 ◽  
Vol 29 (04) ◽  
pp. 1850005 ◽  
Author(s):  
Xin Ma ◽  
Nana Yu ◽  
Weidong Zhou
Keyword(s):  
Detection Rate ◽  
Recognition Rate ◽  
Seizure Detection ◽  
False Detection Rate ◽  
False Detection ◽  
Minimization Method ◽  
System A ◽  
Training Samples ◽  
Eeg Recordings ◽  
Event Based

Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly [Formula: see text]-norm or [Formula: see text]-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530[Formula: see text]h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.

scholarly journals A Probabilistic Multispectral Pattern Recognition Method for Detection of Overshooting Cloud Tops Using Passive Satellite Imager Observations

2016 ◽  
Vol 55 (9) ◽  
pp. 1983-2005 ◽  
Author(s):  
Kristopher M. Bedka ◽  
Konstantin Khlopenkov
Keyword(s):  
Pattern Recognition ◽  
Detection Rate ◽  
Earth Science ◽  
Weather Conditions ◽  
The United States ◽  
Detection Methods ◽  
False Detection Rate ◽  
False Detection ◽  
Weather And Climate ◽  
Climate Analysis

AbstractDeep convective updrafts often penetrate through the surrounding cirrus anvil and into the lower stratosphere. Cross-tropopause transport of ice, water vapor, and chemicals occurs within these “overshooting tops” (OTs) along with a variety of hazardous weather conditions. OTs are readily apparent in satellite imagery, and, given the importance of OTs for weather and climate, a number of automated satellite-based detection methods have been developed. Some of these methods have proven to be relatively reliable, and their products are used in diverse Earth science applications. Nevertheless, analysis of these methods and feedback from product users indicate that use of fixed infrared temperature–based detection criteria often induces biases that can limit their utility for weather and climate analysis. This paper describes a new multispectral OT detection approach that improves upon those previously developed by minimizing use of fixed criteria and incorporating pattern recognition analyses to arrive at an OT probability product. The product is developed and validated using OT and non-OT anvil regions identified by a human within MODIS imagery. The product offered high skill for discriminating between OTs and anvils and matched 69% of human OT identifications for a particular probability threshold with a false-detection rate of 18%, outperforming previously existing methods. The false-detection rate drops to 1% when OT-induced texture detected within visible imagery is used to constrain the IR-based OT probability product. The OT probability product is also shown to improve severe-storm detection over the United States by 20% relative to the best existing method.

scholarly journals AN IMPROVED ARTIFICIAL DENDRITE CELL ALGORITHM FOR ABNORMAL SIGNAL DETECTION

2017 ◽  
Author(s):  
Mohamad Farhan Mohamad Mohsin ◽  
Azuraliza Abu Bakar ◽  
Abdul Razak Hamdan ◽  
Mohd Helmy Abdul Wahab
Keyword(s):  
Detection Rate ◽  
Extreme Values ◽  
Detection Accuracy ◽  
False Detection Rate ◽  
Detection Capability ◽  
False Detection ◽  
Abnormal Signal ◽  
Dendrite Cell ◽  
Data Points ◽  
Sensitivity Specificity

In dendrite cell algorithm (DCA), the abnormality of a data point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing MCAV is inefficient when exposed to extreme values. This causes the DCA fails to detect new data points if the pattern has distinct behavior from previous information and affects detection accuracy. This paper proposed an improved anomaly threshold solution for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability. In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV. From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy.  

scholarly journals An Improved K -Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bin Xie ◽  
Xinyu Dong ◽  
Changguang Wang
Keyword(s):  
Wireless Networks ◽  
Intrusion Detection ◽  
Wireless Network ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Network Data ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set ◽  
Network Intrusion

The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k -means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k -means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.

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