scholarly journals A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 552-577
Author(s):  
Mai Ibraheam ◽  
Kin Fun Li ◽  
Fayez Gebali ◽  
Leonard E. Sielecki
Keyword(s):  
Object Detection ◽  
Network Architecture ◽  
Animal Species ◽  
Detection System ◽  
Real Life ◽  
Medical Diagnostics ◽  
Performance Comparison ◽  
Species Detection ◽  
Detection Techniques ◽  
Wide Range

Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.

T1D Hypoglycemia Prediction Algorithms: A Systematic Review (Preprint)

2021 ◽  
Author(s):  
Stella Tsichlaki ◽  
Lefteris Koumakis ◽  
Manolis Tsiknakis
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Digital Libraries ◽  
Predictive Models ◽  
Real Life ◽  
Blood Glucose Monitoring ◽  
Detection Techniques ◽  
Glucose Levels ◽  
Wide Range

BACKGROUND Diabetes is a chronic condition that necessitates regular monitoring and self-management of the patient's blood glucose levels. People with type 1 diabetes (T1D) can live a productive life if they receive proper diabetes care. Nonetheless, a loose glycemic control might increase the risk of developing hypoglycemia. This incident can occur due to a variety of causes, such as taking additional doses of insulin, skipping meals, or over-exercising. Mainly, the symptoms of hypoglycemia range from mild dysphoria to more severe conditions, if not detected in a timely manner. OBJECTIVE In this review, we report on innovative detection techniques and tactics for identifying and preventing hypoglycemic episodes, focusing on type 1 diabetes. METHODS A systematic literature search following the PRISMA guidelines was performed focusing on the “PUBMED”, “Google Scholar”, “IEEE Xplore” and “ACM” digital libraries to find articles about technologies related to hypoglycemia detection in type 1 diabetes patients. RESULTS The presented approaches have been utilized or devised to enhance blood glucose monitoring and boost its efficacy to forecast future glucose levels, which could aid the prediction of future episodes of hypoglycemia. We detected nineteen predictive models for hypoglycemia, specifically on type 1 diabetes, utilizing a wide range of algorithmic methodologies, spanning from statistics (10%) to machine learning (52%) and deep learning (38%). The algorithms employed most are the kalman filtering and classification models (SVM, KNN, random forests). The performance of the predictive models was found overall to be satisfactory, reaching accuracies between 70% and 99% which proves that such technologies are capable to facilitate the prediction of T1D hypoglycemia. CONCLUSIONS It is evident that CGM can improve the glucose control in diabetes but predictive models for hypo- and hyper- glycemia using only mainstream noninvasive sensors such as wristbands and smartwatches are foreseen to be the next step for mHealth in T1D. Prospective studies are required to demonstrate the value of such models in real-life mHealth interventions.

Hybrid Intrusion Detection Framework for Ad hoc networks

2016 ◽  
Vol 10 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Abdelaziz Amara Korba ◽  
Mehdi Nafaa ◽  
Salim Ghanemi
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Ad Hoc Networks ◽  
Intrusion Detection System ◽  
Ad Hoc ◽  
Detection System ◽  
Security Framework ◽  
Detection Techniques ◽  
Wide Range ◽  
Hoc Networks

In this paper, a cluster-based hybrid security framework called HSFA for ad hoc networks is proposed and evaluated. The proposed security framework combines both specification and anomaly detection techniques to efficiently detect and prevent wide range of routing attacks. In the proposed hierarchical architecture, cluster nodes run a host specification-based intrusion detection system to detect specification violations attacks such as fabrication, replay, etc. While the cluster heads run an anomaly-based intrusion detection system to detect wormhole and rushing attacks. The proposed specification-based detection approach relies on a set of specifications automatically generated, while anomaly-detection uses statistical techniques. The proposed security framework provides an adaptive response against attacks to prevent damage to the network. The security framework is evaluated by simulation in presence of malicious nodes that can launch different attacks. Simulation results show that the proposed hybrid security framework performs significantly better than other existing mechanisms.

scholarly journals Methods and Algorithms for Detecting Objects in Video Files

2018 ◽  
Vol 155 ◽  
pp. 01016 ◽  
Author(s):  
Cuong Nguyen The ◽  
Dmitry Shashev
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Traffic Control ◽  
Motion Pictures ◽  
Real Life ◽  
Video File ◽  
Detection And Tracking ◽  
Object Movement ◽  
Wide Range ◽  
Automated Processing

Video files are files that store motion pictures and sounds like in real life. In today's world, the need for automated processing of information in video files is increasing. Automated processing of information has a wide range of application including office/home surveillance cameras, traffic control, sports applications, remote object detection, and others. In particular, detection and tracking of object movement in video file plays an important role. This article describes the methods of detecting objects in video files. Today, this problem in the field of computer vision is being studied worldwide.

scholarly journals Automated Detection of Animals in Low-Resolution Airborne Thermal Imagery

2021 ◽  
Vol 13 (16) ◽  
pp. 3276
Author(s):  
Anwaar Ulhaq ◽  
Peter Adams ◽  
Tarnya E. Cox ◽  
Asim Khan ◽  
Tom Low ◽  
...  
Keyword(s):  
Object Detection ◽  
Thermal Imaging ◽  
Animal Species ◽  
Detection Algorithm ◽  
Automated Detection ◽  
Small Object ◽  
Low Resolution ◽  
Thermal Imagery ◽  
Species Detection ◽  
Thermal Imagers

Detecting animals to estimate abundance can be difficult, particularly when the habitat is dense or the target animals are fossorial. The recent surge in the use of thermal imagers in ecology and their use in animal detections can increase the accuracy of population estimates and improve the subsequent implementation of management programs. However, the use of thermal imagers results in many hours of captured flight videos which require manual review for confirmation of species detection and identification. Therefore, the perceived cost and efficiency trade-off often restricts the use of these systems. Additionally, for many off-the-shelf systems, the exported imagery can be quite low resolution (<9 Hz), increasing the difficulty of using automated detections algorithms to streamline the review process. This paper presents an animal species detection system that utilises the cost-effectiveness of these lower resolution thermal imagers while harnessing the power of transfer learning and an enhanced small object detection algorithm. We have proposed a distant object detection algorithm named Distant-YOLO (D-YOLO) that utilises YOLO (You Only Look Once) and improves its training and structure for the automated detection of target objects in thermal imagery. We trained our system on thermal imaging data of rabbits, their active warrens, feral pigs, and kangaroos collected by thermal imaging researchers in New South Wales and Western Australia. This work will enhance the visual analysis of animal species while performing well on low, medium and high-resolution thermal imagery.

An Ultrasonic Double-Sheet Detection System for Collators

1992 ◽  
Vol 114 (4) ◽  
pp. 489-493 ◽  
Author(s):  
M. Shiraishi ◽  
Y. Fujinuma ◽  
T. Ishikawa ◽  
K. Ishige ◽  
H. Doki
Keyword(s):  
Detection System ◽  
Ultrasonic Method ◽  
Temperature Fluctuations ◽  
Threshold Level ◽  
Ultrasonic Signal ◽  
Air Curtain ◽  
Detection Techniques ◽  
Wide Range ◽  
Gain Adjustment ◽  
Conventional Detection

A new ultrasonic method of detecting double sheets in collators has been developed that overcomes several shortcomings of conventional detection techniques. An air curtain efficiently reduces the ultrasonic detector’s vulnerability to ambient temperature fluctuations. The accuracy of detection is enhanced by utilizing the low-level component of the received ultrasonic signal. A gain adjustment technique is introduced which enables detection for a wide range of paper stocks using a single threshold level.

scholarly journals Stealthy Attacks against Robotic Vehicles Protected by Control-based Intrusion Detection Techniques

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Pritam Dash ◽  
Mehdi Karimibiuki ◽  
Karthik Pattabiraman
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Physical World ◽  
Sensors And Actuators ◽  
Detection Techniques ◽  
Industrial Sectors ◽  
Wide Range ◽  
Robotic Vehicles ◽  
Automated Algorithms ◽  
Stealthy Attacks

Robotic vehicles (RV) are increasing in adoption in many industrial sectors. RVs use auto-pilot software for perception and navigation and rely on sensors and actuators for operating autonomously in the physical world. Control algorithms have been used in RVs to minimize the effects of noisy sensors, prevent faulty actuator output, and, recently, to detect attacks against RVs. In this article, we demonstrate the vulnerabilities in control-based intrusion detection techniques and propose three kinds of stealthy attacks that evade detection and disrupt RV missions. We also propose automated algorithms for performing the attacks without requiring the attacker to expend significant effort or to know specific details of the RV, thus making the attacks applicable to a wide range of RVs. We demonstrate the attacks on eight RV systems including three real vehicles in the presence of an Intrusion Detection System using control-based techniques to monitor RV’s runtime behavior and detect attacks. We find that the control-based techniques are incapable of detecting our stealthy attacks and that the attacks can have significant adverse impact on the RV’s mission (e.g., deviate it significantly from its target, or cause it to crash).

scholarly journals Fast and Real Life Object Detection System Using Simple Webcam

2018 ◽  
Vol 6 (4) ◽  
pp. 18-23
Author(s):  
Md.T. Akhtar ◽  
S.T. Razi ◽  
K.N. Jaman ◽  
A. Azimusshan ◽  
Md.A. Sohel
Keyword(s):  
Object Detection ◽  
Detection System ◽  
Real Life

scholarly journals REAL TIME OBJECT DETECTION USING YOLO ALGORITHM

2021 ◽  
Vol 10 (7) ◽  
pp. 61-67
Author(s):  
Bobburi Taralathasri ◽  
Dammati Vidya Sri ◽  
Gadidammalla Narendra Kumar ◽  
Annam Subbarao ◽  
Palli R Krishna Prasad
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Detection System ◽  
Image Data ◽  
Image Detection ◽  
The Core ◽  
Wide Range ◽  
Driverless Cars ◽  
Learning Technique

The major and wide range applications like Driverless cars, robots, Image surveillance has become famous in the Computer vision .Computer vision is the core in all those applications which is responsible for the image detection and it became more popular worldwide. Object Detection System using Deep Learning Technique” detects objects efficiently based on YOLO algorithm and applies the algorithm on image data to detect objects.

scholarly journals Deep Learning-Based Detection of Articulatory Features in Arabic and English Speech

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1205
Author(s):  
Mohammed Algabri ◽  
Hassan Mathkour ◽  
Mansour M. Alsulaiman ◽  
Mohamed A. Bencherif
Keyword(s):  
Object Detection ◽  
Speech Signal ◽  
State Of The Art ◽  
Detection System ◽  
Phoneme Recognition ◽  
Detection Techniques ◽  
Articulatory Features ◽  
Arabic And English ◽  
Scale Detection ◽  
Phoneme Detection

This study proposes using object detection techniques to recognize sequences of articulatory features (AFs) from speech utterances by treating AFs of phonemes as multi-label objects in speech spectrogram. The proposed system, called AFD-Obj, recognizes sequence of multi-label AFs in speech signal and localizes them. AFD-Obj consists of two main stages: firstly, we formulate the problem of AFs detection as an object detection problem and prepare the data to fulfill requirement of object detectors by generating a spectral three-channel image from the speech signal and creating the corresponding annotation for each utterance. Secondly, we use annotated images to train the proposed system to detect sequences of AFs and their boundaries. We test the system by feeding spectrogram images to the system, which will recognize and localize multi-label AFs. We investigated using these AFs to detect the utterance phonemes. YOLOv3-tiny detector is selected because of its real-time property and its support for multi-label detection. We test our AFD-Obj system on Arabic and English languages using KAPD and TIMIT corpora, respectively. Additionally, we propose using YOLOv3-tiny as an Arabic phoneme detection system (i.e., PD-Obj) to recognize and localize a sequence of Arabic phonemes from whole speech utterances. The proposed AFD-Obj and PD-Obj systems achieve excellent results for Arabic corpus and comparable to the state-of-the-art method for English corpus. Moreover, we showed that using only one-scale detection is suitable for AFs detection or phoneme recognition.

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