scholarly journals Feasibility Analyses of Real-Time Detection of Wildlife Using UAV-Derived Thermal and RGB Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2169
Author(s):  
Seunghyeon Lee ◽  
Youngkeun Song ◽  
Sung-Ho Kil
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Technological Development ◽  
Input Image ◽  
Training Data ◽  
Future Research ◽  
Wildlife Monitoring ◽  
Monitoring Methods ◽  
Rgb Images ◽  
Real Time Detection

Wildlife monitoring is carried out for diverse reasons, and monitoring methods have gradually advanced through technological development. Direct field investigations have been replaced by remote monitoring methods, and unmanned aerial vehicles (UAVs) have recently become the most important tool for wildlife monitoring. Many previous studies on detecting wild animals have used RGB images acquired from UAVs, with most of the analyses depending on machine learning–deep learning (ML–DL) methods. These methods provide relatively accurate results, and when thermal sensors are used as a supplement, even more accurate detection results can be obtained through complementation with RGB images. However, because most previous analyses were based on ML–DL methods, a lot of time was required to generate training data and train detection models. This drawback makes ML–DL methods unsuitable for real-time detection in the field. To compensate for the disadvantages of the previous methods, this paper proposes a real-time animal detection method that generates a total of six applicable input images depending on the context and uses them for detection. The proposed method is based on the Sobel edge algorithm, which is simple but can detect edges quickly based on change values. The method can detect animals in a single image without training data. The fastest detection time per image was 0.033 s, and all frames of a thermal video could be analyzed. Furthermore, because of the synchronization of the properties of the thermal and RGB images, the performance of the method was above average in comparison with previous studies. With target images acquired at heights below 100 m, the maximum detection precision and detection recall of the most accurate input image were 0.804 and 0.699, respectively. However, the low resolution of the thermal sensor and its shooting height limitation were hindrances to wildlife detection. The aim of future research will be to develop a detection method that can improve these shortcomings.

MobileNet-Yolo Based wildlife detection model: A case study in yunnan tongbiguan nature reserve, China

2021 ◽  
pp. 1-11
Author(s):  
Tingting Zhao ◽  
Xiaoli Yi ◽  
Zhiyong Zeng ◽  
Tao Feng
Keyword(s):  
Real Time ◽  
Nature Reserve ◽  
Complex Model ◽  
Training Data ◽  
Mean Average Precision ◽  
Detection Accuracy ◽  
High Definition ◽  
Average Precision ◽  
Data Set ◽  
Real Time Detection

YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3.

Automatic real-time detection method of faint solar-blind ultraviolet corona

2015 ◽  
Vol 8 (6) ◽  
pp. 926-932
Author(s):  
周影 ZHOU Ying ◽  
娄洪伟 LOU Hong-wei ◽  
周跃 ZHOU Yue ◽  
毕琳 BI Lin ◽  
张鑫磊 ZHANG Xin-lei
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Solar Blind ◽  
Real Time Detection

scholarly journals Real-Time Detection for Cache Side Channel Attack using Performance Counter Monitor

2020 ◽  
Vol 10 (3) ◽  
pp. 984 ◽  
Author(s):  
Jonghyeon Cho ◽  
Taehun Kim ◽  
Soojin Kim ◽  
Miok Im ◽  
Taehyun Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection Method ◽  
Machine Learning Algorithms ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Detection Techniques ◽  
Performance Counter ◽  
Secret Information ◽  
Real Time Detection

Cache side channel attacks extract secret information by monitoring the cache behavior of a victim. Normally, this attack targets an L3 cache, which is shared between a spy and a victim. Hence, a spy can obtain secret information without alerting the victim. To resist this attack, many detection techniques have been proposed. However, these approaches have limitations as they do not operate in real time. This article proposes a real-time detection method against cache side channel attacks. The proposed technique performs the detection of cache side channel attacks immediately after observing a variation of the CPU counters. For this, Intel PCM (Performance Counter Monitor) and machine learning algorithms are used to measure the value of the CPU counters. Throughout the experiment, several PCM counters recorded changes during the attack. From these observations, a detecting program was implemented by using these counters. The experimental results show that the proposed detection technique displays good performance for real-time detection in various environments.

DEVELOPMENT OF AN EVANESCENT FIELD SYSTEM FOR REAL-TIME DETECTION OF DNA HYBRIDIZATION

2002 ◽  
Vol 01 (05n06) ◽  
pp. 663-666
Author(s):  
DO-KYUN KIM ◽  
YOUNG-SOO KWON ◽  
EIICHI TAMIYA
Keyword(s):  
Real Time ◽  
Dna Hybridization ◽  
Detection Method ◽  
Evanescent Field ◽  
Surface Excitation ◽  
Oligonucleotide Hybridization ◽  
Real Time Detection ◽  
Field Excitation ◽  
Hybridization Reaction

In this research, we report the characterization of the probe and target oligonucleotide hybridization reaction using the evanescent field microscopy. For detection of DNA hybridization assay, a high-density array of sensor probes were prepared by randomly distributing a mixture of particles immobilized with oligonucleotides for DNA chip applications. With the evanescent field excitation and real-time detection method, we suggest that a very sharp discrimination of bulk fluorescence against surface excitation in combination with high excitation intensities can be achieved.

A Real-Time Sea-Sky-Line Detection Method under Complicated Sea-Sky Background

2012 ◽  
Vol 182-183 ◽  
pp. 1826-1831 ◽  
Author(s):  
Yang Gui ◽  
Xiao Hu Zhang ◽  
Yang Shang ◽  
Kun Peng Wang
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Computational Cost ◽  
Input Image ◽  
Line Detection ◽  
Image Position ◽  
Interior Points ◽  
Line Fitting ◽  
Ransac Algorithm ◽  
Correlation Matching

A real-time sea-sky-line detection method under complicated sea-sky background is presented. Firstly, a black-white template is constructed and used for fast correlation matching in several searching regions which are predefined in input image, position of maximal correlation coefficient in each predefined region is hunt out, and coordinates of several candidate sea-sky-line points are made certain according to the position. Then, RANSAC algorithm is applied to preserve interior points which are really on the sea-sky-line and eliminate exterior points which are not. Finally, line parameters of the sea-sky-line can be gained by applying least squares line fitting for all interior points. The pixels of several regions in the image instead of the whole image need to be considered, so computational cost can be reduced dramatically. The experimental results show that the proposed method can detect out sea-sky-line under complicated sea-sky background effectively and has many advantages such as strong robustness and speedy calculation.

scholarly journals A Real-Time Detection Method for Abnormal Data of Internet of Things Sensors Based on Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xuguang Liu
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Data Processing ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Spatiotemporal Correlation ◽  
Real Time Detection

Aiming at the anomaly detection problem in sensor data, traditional algorithms usually only focus on the continuity of single-source data and ignore the spatiotemporal correlation between multisource data, which reduces detection accuracy to a certain extent. Besides, due to the rapid growth of sensor data, centralized cloud computing platforms cannot meet the real-time detection needs of large-scale abnormal data. In order to solve this problem, a real-time detection method for abnormal data of IoT sensors based on edge computing is proposed. Firstly, sensor data is represented as time series; K-nearest neighbor (KNN) algorithm is further used to detect outliers and isolated groups of the data stream in time series. Secondly, an improved DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is proposed by considering spatiotemporal correlation between multisource data. It can be set according to sample characteristics in the window and overcomes the slow convergence problem using global parameters and large samples, then makes full use of data correlation to complete anomaly detection. Moreover, this paper proposes a distributed anomaly detection model for sensor data based on edge computing. It performs data processing on computing resources close to the data source as much as possible, which improves the overall efficiency of data processing. Finally, simulation results show that the proposed method has higher computational efficiency and detection accuracy than traditional methods and has certain feasibility.

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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