scholarly journals Ship Target Detection Algorithm Based on Improved YOLOv5

2021 ◽  
Vol 9 (8) ◽  
pp. 908
Author(s):  
Junchi Zhou ◽  
Ping Jiang ◽  
Airu Zou ◽  
Xinglin Chen ◽  
Wenwu Hu
Keyword(s):  
Visual Detection ◽  
Detection System ◽  
Recall Rate ◽  
Detection Algorithm ◽  
Average Precision ◽  
Fast Detection ◽  
Detection And Identification ◽  
Small Targets ◽  
Improved Model ◽  
Field Area

In order to realize the real-time detection of an unmanned fishing speedboat near a ship ahead, a perception platform based on a target visual detection system was established. By controlling the depth and width of the model to analyze and compare training, it was found that the 5S model had a fast detection speed but low accuracy, which was judged to be insufficient for detecting small targets. In this regard, this study improved the YOLOv5s algorithm, in which the initial frame of the target is re-clustered by K-means at the data input end, the receptive field area is expanded at the output end, and the loss function is optimized. The results show that the precision of the improved model’s detection for ship images was 98.0%, and the recall rate was 96.2%. Mean average precision (mAP) reached 98.6%, an increase of 4.4% compared to before the improvements, which shows that the improved model can realize the detection and identification of multiple types of ships, laying the foundation for subsequent path planning and automatic obstacle avoidance of unmanned ships.

Mechanism for Visual Detection of Small Targets in Insects

2013 ◽  
Author(s):  
David C. O'Carroll ◽  
Steven D. Wiederman ◽  
Patrick A. Shoemaker
Keyword(s):  
Visual Detection ◽  
Small Targets

scholarly journals Design of bio-inspired binocular UAV detection system based on improved STC algorithm of scale transformation and occlusion detection

2021 ◽  
Vol 13 ◽  
pp. 175682932110048
Author(s):  
Huajun Song ◽  
Yanqi Wu ◽  
Guangbing Zhou
Keyword(s):  
Detection System ◽  
Rapid Development ◽  
Reference Value ◽  
Scale Transformation ◽  
Hardware System ◽  
Time Requirements ◽  
Aerial Vehicle ◽  
Spatio Temporal ◽  
Small Targets ◽  
Vision Detection

With the rapid development of drones, many problems have arisen, such as invasion of privacy and endangering security. Inspired by biology, in order to achieve effective detection and robust tracking of small targets such as unmanned aerial vehicles, a binocular vision detection system is designed. The system is composed of long focus and wide-angle dual cameras, servo pan tilt, and dual processors for detecting and identifying targets. In view of the shortcomings of spatio-temporal context target tracking algorithm that cannot adapt to scale transformation and easy to track failure in complex scenes, the scale filter and loss criterion are introduced to make an improvement. Qualitative and quantitative experiments show that the designed system can adapt to the scale changes and partial occlusion conditions in the detection, and meets the real-time requirements. The hardware system and algorithm both have reference value for the application of anti-unmanned aerial vehicle systems.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

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.

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

scholarly journals A Fast Detection Algorithm for the X-Ray Pulsar Signal

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Hao Liang ◽  
Yafeng Zhan
Keyword(s):  
False Alarm ◽  
Navigation System ◽  
Detection Probability ◽  
Autonomous Navigation ◽  
Detection Algorithm ◽  
Observation Time ◽  
X Ray ◽  
Fast Detection ◽  
Simulation Results ◽  
Short Observation Time

The detection of the X-ray pulsar signal is important for the autonomous navigation system using X-ray pulsars. In the condition of short observation time and limited number of photons for detection, the noise does not obey the Gaussian distribution. This fact has been little considered extant. In this paper, the model of the X-ray pulsar signal is rebuilt as the nonhomogeneous Poisson distribution and, in the condition of a fixed false alarm rate, a fast detection algorithm based on maximizing the detection probability is proposed. Simulation results show the effectiveness of the proposed detection algorithm.

scholarly journals Event-triggered attack detection and identification in power-electronics-based DC microgrid

2021 ◽  
Author(s):  
Sicheng Gong
Keyword(s):  
Power Flow ◽  
Detection Algorithm ◽  
Power Line ◽  
Current Data ◽  
Attack Detection ◽  
Dc Microgrid ◽  
Detection Mechanism ◽  
Detection And Identification ◽  
Event Triggered ◽  
Identification Strategy

This paper proposes a novel event-triggered attack detection mechanism for converter-based DC microgrid system. Under a distributive network framework, each node collects its neighbours' relative data to regulate its own output for local stabilization. Without power line current data, hardly can an agent directly identify the source of unexpected power flow, especially under an organized attack composed of voltage variations and corresponding deceptive messages. In order to recognize traitors who broadcast wrong data, target at system distortion and even splitting, an efficient attack detection and identification strategy is mandatory. After the attack detector is triggered, each relative agent refuses to trust any received data directly before authentication. Through proposed two-step verification by comparing theoretical estimated signals with received ones, both self sensors and neighbour nodes would be inspected, and the attacker was difficult to hide himself. Through simulation on SIMULINK/PLECS and hardware experiments on dSpace Platform, the effectiveness of proposed detection algorithm has been proved.

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