Real-time Network Attack Intention Recognition Algorithm

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

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Real-Time Face Occlusion Recognition Algorithm Based on Feature Fusion

Biometric Recognition - Lecture Notes in Computer Science ◽

10.1007/978-3-030-31456-9_29 ◽

2019 ◽

pp. 258-265

Author(s):

Xiangde Zhang ◽

Bin Zheng ◽

Yuanjie Li ◽

Lianping Yang

Keyword(s):

Real Time ◽

Feature Fusion ◽

Recognition Algorithm

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A Novel Real-Time Gesture Recognition Algorithm for Human-Robot Interaction on the UAV

Lecture Notes in Computer Science - Computer Vision Systems ◽

10.1007/978-3-319-68345-4_46 ◽

2017 ◽

pp. 518-526

Author(s):

Bo Chen ◽

Chunsheng Hua ◽

Jianda Han ◽

Yuqing He

Keyword(s):

Real Time ◽

Gesture Recognition ◽

Recognition Algorithm ◽

Human Robot Interaction ◽

Robot Interaction

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Research of D-S Evidence Method in Network Attack Intention Recognition

Proceedings of the 2nd International Conference on Electronic and Mechanical Engineering and Information Technology (2012) ◽

10.2991/emeit.2012.516 ◽

2012 ◽

Cited By ~ 2

Author(s):

Yajing Zhang ◽

Lina Yu ◽

Wei Li

Keyword(s):

Network Attack ◽

Intention Recognition

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Intelligent Highway Traffic Detection Algorithm based on Deep Learning

10.21203/rs.3.rs-805608/v1 ◽

2021 ◽

Author(s):

ming ji ◽

Chuanxia Sun ◽

Yinglei Hu

Keyword(s):

Deep Learning ◽

Real Time ◽

Traffic Congestion ◽

Road Network ◽

Vehicle Detection ◽

Detection Algorithm ◽

Recognition Algorithm ◽

Traffic Data ◽

Vehicle Recognition ◽

Environment Recognition

Abstract In order to solve the increasingly serious traffic congestion problem, an intelligent transportation system is widely used in dynamic traffic management, which effectively alleviates traffic congestion and improves road traffic efficiency. With the continuous development of traffic data acquisition technology, it is possible to obtain real-time traffic data in the road network in time. A large amount of traffic information provides a data guarantee for the analysis and prediction of road network traffic state. Based on the deep learning framework, this paper studies the vehicle recognition algorithm and road environment discrimination algorithm, which greatly improves the accuracy of highway vehicle recognition. Collect highway video surveillance images in different environments, establish a complete original database, build a deep learning model of environment discrimination, and train the classification model to realize real-time environment recognition of highway, as the basic condition of vehicle recognition and traffic event discrimination, and provide basic information for vehicle detection model selection. To improve the accuracy of road vehicle detection, the vehicle target labeling and sample preprocessing of different environment samples are carried out. On this basis, the vehicle recognition algorithm is studied, and the vehicle detection algorithm based on weather environment recognition and fast RCNN model is proposed. Then, the performance of the vehicle detection algorithm described in this paper is verified by comparing the detection accuracy differences between different environment dataset models and overall dataset models, different network structures and deep learning methods, and other methods.

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A Robust Real-Time Automatic Recognition Prototype for Maritime Optical Morse-Based Communication Employing Modified Clustering Algorithm

Applied Sciences ◽

10.3390/app10041227 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1227 ◽

Cited By ~ 1

Author(s):

Xiaozheng Wang ◽

Minglun Zhang ◽

Hongyu Zhou ◽

Xinglong Lin ◽

Xiaomin Ren

Keyword(s):

Real Time ◽

Clustering Algorithm ◽

Signal To Noise Ratio ◽

Light Emitting Diode ◽

Experimental Tests ◽

Recognition Algorithm ◽

Automatic Recognition ◽

Decision Threshold ◽

Low Snr ◽

Led Array

In maritime communications, the ubiquitous Morse lamp on ships plays a significant role as one of the most common backups to radio or satellites just in case. Despite the advantages of its simplicity and efficiency, the requirement of trained operators proficient in Morse code and maintaining stable sending speed pose a key challenge to this traditional manual signaling manner. To overcome these problems, an automatic system is needed to provide a partial substitute for human effort. However, few works have focused on studying an automatic recognition scheme of maritime manually sent-like optical Morse signals. To this end, this paper makes the first attempt to design and implement a robust real-time automatic recognition prototype for onboard Morse lamps. A modified k-means clustering algorithm of machine learning is proposed to optimize the decision threshold and identify elements in Morse light signals. A systematic framework and detailed recognition algorithm procedure are presented. The feasibility of the proposed system is verified via experimental tests using a light-emitting diode (LED) array, self-designed receiver module, and microcontroller unit (MCU). Experimental results indicate that over 99% of real-time recognition accuracy is realized with a signal-to-noise ratio (SNR) greater than 5 dB, and the system can achieve good robustness under conditions with low SNR.

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