scholarly journals Cache Pollution Detection Method Based on GBDT in Information-Centric Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dapeng Man ◽  
Yongjia Mu ◽  
Jiafei Guo ◽  
Wu Yang ◽  
Jiguang Lv ◽  
...  
Keyword(s):  
Detection Method ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Model Learning ◽  
Pollution Attack ◽  
Pollution Attacks ◽  
Pollution Detection ◽  
Threshold Setting ◽  
Cache Pollution ◽  
Network Environments

There is a new cache pollution attack in the information-centric network (ICN), which fills the router cache by sending a large number of requests for nonpopular content. This attack will severely reduce the router cache hit rate. Therefore, the detection of cache pollution attacks is also an urgent problem in the current information center network. In the existing research on the problem of cache pollution detection, most of the methods of manually setting the threshold are used for cache pollution detection. The accuracy of the detection result depends on the threshold setting, and the adaptability to different network environments is weak. In order to improve the accuracy of cache pollution detection and adaptability to different network environments, this paper proposes a detection algorithm based on gradient boost decision tree (GBDT), which can obtain cache pollution detection through model learning. Method. In feature selection, the algorithm uses two features based on node status and path information as model input, which improves the accuracy of the method. This paper proves the improvement of the detection accuracy of this method through comparative experiments.

Robot Collision Detection Without External Sensors Based on Time-Series Analysis

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Tie Zhang ◽  
Peizhong Ge ◽  
Yanbiao Zou ◽  
Yingwu He
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Collision Detection ◽  
Degrees Of Freedom ◽  
Detection Method ◽  
Industrial Robot ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
External Torque ◽  
Series Analysis

Abstract To ensure the human safety in the process of human–robot cooperation, this paper proposes a robot collision detection method without external sensors based on time-series analysis (TSA). In the investigation, first, based on the characteristics of the external torque of the robot, the internal variation of the external torque sequence during the movement of the robot is analyzed. Next, a time-series model of the external torque is constructed, which is used to predict the external torque according to the historical motion information of the robot and generate a dynamic threshold. Then, the detailed process of time-series analysis for collision detection is described. Finally, the real-machine experiment scheme of the proposed real-time collision detection algorithm is designed and is used to perform experiments with a six degrees-of-freedom (6DOF) articulated industrial robot. The results show that the proposed method helps to obtain a detection accuracy of 100%; and that, as compared with the existing collision detection method based on a fixed symmetric threshold, the proposed method based on TSA possesses smaller detection delay and is more feasible in eliminating the sensitivity difference of collision detection in different directions.

scholarly journals An Improved Object Detection Method using Feature Map Refinement and Anchor Optimization

2021 ◽  
Vol 15 ◽  
pp. 541-549
Author(s):  
Yuxia Wang ◽  
Wenzhu Yang ◽  
Tongtong Yuan ◽  
Qian Li
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Search Space ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Semantic Features ◽  
Feature Map ◽  
Input Size ◽  
Fusion Operation ◽  
Different Levels

Lower detection accuracy and insufficient detection ability for small objects are the main problems of the region-free object detection algorithm. Aiming at solving the abovementioned problems, an improved object detection method using feature map refinement and anchor optimization is proposed. Firstly, the reverse fusion operation is performed on each of the object detection layer, which can provide the lower layers with more semantic information by the fusion of detection features at different levels. Secondly, the self-attention module is used to refine each detection feature map, calibrates the features between channels, and enhances the expression ability of local features. In addition, the anchor optimization model is introduced on each feature layer associated with anchors, and the anchors with higher probability of containing an object and more closely match the location and size of the object are obtained. In this model, semantic features are used to confirm and remove negative anchors to reduce search space of the objects, and preliminary adjustments are made to the locations and sizes of anchors. Comprehensive experimental results on PASCAL VOC detection dataset demonstrate the effectiveness of the proposed method. In particular, with VGG-16 and lower dimension 300×300 input size, the proposed method achieves a mAP of 79.1% on VOC 2007 test set with an inference speed of 24.7 milliseconds per image.

scholarly journals Research on insulator defect detection algorithm of transmission line based on CenterNet

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255135
Author(s):  
Chunming Wu ◽  
Xin Ma ◽  
Xiangxu Kong ◽  
Haichao Zhu
Keyword(s):  
Transmission Line ◽  
Defect Detection ◽  
Detection Method ◽  
Detection Efficiency ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Detection Algorithm ◽  
Electric Power System ◽  
Stable Operation ◽  
Detection Accuracy

The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.

scholarly journals DEFECTS DETECTION METHOD BASED ON K-MEANS WITH PRIOR KNOWLEDGE FOR BIOMASS PARTICLES

2020 ◽  
Vol 61 (2) ◽  
pp. 225-232 ◽  
Author(s):  
Wei Wang ◽  
Yuan Juan Gong
Keyword(s):  
Prior Knowledge ◽  
Detection Method ◽  
Vision System ◽  
Detection Algorithm ◽  
Biomass Energy ◽  
Detection Accuracy ◽  
Detection Time ◽  
Region Detection ◽  
High Detection ◽  
Biomass Particles

Biomass particle is one of the most important solid briquette fuels for agricultural and forestry biomass energy. Temperature, pressure, moisture and discharge holes are important factors to control biomass particle forming. The inappropriate setting of the parameters or blocking of the discharge hole will lead to the defects of the biomass particles, such as too short or poor roundness or pits or cracks. In order to detect these defects automatically, this paper proposes a method based on K-Means with prior knowledge. Firstly, the inner boundary tracking region detection algorithm and filling algorithm are combined to extract the regions in the backlight image. The regions are divided into debris, independent biomass particle regions and adhesive biomass particle regions. Secondly, K-Means with prior knowledge is used to segment the adhesive regions to get the independent biomass particle regions. Finally, the features of the biomass particles are extracted to judge the type of defects. The proposed method has been tested on images acquired from the vision system of the ring roller pellet mill. Experimental results show the efficiency of the proposed method in high detection accuracy and short detection time.

scholarly journals Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 576
Author(s):  
Kaihua Zhang ◽  
Haikuo Shen
Keyword(s):  
Solder Joint ◽  
Defect Detection ◽  
Data Augmentation ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Set ◽  
Electronic Products ◽  
Original Algorithm

The miniaturization and high integration of electronic products have higher and higher requirements for welding of internal components of electronic products. A welding quality detection method has always been one of the important research contents in the industry, among which, the research on solder joint defect detection of a connector has gradually attracted people’s attention with the development of image detection algorithm. The traditional solder joint detection method of connector adopts manual detection or automatic detection methods, which is inefficient and not safe enough. With the development of deep learning, the application of a deep convolutional neural network to target detection has become a research hotspot. In this paper, a data set of connector solder joint samples was made and the number of image samples was expanded to more than 3 times of the original by using data augmentation. Clustering generates anchor boxes and transfer learning with ResNet-101 were fused, so an improved faster region-based convolutional neural networks (Faster RCNN) algorithm was proposed. The experiment verified that the improved algorithm proposed in this paper had a great improvement in all aspects compared with the original algorithm. The average detection accuracy of this method can reach 94%, and the detection rate of some defects can even reach 100%, which can completely meet the industrial requirements.

scholarly journals Texture Construction Edge Detection Algorithm

2019 ◽  
Vol 9 (5) ◽  
pp. 897 ◽  
Author(s):  
Shou-Cih Chen ◽  
Chung-Cheng Chiu
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Edge Information ◽  
Edge Detection Algorithm ◽  
Detection Technology ◽  
Texture Change ◽  
Object Edge ◽  
Edge Detection Method ◽  
Threshold Setting

The edge detection algorithm is the cornerstone of image processing; a good edge detection result can further extract the required information through rich texture information and achieve object detection, segmentation, and identification. To obtain a rich texture edge detection technology, this paper proposes using edge texture change for edge construction and constructs the edge contour through constructing an edge texture extension between the blocks to reduce the missing edge problem caused by the threshold setting. Finally, through verification of the experimental results, the proposed method can effectively overcome the problem caused by unsuitable threshold setting and detect rich object edge information compared to the adaptive edge detection method.

Accuracy Analysis of Centroid Detection Method Used to Star Sensor

2012 ◽  
Vol 468-471 ◽  
pp. 401-404 ◽  
Author(s):  
Qi Li ◽  
Wei Xu ◽  
Zhi Hai Xu ◽  
Hua Jun Feng
Keyword(s):  
Simulation Experiment ◽  
Detection Method ◽  
Detection Algorithm ◽  
Motion Blur ◽  
Detection Accuracy ◽  
Star Sensor ◽  
Detection Algorithms ◽  
Satellite Attitude ◽  
Centroid Detection ◽  
Acceptable Parameter

Star sensor is important equipment for measuring satellite attitude and motion, and star centroid detection accuracy is the basis of the overall accuracy of star sensor. In star sensors, slightly- defocus method is often adopted to acquire dispersive light spots so as to facilitate centroid detection, and certain motion blur can also be introduced because of the motion of satellites. In this paper, we analyzed several commonly-used centroid detection algorithms by using simulation experiment to study the influence of defocus and motion parameters on the accuracy of centroid detection algorithm and provided acceptable parameter value ranges.

scholarly journals Fast Cartoon-Texture Decomposition Filtering Based License Plate Detection Method

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yingjun Wang ◽  
Chenping Zhao ◽  
Xiaoyan Liu ◽  
Mingfu Zhao ◽  
Linfeng Bai
Keyword(s):  
High Efficiency ◽  
Detection Method ◽  
Correction Method ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
License Plate ◽  
License Plate Recognition ◽  
Background Interference ◽  
Morphological Filtering ◽  
License Plate Detection

Vehicle license plate detection is an important step in automatic license plate recognition, which is prone to be influenced by the background interference and complex environment conditions. It is known that cartoon-texture decomposition split an image into geometric cartoon and texture component, which can remove background interference away from the vehicle image. In this paper, we introduce a fast cartoon-texture decomposition filter into the detection process. Combining the edge detection, morphological filtering and Radon transform based tilt correction method, we formulate a new license plate detection algorithm. Experiment results confirm that the proposed algorithm can remove background interference away, inhibit the emergence of fake license plates, and improve the detection accuracy. Moreover, there is no inner loop iteration in the new algorithm, so it is fast and high-efficiency.

scholarly journals An easy iris center detection method for eye gaze tracking system

2015 ◽  
Vol 8 (3) ◽  
Author(s):  
Mingxin Yu ◽  
Yingzi Lin ◽  
Xiaoying Tang ◽  
Jing Xu ◽  
David Schmidt ◽  
...  
Keyword(s):  
Detection Method ◽  
Tracking System ◽  
Eye Gaze ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ellipse Fitting ◽  
Gaze Tracking ◽  
Edge Points ◽  
Fitting In ◽  
Iris Center

Iris center detection accuracy has great impact on eye gaze tracking system performance. This paper proposes an easy and efficient iris center detection method based on modeling the geometric relationship between the detected rough iris center and the two corners of the eye. The method fully considers four states of iris within the eye region, i.e. center, left, right, and upper. The proposed active edge detection algorithm is utilized to extract iris edge points for ellipse fitting. In addition, this paper also presents a predicted edge point algorithm to solve the decrease in ellipse fitting accuracy, when part of the iris becomes hidden from rolling into a nasal or temporal eye corner. The evaluated result of the method on our eye database shows the global average accuracy of 94.3%. Compared with existing methods, our method achieves the highest iris center detection accuracy. Additionally, in order to test the performance of the proposed method in gaze tracking, this paper presents the results of gaze estimation achieved by our eye gaze tracking system.

scholarly journals Size Detection Method of Embedded Ball Based on Aviation Bearing Image

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ping Xue ◽  
Yihui Wang ◽  
Hongmin Wang
Keyword(s):  
Detection Rate ◽  
Light Propagation ◽  
Detection Method ◽  
Detection Algorithm ◽  
Size Difference ◽  
Detection Accuracy ◽  
False Detection Rate ◽  
False Detection ◽  
Imaging Characteristics ◽  
Ball Detection

In the aerospace industry, bearing is widely used in various rotating machinery. The performance of bearing affects the operation of the whole machinery and even aviation equipment. The wrongly assembled ball due to size is an important reason for unqualified bearing. To solve this problem, an accurate ball detection method based on the bearing image is proposed. Firstly, according to the imaging characteristics of bearing and light propagation characteristics, an image collection system based on the coaxial light source is designed. Then, aiming at the problem that the embedded ball is occluded by the bearing ring and the cage, only partial ball in the narrow gap can be used to predict the full ball and the high-precision requirement of ball detection, a ball segmentation model based on DeepLab v3+ network is used to segment the local ball, and CBAM is added in the Xception network of the original network. According to the characteristics of the segmentation result, a circle detection algorithm based on circle fitting evaluation designed for incomplete short arc is proposed. Finally, according to the detection results, judge whether the bearing is qualified or not and evaluate the feasibility of this method. Experimental results show that the ball detection accuracy is about 27 microns, and the wrongly assembled ball with a size difference of only 198 microns can be distinguished. The false detection rate of unqualified bearing is 1%. As the last line of defense of bearing quality inspection, this method can achieve zero false detection rate of unqualified bearing in the industry.

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