Intelligent Detection Method Research Articles

Intelligent passive detection of aerial target in space-air-ground integrated networks

Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks (SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is −36dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios.

Infrared Thermographic Fault Detection Using Machine Vision With Convolutional Neural Network for Blast Furnace Chute

The metallurgical industry is the manufacturing industry with the highest carbon emissions in China. Carbon emission in blast furnace accounts for 73.6% of the total long process flow. The working state of chute directly affects the longevity, safety, efficiency and low-carbon of blast furnace. However, at present, there is no monitoring method for chute damage. In this paper, an intelligent chute fault detection method based on numerical simulation, machine vision and deep learning is proposed. Specifically, discrete element method (DEM), gray level co-occurrence matrix (GLCM) and kernel correlation filter (KCF) modules are introduced to screen the best detection frames. Comprehensive experiments show that the proposed algorithm is superior to other traditional classification methods and other deep learning classification network (SVM and LetNet-5), with an accuracy of 96.1%. This method not only contributes to understand the wear mechanism of rotating chute, but also provides a reference for automatic wear monitoring of chute.

An intelligent image detection method using improved canny edge detection operator

In order to meet the requirement of edge detection of paper disease image in papermaking process, a paper disease image detection method based on improved Canny operator is proposed. Firstly, according to the principle of Gauss filtering and the method of feature statistical analysis, the filtering function and window are selected adaptively. Then, in the gradient solution, the traditional 2 × 2 neighbourhood is replaced by the 3 × 2 or 2 × 3 neighbourhood which enhances the weight of the intermediate pixel, and the accuracy of edge detection is improved by enhancing the influence of the intermediate pixel. Finally, the iterative averaging method is used to determine the optimal threshold and reduce the error rate of image edge segmentation. The experimental results show that this method can effectively detect the edges of paper disease area and has good edge continuity.

CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

In today’s real world, an important research part in image processing is scene text detection and recognition. Scene text can be in different languages, fonts, sizes, colours, orientations and structures. Moreover, the aspect ratios and layouts of a scene text may differ significantly. All these variations appear assignificant challenges for the detection and recognition algorithms that are considered for the text in natural scenes. In this paper, a new intelligent text detection and recognition method for detectingthe text from natural scenes and forrecognizing the text by applying the newly proposed Conditional Random Field-based fuzzy rules incorporated Convolutional Neural Network (CR-CNN) has been proposed. Moreover, we have recommended a new text detection method for detecting the exact text from the input natural scene images. For enhancing the presentation of the edge detection process, image pre-processing activities such as edge detection and color modeling have beenapplied in this work. In addition, we have generated new fuzzy rules for making effective decisions on the processes of text detection and recognition. The experiments have been directedusing the standard benchmark datasets such as the ICDAR 2003, the ICDAR 2011, the ICDAR 2005 and the SVT and have achieved better detection accuracy intext detection and recognition. By using these three datasets, five different experiments have been conducted for evaluating the proposed model. And also, we have compared the proposed system with the other classifiers such as the SVM, the MLP and the CNN. In these comparisons, the proposed model has achieved better classification accuracywhen compared with the other existing works.

Automatic Classification System of Drainage Hole Blockage Based on Convolution Neural Network Transfer Learning

The blockage or failure of the drainage holes will endanger the stability of the slopes and traffic safety of a highway tunnel. This paper studies an algorithm for the automatic classification of drainage hole blockage degree based on convolutional neural network transfer learning to explore the intelligent detection method of drainage hole blockage. The model transfer method is adopted to input drainage hole image samples to retrain the pretrained network to classify new images. Experiments are performed on the collected samples of drainage hole images, and the accuracy of different network models is compared, ResNet‐18 being the best. The ResNet‐18 performance is compared using different transfer strategies and parameters. The results show that when the SGDM gradient optimisation algorithm is used and the learning rate is 0.0001, the identification effect of these samples is the best. The validation accuracy can reach 91.7%, test accuracy is 90.0%, and the effective classification of drainage hole blockage to different degrees is realised under the transfer learning strategy of ResNet‐18 model 1–34 frozen layers. Furthermore, with an expansion of the samples in the future, the identification accuracy will be further improved. The automatic classification system of the blockage degree of drainage hole greatly reduces the cost of manual detection, plays a guiding role in the maintenance of drainage pipes, and effectively improves the safety of highway tunnels and slopes.

Bottleneck detection for discrete manufacturing system based on object-oriented colored petri nets and cloud simulation

To identify the bottleneck unit of discrete manufacturing system dynamically, an intelligent detection method of bottleneck (IDMOB) is proposed by combining object-oriented colored Petri nets (OOCPN) and cloud simulation. With the proposed method, the production cells of manufacturing system are abstracted as several Petri net sub-modules. And the messages are passed through mapping functions and message passing gates among Petri net modules. Then, the cloud simulation is created to simulate the real processing, and the production data and capacity function are plugged into the cloud to detect the bottleneck unit intelligently. Finally, the effectiveness of IDMOB method is applied and validated in a practical case study from the power transformer industry.

Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm

In the process of production, automobile steel forgings are prone to various cracks, which affect the product quality. At present, forgings defects are mainly detected by fluorescent magnetic particle inspection and manual inspection. Aiming at the problems of low detection accuracy and efficiency in this method, an improved convolutional neural network model is proposed. The fluorescent magnetic particle inspection images of two typical forgings were intelligently inspected. Firstly, a deep learning model with EfficientNet as the backbone and Feature Pyramid Network (FPN) as the fusion layer is constructed. Secondly, In order to improve the convergence speed and detection accuracy, the calculation method of intersection over union is improved, and the network is improved by using the Attention Mechanism. Finally, Particle Swarm Optimization algorithm (PSO) with adaptive parameters is introduced to optimize the hyperparameters of neural network, and a fluorescent magnetic particle inspection image acquisition platform is built for verification. The mean Average Precision (mAP) of the best model of EfficientNet-PSO on the validation set is 95.69%. F1 score is 0.94 and FLOPs is 1.86B. Compared with other five deep learning neural network models, this method effectively improves the defect detection efficiency and accuracy of flange plate and cylinder head, which can meet the defect detection requirements.

A Lightweight CNN-Based Vision System for Concrete Crack Detection on a Low-Power Embedded Microcontroller Platform

The detection of cracks is a key aspect for assessing the condition of in-service structures such as road, bridges or dams. Therefore it assumes a great importance for civil infrastructures monitoring, road maintenance and traffic safety. Intelligent detection methods based on convolutional neural networks (CNNs) have been widely applied to the field of crack detection in recently years. In this work we propose a vision system based on two lightweight and accurate CNN models implemented in a low-cost, low-power platform, namely the OpenMV Cam H7 Plus, to monitor and to detect concrete cracks in real-time, suitable to realize a prototype of early warning system. In order to be useful, such a system must provide a very high accuracy, so as not to give false alarms, and be parsimonious enough on computational resources to be embedded into low-power, portable systems that can be deployed on the field. To reach this goal, firstly we analyze different state-of-the-art CNNs applied to the concrete crack detection task in order to discover the smallest network in terms of memory storage and number of parameters. Then, we compare the performance, in terms of memory occupancy and accuracy, of the proposed CNN architectures with the smallest network in the investigated literature, LeNet, all trained on two different image datasets, the Concrete Crack Images for Classification dataset and the SDNET2018 dataset, and implemented on the embedded system OpenMV Cam H7 Plus. The proposed CNN architectures perform nicely on this platform, using only a small fraction, between 6% to 26%, of the memory required by LeNet, and always providing better accuracy in all the tested cases and on both the datasets tried, with only a marginal increase in inference time.

An intelligent detection method of local feature points in computer vision image

An intelligent detection method of local feature points in computer vision image

Synthesis of pH-switchable Pt/Co3O4 nanoflowers: Catalytic mechanism, four-enzyme activity and smartphone biosensing applications

Pt/Co3O4 were synthesized by precipitation and reduction methods, exhibited unparalleled four enzyme activities: oxidase-like, peroxidase-like, catalase-like and superoxide dismutase activities by adjusting pH. The mechanism of enzyme activity was deeply explored, it was found that its oxidase activity originated from the oxygen vacancies of the material and O2−, 1O2, the peroxidase activity was attributed to the 1O2 and OH formed by nanomaterials catalyzing H2O2. Density functional theory calculations showed that Pt-Co with strong metal-oxide interaction interface was the active center, which needed a lower energy than others in the key step of catalyzing O2 to produce O2−. Based on its excellent oxidase activity, a muti-functional colorimetric detection platform for thiram, Cu2+ and ziram had been constructed successfully. The linear ranges of thiram was 0.6–250 µM and the limit of detection was 0.065 µM. The linear ranges of Cu2+ and ziram were 10–200 µM and 5–50 µM, the limit of detection were 4.066 µM and 3.36 µM. It should be noted that the portable smart phone platforms for thiram, Cu2+ and ziram had also been established with instant and quick detection. It was worth noting that in-depth exploration of the detection mechanism combined with in situ diffuse reflectance infrared fourier transform spectroscopy revealed that hydrogen bonds can be formed between thiram and oxidation substrate. And Cu2+ would inhibit the combination. Furthermore, it also showed excellent applications in detecting pesticide and Cu2+ in environmental soil and water samples. This study presented herein may provide an intelligent detection method to monitor environment.

An approach for brain tumor detection using optimal feature selection and optimized deep belief network

Nowadays, a Magnetic Resonance Image (MRI) scan acts as an efficient tool for efficiently detecting the abnormal tissues present in the brain. It is a complex process for radiologists to diagnose as well as classify the tumor from several images. This paper develops an intelligent method for the accurate detection of brain tumors. Initially, the pre-processing is performed for the input MRI image using the skull stripping and the entropy-based trilateral filtering methods. Further, fuzzy centroid-based region growing is adopted for segmenting the tumor from the image. Once the tumor is segmented, feature extraction is done using four sets of well-performing features like Gray-Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GLRM), statistical features, and shape features. The optimal feature selection is performed by the hybrid meta-heuristic algorithm termed Group Search-based Multi-Verse optimization (GS-MVO). Finally, the optimally selected features are given to a deep learning algorithm called Deep Belief Network (DBN). The weight is optimized by the same GS-MVO that classifies the final image as normal or abnormal. The simulation outcomes are performed by the standard benchmark database which proves that the developed technique obtains a high classification accuracy. From the analysis, the accuracy of the proposed GS-MVO-DBN is 9.09% superior to SVM, 7.14% superior to NN, 3.45% superior to DBN, 17.65% superior to CNN, 15.38% superior to NN-CNN, and 1.69% superior to COR-CSO-CNN-NN. The proposed GS-MVO-DBN is very effective in accurately detecting brain tumors. In the future, it is encouraged to work on challenging parts of the tumor region like edema, necrosis, and active regions with the help of the fusion process of multi-modality MRI images and effective pre-processing techniques incorporated with innovative deep learning methods.

Intelligent Detection Method for Roll Stability of Unmanned Vehicle based on Fuzzy Control

When the unmanned vehicle is disturbed by the outside world or carries out dangerous actions such as steering and continuous lane changing, the yaw stability of the unmanned vehicle decreases and the dangerous situation such as rollover is easy to occur. In this paper, the intelligent detection method for roll stability of unmanned vehicles based on fuzzy control is studied. The roll control system of the unmanned vehicle based on a double-layer control strategy is designed. The roll stability of the unmanned vehicle is controlled by an upper-layer fuzzy controller and lower-layer differential braking control. The dynamic model and tire model are built in MATLAB/Simulink to restore the running characteristics of unmanned vehicles. Based on the operation characteristics, the roll stability of the unmanned vehicle’s roll control system based on fuzzy control is tested from three aspects: steady-state response, roll stability and dynamic stability coefficient. The experimental results show that the transverse load’s transfer rate of the proposed method is reduced by more than 0.2% compared with the contrast method, the yaw angular velocity, centroid’s roll angle and roll angle measured under the two working conditions are closer to the actual values, which shows that the method has better control effect and detection accuracy.

Intelligent Defect Detection Method for Additive Manufactured Lattice Structures Based on a Modified YOLOv3 Model

Intelligent Defect Detection Method for Additive Manufactured Lattice Structures Based on a Modified YOLOv3 Model

Intelligent Detection Method of English Text in Natural Scenes in Video

With the rapid development of Internet technology, breakthroughs have been made in all branches of computer vision. Especially in image detection and target tracking, deep learning techniques such as convolutional neural networks have achieved excellent results. In order to explore the applicability of machine learning technology in the field of video text recognition and extraction, a YOLOv3 network based on multiscale feature transformation and migration fusion is proposed to improve the accuracy of english text detection in natural scenes in video. Firstly, aiming at the problem of multiscale target detection in video key frames, based on the YOLOv3 network, the scale conversion module of STDN algorithm is used to reduce the low-level feature map, and a backbone network with feature reuse is constructed to extract features. Then, the scale conversion module is used to enlarge the high-level feature map, and a feature pyramid network (FPN) is built to predict the target. Finally, the improved YOLOv3 network is verified to extract key text from images. The experimental results show that the improved YOLOv3 network can effectively improve the false detection and missed detection caused by occlusion and small target, and the accuracy of English text extraction is obviously improved.

INTELLIGENT HEALTH STATUS DETECTION METHOD FOR LOCOMOTIVE FUEL CELL BASED ON DATA-DRIVEN TECHNIQUES

Main drawbacks of fuel cell systems, namely, high cost, poor reliability, and short lifespan, limit the large-scale commercial application of fuel cell systems. The health status detection of fuel cell systems for locomotives is of great significance to the safe and stable operation of locomotives. To identify the failure modes of the fuel cell system accurately and quickly, this study proposed an intelligent health status detection method for locomotive fuel cells based on data-driven techniques. In this study, the actual test data of a 150-kW fuel cell system for locomotives was analyzed. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was combined with the general regression neural network (GRNN) to intelligently detect the health status of the fuel cell system for locomotives. Specifically, t-SNE was used to process the high-dimensionality and strong coupling raw data of health status, enabling the dimensional reduction of the raw data to reflect essential features. Then, GRNN was used to identify the feature data to achieve the fast and accurate detection of the health status of the fuel cell system. Results show that the proposed method can effectively detect four health conditions, namely, normal state, high inlet coolant temperature, low air pressure, and low spray pump pressure, with a diagnostic accuracy of 98.75%. This study is applicable to the analysis of the actual measurement data of high-power level fuel cell systems and provides a reference for the health status detection of fuel cell systems for locomotives. Keywords: fuel cell system for locomotive; data-driven; general regression neural network; t-distributed stochastic neighbor embedding; health status detection

A spatial-temporal correlation based method for advanced persistent threat detection

Advanced Persistent Threats (APT) have caused severe damage to the core information infrastructure of many governments and organizations. APT attacks usually remain low and slow which makes them difficult to be detected. In this case, the way of correlatively analyzing massive logs generated by various security devices for effectively detecting the new type of cyber threat turns out to be more and more significant. In this paper, on the basis of analyzing the principles and characteristics of APT, we propose an intelligent threat detection method based on the expanded Cyber Attack Chain (CAC) model and the long short-term memory network (LSTM) autoencoder to extensively correlate malicious behaviors from spatial and temporal dimensions, which provides a brain new idea for the application and practice of complex network attack detection.

Intelligent detection method of abnormal state of power equipment based on infrared and visible image information

Infrared thermal imaging cameras are usually used to detect the thermal state of the equipment. According to the heat radiation situation and temperature value emitted by the equipment, the thermal failure of the equipment is inferred, and whether the equipment is working is verified properly. This paper analyzes the thermal imaging principle of infrared technology and the characteristics of infrared images, and quantifies the surface temperature indicators of insulators. Through infrared and visible light image information, the pollution degree of insulators can be detected, and the abnormal condition of the equipment can be checked. The evaporation of moisture on the surface of the insulator mainly depends on the assumption that the surface heats up. A method for analyzing heat on the surface of wet-contaminated insulators is proposed, and the judgment conditions for arc generation in the drying zone and the drying zone and the heating models of the insulators in different operating conditions are established. The computer simulation results reveal the distribution of heat on the surface of insulators under different operating conditions, and the influence of the occurrence of drying zone and drying zone arc on leakage current and heating. The infrared thermal imaging test results of wet-contaminated insulators show that the model is reasonable and provides theoretical support for infrared thermal imaging detection of insulator contamination levels.

Stop-Phish: an intelligent phishing detection method using feature selection ensemble

Stop-Phish: an intelligent phishing detection method using feature selection ensemble

Intelligent Detection Methods of Electrical Connection Faults in RF Circuits

Printed circuit boards (PCBs) have a large number of electrical connection nodes. Exposure to harsh environments may lead to connection faults in these nodes. In the present work, intelligent detection methods for electrical connection faults were studied. Specifically, the fault characteristics of connectors, bonding wires and solder balls in the frequency domain were analyzed. The reflection and transmission parameters of an example filter circuit with electrical connection faults were calculated using the Simulation Program with Integrated Circuit Emphasis (SPICE). With these obtained electrical parameters, three machine learning algorithms were used to detect example electrical connection faults for the example circuit. Based upon the performance evaluations of the three algorithms, one can conclude that machine-learning-based intelligent fault detection is a promising technique in diagnosing circuit faults due to electrical connection issues with high accuracy and lower time cost as compared to current manual processes.

An intelligent behavior-based intrusion detection method for virtual machines

An intelligent behavior-based intrusion detection method for virtual machines