Application Research of an Intelligent Detection Algorithm for Vehicle Trajectory Route Deviation

In the vehicle trajectory application system, it is often necessary to detect whether the vehicle deviates from the specified route. Trajectory planning in the traditional route deviation detection is defined by the driver through the mobile phone navigation software, which plays a more auxiliary driving role. This paper presents a method of vehicle trajectory deviation detection. Firstly, the manager customizes the trajectory planning and then uses big data technologies to match the deviation between the trajectory planning and the vehicle trajectory. Finally, it achieves the supervisory function of the manager on the vehicle track route in real-time. The results show that this method could detect the vehicle trajectory deviation quickly and accurately, and has practical application value.

Research on Concrete Beam Crack Recognition Algorithm Based on Block Threshold Value Image Processing

To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven ill umination and complex surface color of concrete structure,this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system.The steps in this research are as follows:1.The crack digital images of concrete specimens with typical fea-tures were collected by using the Actis system of KURABO Co,Ltd,of Japan in the concrete beam bending test.2.The images are segmented into blocks to dis-tinguish backgrounds of different grayscale.3.The max imum interclass average gray difference method is used to distinguish the sub-blocks and screen out the image blocks that need to be segmented.4.Segmentation is made to the image with 2D max imum entropy threshold segmentation method to obtain the binary image,and the target image can be obtained by screening the connected domain features of the binary image.Results have shown that compared with other algo-rithms,the proposed method can effectively decrease the image over-segmentation and under segmentation rates,highlight the characteristics of the target cracks,solve the problems of excessive difference between the identified length and actual length of cracks caused by background gray level change and uneven ilumnination,and effectively improve the recognition accuracy of bridge concrete cracks.

Segmented second algorithm of empirical mode decomposition

A new algorithm, named segmented second empirical mode decomposition （EMD） algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency analysis. The original data is divided into some segments with the same length. Each segment data is processed based on the principle of the first-level EMD decomposition. The algorithm is compared with the traditional EMD and results show that it is more useful and effective for analyzing nonlinear and non-stationary signals.

Multifeature Statistical TV Tracker

A multifeature statistical image segmentation algorithm is described. Multiple features such as grey, edge magnitude and correlation are combined to form a multidimensional space statistics. The statistical algorithm is used to segment an image using the decision curved surface determined by the multidimensional feature function. The segmentation problem which is difficult to solve using the features independently will be readily solved using the same features jointly. An adaptive segmentation algorithm is discussed. Test results of the real-time TV tracker newly developed have shown that the segmentation algorithm discussed here improves effectively the image segmentation quality and system tracking performance.

A particle shape-based segmentation method to characterize spray dried materials by X-Ray microtomography

For accurate description of particle structure,single particle properties are required so that the properties of interest can be expressed as distributed parameters.X-Ray microtomography of the powder bed with subsequent particle separation can be used for this purpose.In this paper,a new algorithm for X-Ray microtomography images of spray dried particles was introduced since standard methods tend to fail if the particle size distribution is broad.The algorithm is based on 2D shape classification and subsequent 3D reconstitution of the particle using only a shape classifier as free parameter.The proposed algorithm was validated successfully.Using the algorithm,single particle porosities were obtained,which ranged from 0 to 70%.Prerequisites for the application of the algorithm are that a shape classifier can be set and that the 3D shape is regular.

AGWO-CNN Classification for Computer-Assisted Diagnosis of Brain Tumors

Brain cancer is the premier reason for cancer deaths all over the world.The diagnosis of brain cancer at an initial stage is mediocre,as the radiologist is ineffectual.Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful.Therefore,the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules.The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region.In the beginning stage,an anisotropic diffusion filters(ADF)method with un-sharp intensification masking is utilized to eliminate the noise discernment in images.In the following stage,within the improved nodule image sequence,a Superpixel Segmentation Based Iterative Clustering(SSBIC)algorithm is proposed for irregular brain tissue prediction.Subsequently,the brain nodule samples are captured using deep learning methods:Advanced Grey Wolf Optimization(AGWO)with ONN(AGWO-ONN)and Advanced GWO with CNN-based(AGWOCNN).The proposed technique indicates that the sensitivity is increased and the calculation time is decreased.Consequently,the proposed methodology manifests that the advanced Computer-Assisted Diagnosis(CAD)system has outstanding potential for automatic brain tumor diagnosis.The average segmentation time of the nodule slice order is 1.06s,and 97%of AGWO-ONN and 97.6%of AGWO-CNN achieve the best classification reliability.

Segmentation of Tumor Ultrasound Image via Region-Based Ncut Method

To segment the tumor region precisely is a prerequisite for ultrasound navigation and treatment. In this paper, a normalized cut method to segment tumor ultrasound image is proposed by means of simple linear iterative clustering for presegmentation procedure. The first step, we use simple linear iterative clustering algorithm to divide the image into a number of homogeneous over-segmented regions. Then, these regions are regarded as nodes, and a similarity matrix is constructed by comparing the histograms of each two regions. Finally, we apply the Ncut method to merging the over-segmented regions, then the image segmentation process is completed. The results show that the proposed segmentation scheme handles the strong speckle noise, low contrast, and weak edges well in ultrasound image. Our method has high segmentation precision and computation efficiency than the pixel-based Ncut method.

Design and Implementation of a High-Sensitivity Magnetic Sensing System Based on GMI Effect

A high-sensitivity magnetic sensing system based on giant magneto-impedance(GMI)effect is designed and fabricated.The system comprises a GMI sensor equipped with a gradient probe and an signal acquisition and processing module.A segmented superposition algorithm is used to increase target signal and reduce the random noise.The results show that under unshielded,room temperature conditions,the system achieves successful detection of weak magnetic fields down to 2 pT with a notable sensitivity of 1.84×108V/T(G=1000).By applying 17 overlays,the segmented superposition algorithm increases the power proportion of the target signal at 31 Hz from6.89%to 45.91%,surpassing the power proportion of the 2 Hz low-frequency interference signal.Simultaneously,it reduces the power proportion of the 20 Hz random noise.The segmented superposition process effectively cancels out certain random noise elements,leading to a reduction in their respective power proportions.This high-sensitivity magnetic sensing system features a simple structure,and is easy to operate,making it highly valuable for both practical applications and broader dissemination.

Analyzing capacity of a consumer-grade infrared camera in South Africa for costeffective aerial inspection of building envelopes

Prohibitive equipment cost and certain export regulations are the major obstacles to the widespread adoption of infrared(IR)thermography when evaluating building envelopes.In this work,we propose the use of an affordable and easily available camera as a first step of making the technology accessible.Combined with image post-processing,we hypothesize that a low-cost,low-resolution,and consumer-grade device can provide an economic alternative for the periodic evaluation of building envelopes.Following a market survey,the Seek Thermal Compact(STC)was chosen for evaluation.The STC was able to accurately measure the temperature of surfaces and distinguish small thermal anomalies(3 mm in diameter),and the IR images can be post-processed to reasonably estimate the anomaly areas.The STC was particularly effective when images were taken within 1.75 m from the surface.The 1.75 m distance did not pose a challenge in this study,as the goal was to mount the selected IR camera on an unmanned aerial vehicle for the surveys.The small size and weight of the STC were also useful.The results from the analysis of the capability of the STC and the image post-processing techniques may help form the basis of future investigations aiming at lowering the cost of building thermographic surveys.

Quantification of urban greenery using hemisphere-view panoramas with a green cover index

Urban greenery has positive impacts on the well-being of residents and provides vital ecosystem services.A quantitative evaluation of full-view green coverage at the human scale can guide green space planning and management.We developed a still camera to collect hemisphere-view panoramas(HVPs)to obtain in situ heterogeneous scenes and established a panoramic green cover index(PGCI)model to measure human-scale green coverage.A case study was conducted in Xicheng District,Beijing,to analyze the quantitative relationships of PGCI with the normalized difference vegetation index(NDVI)and land surface temperature(LST)in different land use scenarios.The results show that the HVP is a useful quantization tool:(1)the method adaptively distinguishes the green cover characteristics of the four functional areas,and the PGCI values are ranked as follows:recreational area(29.6)>residential area(19.0)>traffic area(15.9)>commercial area(12.5);(2)PGCI strongly explains NDVI and LST,and for each unit(1%)increase in PGCI,NDVI tends to increase by 0.007,and(3)LST tends to decrease by 0.21 degrees Celsius.This research provides government managers and urban planners with tools to evaluate green coverage in complex urban environments and assistance in optimizing human-scale greenery and microclimate.

Rail Detection Based on LSD and the Least Square Curve Fitting

It is necessary to rely on the rail gauge to determine whether the object beside the track will affect train operation safety or not.A convenient and fast method based on line segment detector(LSD)and the least square curve fitting to identify the rail in the image is proposed in this paper.The image in front of the train can be obtained through the camera on-board.After preprocessing,it will be divided equally along the longitudinal axis.Utilizing the characteristics of the LSD algorithm,the edges are approximated into multiple line segments.After screening the terminals of the line segments,it can generate the mathematical model of the rail in the image based on the least square.Experiments show that the algorithm in this paper can fit the rail curve accurately and has good applicability and robustness.

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.g.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.