Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

Analysis and Design of Surgical Instrument Localization Algorithm

With the help of surgical navigation system,doctors can operate on patients more intuitively and accurately.The positioning accuracy and real-time performance of surgical instruments are very important to the whole system.In this paper,we analyze and design the detection algorithm of surgical instrument location mark,and estimate the posture of surgical instrument.In addition,we optimized the pose by remapping.Finally,the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments.The final result shows a high accuracy.

Identification of thermal front dynamics in the northern Malacca Strait using ROMS 3D-model

The thermal front in the oceanic system is believed to have a significant effect on biological activity.During an era of climate change,changes in heat regulation between the atmosphere and oceanic interior can alter the characteristics of this important feature.Using the simulation results of the 3D Regional Ocean Modelling System(ROMS),we identified the location of thermal fronts and determined their dynamic variability in the area between the southern Andaman Sea and northern Malacca Strait.The Single Image Edge Detection(SIED)algorithm was used to detect the thermal front from model-derived temperature.Results show that a thermal front occurred every year from 2002 to 2012 with the temperature gradient at the location of the front was 0.3°C/km.Compared to the years affected by El Ni?o and negative Indian Ocean Dipole(IOD),the normal years(e.g.,May 2003)show the presence of the thermal front at every selected depth(10,25,50,and 75 m),whereas El Ni?o and negative IOD during 2010 show the presence of the thermal front only at depth of 75 m due to greater warming,leading to the thermocline deepening and enhanced stratification.During May 2003,the thermal front was separated by cooler SST in the southern Andaman Sea and warmer SST in the northern Malacca Strait.The higher SST in the northern Malacca Strait was believed due to the besieged Malacca Strait,which trapped the heat and make it difficult to release while higher chlorophyll a in Malacca Strait is due to the freshwater conduit from nearby rivers(Klang,Langat,Perak,and Selangor).Furthermore,compared to the southern Andaman Sea,the chlorophyll a in the northern Malacca Strait is easier to reach the surface area due to the shallower thermocline,which allows nutrients in the area to reach the surface faster.

Targets detecting in the ocean using the cross-polarized channels of fully polarimetric SAR data

Azimuth ambiguities （ghost targets） discrimination is of great interest with the development of a synthet- ic aperture radar （SAR）. And the azimuth ambiguities are often mistaken as actual targets and cause false alarms. For actual targets, HV channel signals acquired by a fully polarimetric SAR are approximately equal to a VH channel in magnitude and phase, i.e., the reciprocity theorem applies, but shifted in phase about ±π for the first-order azimuth ambiguities. Exploiting this physical behavior, the real part of the product of the two cross-polarized channels, i.e. （SHVSVH）, hereafter called A12r, is employed as a new parameter for a target detection at sea. Compared with other parameters, the contrast of A12r image between a target and the surrounding sea surface will be obviously increased when A12r image is processed by mean filtering algo- rithm. Here, in order to detect target with constant false-alarm rates （CFARs）, an analytical expression for the probability density function （pdf） ofA12r is derived based on the complexWishart-distribution. Because a value of A12r is greater/less than 0 for real target/its azimuth ambiguities, the first-order azimuth ambiguities can be completely removed by this A12r-based CFAR technology. Experiments accomplished over C-band RADARSAT-2 fully polarimetric imageries confirm the validity.

A fast detection algorithm for ceramic ball surface defects based on fringe reflection

A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm.

Multi-sensor Hybrid Fusion Algorithm Based on Adaptive Square-root Cubature Kalman Filter

In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.

Detecting Adjacent Relativity of Engineering Drawing Entities with Container Window

Automatic recognition and interpretation of engineering drawing plays an important role in computer aided engineering. Detecting the positional relation between entities is an important topic in this research field. In this paper the concepts of adjacent relativity and container window of drawing entities were proposed. By means of container window, the adjacent irrelative entities can be detected quickly and effectively, which speeds up the process of adjacent relativity detection. Meanwhile, the algorithm of adjacent relativity detection was discussed.

A Parameter-Detection Algorithm for Moving Ships

In traffic-monitoring systems, numerous vision-based approaches have been used to detect vehicle parameters. However, few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object. In this paper, we present an approach to detecting the parameters of a moving ship in an inland river. This approach involves interactive calibration without a calibration reference. We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios, and we detect ship length, width, speed, and flow. We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90% for all parameters.

Delivery Invoice Information Classification System for Joint Courier Logistics Infrastructure

With the growth of the online market,demand for logistics and courier cargo is increasing rapidly.Accordingly,in the case of urban areas,road congestion and environmental problems due to cargo vehicles are mainly occurring.The joint courier logistics system,a plan to solve this problem,aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery companies.However,several courier companies use different types of courier invoices.Such a system has a problem of information data transmission interruption.Therefore,the data processing process was systematically analyzed,a practically feasible methodology was devised,and delivery invoice information processing standards were established for this.In addition,the importance of this paper can be emphasized in terms of data processing in the logistics sector,which is expected to grow rapidly in the future.The results of this study can be used as basic data for the implementation of the logistics joint delivery terminal system in the future.And it can be used as a basis for securing the operational reliability of the joint courier logistics system.

Research and Application of Log Defect Detection and Visualization System Based on Dry Coupling Ultrasonic Method

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.

Combining TDLAS and multi-fusion algorithms for methane gas concentration detection

High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration information is susceptible to noise, which leads to its useful signal being drowned by noise. A fusion algorithm of variational modal decomposition(VMD) and improved wavelet threshold filtering is proposed, which is used in combination with tunable diode laser absorption spectroscopy(TDLAS) to implement a non-contact, high-resolution methane gas concentration detection. The fusion algorithm can perform noise reduction and further segmentation of the methane gas detection signal. And the simulation and experiment verify the effectiveness of the fusion algorithm, and the experimental results show that for the detection of air containing 10 ppm, 30 ppm, 60 ppm, 80 ppm, and 99 ppm methane, the errors are 12.75%, 8.18%, 3.37%, 2.46%, and 1.78%, respectively.

Fast Digital Correlation Calculation Method in Extended Beacon Wavefront Detection

On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both theoretical interpreting and computer simulation, explains how to measure the wavefront slope difference between two sub apertures through the determination of image displacements on detector plane. It includes a fast and accurate digital algorithm for detecting wavefront disturbance, which is much suitable for realization in such electrical hardwares as digital signal processors.

AIRS Cloud Detection Scheme Based on FOV

6 hours scanning of view field was conducted by Grapes-3DVar detection system.The process is based on cloud detection,combining Grapes-3DVar system with AIRS instrument characteristics to eliminate field of view contaminated by cloud,which lays a solid foundation for application of AIRS data in 3-dimensional variational assimilation system.

A fault injection model-oriented testing strategy for component security

A fault injection model-oriented testing strategy was proposed for detecting component vulnerabilities.A fault injection model was defined,and the faults were injected into the tested component based on the fault injection model to trigger security exceptions.The testing process could be recorded by the monitoring mechanism of the strategy,and the monitoring information was written into the security log.The component vulnerabilities could be detected by the detecting algorithm through analyzing the security log.Lastly,some experiments were done in an integration testing platform to verify the applicability of the strategy.The experimental results show that the strategy is effective and operable.The detecting rate is more than 90%for vulnerability components.

Characteristics of cyclone climatology and variability in the Southern Ocean

A new climatology of cyclones in the Southern Ocean is generated by applying an automated cyclone detection and tracking algorithm （developed by Hodges at the Reading University） for an improved and relatively high- resolution European Centre for Medium-Range Weather Forecasts atmospheric reanalysis during 1979-2013. A validation shows that identified cyclone tracks are in good agreement with a available analyzed cyclone product. The climatological characteristics of the Southern Ocean cyclones are then analyzed, including track, number, density, intensity, deepening rate and explosive events. An analysis shows that the number of cyclones in the Southern Ocean has increased for 1979-2013, but only statistically significant in summer. Coincident with the circumpolar trough, a single high-density band of cyclones is observed in 55^-67~S, and cyclone density has generally increased in north of this band for 1979-2013, except summer. The intensity of up to 70% cyclones in the Southern Ocean is less than 980 hPa, and only a few cyclones with pressure less than 920 hPa are detected for 1979-2013. Further analysis shows that a high frequency of explosive cyclones is located in the band of 45^-55~S, and the Atlantic Ocean sector has much higher frequent occurrence of the explosive cyclones than that in the Pacific Ocean sector. Additionally, the relationship between cyclone activities in the Southern Ocean and the Southern Annular Mode is discussed.

Automatic Change Detection for Road Networks from Images Based on GIS

Up to now, detailedstrategies and algorithms of automaticchange detection for road networksbased on GIS have not been discussed.This paper discusses two differentstrategies of automatic change detec-tion for images with low resolution andhigh resolution using old GIS data,and presents a buffer detection andtracing algorithm for detecting roadfrom low-resolution images and a newprofile tracing algorithm for detectingroad from high-resolution images. Forfeature-level change detection (FL-CD), a so-called buffer detection algo-rithm is proposed to detect changes offeatures. Some ideas and algorithms ofusing GIS prior information and somecontext information such as substructures of road in high-resolution imagesto assist road detection and extractionare described in detail.

Piecewise linear detection for direct superposition modulation

Considering high-order digital modulation schemes, the bottleneck in consumer products is the detector rather than the modulator. The complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of modulated bits per data symbol. Thus, it is necessary to develop low-complexity detection algorithms with an APP-like performance, especially when performing iterative detection, for example in conjunction with bit interleaved coded modulation. We show that a special case of superposition modulation, dubbed Direct Superposition Modulation （DSM）, is particularly suitable for complexity reduction at the receiver side. As opposed to square QAM, DSM achieves capacity without active signal shaping. The main contribution is a low-cost detection algorithm for DSM, which enables iterative detection by taking a priori information into account. This algorithm exploits the approximate piecewise linear behavior of the soft outputs of an APP detector over the entire range of detector input values. A theoretical analysis and simulation results demonstrate that at least max-log APP performance can be reached, while the complexity is significantly reduced compared to classical APP detection.

Seasonal and inter-annual variations of Arctic cyclones and their linkage with Arctic sea ice and atmospheric teleconnections

The seasonal and inter-annual variations of Arctic cyclone are investigated. An automatic cyclone tracking algorithm developed by University of Reading was applied on the basis of European Center for Medium-range Weather Forecasts（ECMWF） ERA-interim mean sea level pressure field with 6 h interval for 34 a period. The maximum number of the Arctic cyclones is counted in winter, and the minimum is in spring not in summer.About 50% of Arctic cyclones in summer generated from south of 70°N, moving into the Arctic. The number of Arctic cyclones has large inter-annual and seasonal variabilities, but no significant linear trend is detected for the period 1979–2012. The spatial distribution and linear trends of the Arctic cyclones track density show that the cyclone activity extent is the widest in summer with significant increasing trend in CRU（central Russia）subregion, and the largest track density is in winter with decreasing trend in the same subregion. The linear regressions between the cyclone track density and large-scale indices for the same period and pre-period sea ice area indices show that Arctic cyclone activities are closely linked to large-scale atmospheric circulations, such as Arctic Oscillation（AO）, North Atlantic Oscillation（NAO） and Pacific-North American Pattern（PNA）. Moreover,the pre-period sea ice area is significantly associated with the cyclone activities in some regions.

An efficient approach for shadow detection based on Gaussian mixture model

An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate(the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step.

Development of 2D computer program to determine geometry of rock mass blocks

Due to various geological processes such as tectonic activities fractures might be created in rock mass body which causes creation of blocks with different shapes and sizes in the rock body. Exact understand- ing of these blocks geometry is an essential issue concerned in different domains of rock engineering such as support system of underground spaces built in jointed rock masses, design of blasting pattern, optimi- zation of fragmentation, determination of cube blocks in quarry mines, blocks stability, etc. The aim of this paper is to develop a computer program to determine geometry of rock mass blocks in two dimen- sional spaces. In this article, the eometrv of iointed rock mass is programmed in MATLABTM.