Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural networks.There have been no previous studies on the detection of real rifle numbers using real rifle image datasets.In this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient.The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods.In the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental environment.TensorFlow was used as the machine learning library.Experimental results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle numbers.The proposed method is effective in detecting rifle numbers.

Automatic Team Assignment and Jersey Number Recognition in Football Videos

Football is one of the most-watched sports,but analyzing players’per-formance is currently difficult and labor intensive.Performance analysis is done manually,which means that someone must watch video recordings and then log each player’s performance.This includes the number of passes and shots taken by each player,the location of the action,and whether or not the play had a successful outcome.Due to the time-consuming nature of manual analyses,interest in automatic analysis tools is high despite the many interdependent phases involved,such as pitch segmentation,player and ball detection,assigning players to their teams,identifying individual players,activity recognition,etc.This paper proposes a system for developing an automatic video analysis tool for sports.The proposed system is the first to integrate multiple phases,such as segmenting the field,detecting the players and the ball,assigning players to their teams,and iden-tifying players’jersey numbers.In team assignment,this research employed unsu-pervised learning based on convolutional autoencoders(CAEs)to learn discriminative latent representations and minimize the latent embedding distance between the players on the same team while simultaneously maximizing the dis-tance between those on opposing teams.This paper also created a highly accurate approach for the real-time detection of the ball.Furthermore,it also addressed the lack of jersey number datasets by creating a new dataset with more than 6,500 images for numbers ranging from 0 to 99.Since achieving a high perfor-mance in deep learning requires a large training set,and the collected dataset was not enough,this research utilized transfer learning(TL)to first pretrain the jersey number detection model on another large dataset and then fine-tune it on the target dataset to increase the accuracy.To test the proposed system,this paper presents a comprehensive evaluation of its individual stages as well as of the sys-tem as a whole.

Design of fuzzy number recognition based on embedded system platform

A system of number recognition with a graphic user interface （GUI） is implemented on the embedded development platform by using the fuzzy pattern recognition method. An application interface （API） of uC/ OS-Ⅱ is used to implement the features of multi-task concurrency and the communications among tasks. Handwriting function is implemented by the improvement of the interface provided by the platform. Fuzzy pattern recognition technology based on fuzzy theory is used to analyze the input of handwriting. A primary system for testing is implemented. It can receive and analyze user inputs from both keyboard and touch-screen. The experimental results show that the embedded fuzzy recognition system which uses the technology which integrates two ways of fuzzy recognition can retain a high recognition rate and reduce hardware requirements.

Parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on micro-Doppler features using CNN

This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-frequency spectrograms are acquired from the radar echo by the short-time Fourier transform.Secondly, based on the obtained spectrograms, a seven-layer CNN architecture is built to recognize the blade-number parity and classify the manoeuvre intention of the rotor target. The constructed architecture contains a leaky rectified linear unit and a dropout layer to accelerate the convergence of the architecture and avoid over-fitting. Finally, the spectrograms of the datasets are divided into three different ratios, i.e., 20%, 33% and 50%,and the cross validation is used to verify the effectiveness of the constructed CNN architecture. Simulation results show that, on the one hand, as the ratio of training data increases, the recognition accuracy of parity and manoeuvre intention is improved at the same signal-to-noise ratio(SNR);on the other hand, the proposed algorithm also has a strong robustness: the accuracy can still reach 90.72% with an SNR of – 6 dB.

Number Entities Recognition in Multiple Rounds of Dialogue Systems

As a representative technique in natural language processing(NLP),named entity recognition is used in many tasks,such as dialogue systems,machine translation and information extraction.In dialogue systems,there is a common case for named entity recognition,where a lot of entities are composed of numbers,and are segmented to be located in different places.For example,in multiple rounds of dialogue systems,a phone number is likely to be divided into several parts,because the phone number is usually long and is emphasized.In this paper,the entity consisting of numbers is named as number entity.The discontinuous positions of number entities result from many reasons.We find two reasons from real-world dialogue systems.The first reason is the repetitive confirmation of different components of a number entity,and the second reason is the interception of mood words.The extraction of number entities is quite useful in many tasks,such as user information completion and service requests correction.However,the existing entity extraction methods cannot extract entities consisting of discontinuous entity blocks.To address these problems,in this paper,we propose a comprehensive method for number entity recognition,which is capable of extracting number entities in multiple rounds of dialogues systems.We conduct extensive experiments on a real-world dataset,and the experimental results demonstrate the high performance of our method.

Optimization of Convolutional Neural Network for Recognition of Vehicle Frame Number

With the development of the economy and the surge in car ownership, the sale of used cars has been welcomed by more and more people, and the information of the vehicle condition is the focus information of them. The frame number is a unique number used in the vehicle, and by identifying it can quickly find out the vehicle models and manufacturers. The traditional character recognition method has the problem of complex feature extraction, and the convolutional neural network has unique advantages in processing two-dimensional images. This paper analyzed the key techniques of convolutional neural networks compared with traditional neural networks, and proposed improved methods for key technologies, thus increasing the recognition of characters and applying them to the recognition of frame number characters.

Study on Mimetic Peroxidase and Molecular Recognition of Phenols With Inclusion Complex of Ironporphyrin Immobilized by β-CD Polymer

β-Cyclodextrin (β-CD) and its cross-linked polymer (β-CDP) were known as the mimetic models. Metalloporphyrin had been widely used in the enzymatic method of analysis and molecular recognition. In present work, it was investigation that supramolecular recognition for halogenated phenols, three crosols, three nitrophenols and three aminophenols, served respectively as the substrate of the mimetic receptor, iron-5, 10, 15, 20-tetrakis (sulforphenyl)-21H, 23H-porphine (FeTPPS) or FeTPPS-β-CDP. Supramolecular complex, FeTPPS-β-CDP with function of mult i-recognition and induced-fit, was a advanced kind of mimetic peroxidase; Methyl phenol or polyphenol was the substitute of chlorophenic acid, while aminophenols and other phenols were suggested not to be utilized to enzymatic assay of H2O2. Being a mimetic enzyme mimicking the space structure of overall proteinase, beaimed by immobilized mimetic enzyme with a large number of β-CD interior cavities, chlorophenol was identified optimal substrate in the system tested.

Relationship between Extent of Coffee Intake and Recognition of Its Effects and Ingredients

This study examined the relationship between number of cups of coffee intake and recognition of the effects of coffee intake and its ingredients in young males and females. The subjects included 624 young people (ages 15 - 24;359 males, 265 females), who drank coffee habitually. They were classified into three groups on the basis of the number of cups of coffee consumed per day: “one cup,” “two cups,” and “over three cups.” In males, about 25% of the “over three cups” group expected “resolution of stress” from coffee, and this percentage was higher than that in the other groups. In females, about 18% of the same group had similar expectations;however, no significant group difference was found among the three groups. Few persons expected protective effects of diabetes mellitus and cancer in both genders (about 5% answer rate). About 20% of males and 18% of females in the “over three cups” group recognized the “laxative property” of coffee intake, and a significant group difference was found only in males. Even in the “one cup” group, over 77% knew that “caffeine” is an ingredient of coffee;however, few persons (under 15%) knew “poly-phenol,” which has protective effects of diabetes mellitus and cancer. In addition, no significant group difference was found in both genders. In conclusion, regardless of the coffee intake cup-number in both genders, recognition of the effects of coffee intake was low in both males and females and the recognition of effects differs by the intake cup-number in males. Only few persons knew the other ingredients in coffee apart from “caffeine.”

RELATIONSHIP OF TARGET RECOGNITION PERFORMANCE AND RADAR WAVEFORM PARAMETERS

Target recognition performance can be affected by radar waveform parameters.In this paper,we established rigorous relationship between target recognition efficiency and the parameters of a repeatedly transmitted waveform.It is based on Kullback-Leibler Information Number of single observation(KLINs),which measures the dissimilarity between targets depicted by a range-velocity double spread density function in frequency domain.We considered two signal models which are different in the coherence of the observations.The method we proposed takes advantage of the methodology of sequential hypothesis test,and then the recognition performance in terms of correct classification rate is expressed by Receiver Operating Characteristic(ROC).Simulation results about the parameters of LFM signal show the validity of the method.

Safer Design and Less Cost Operation for Low-Traffic Long-Road Illumination Using Control System Based on Pattern Recognition Technique

The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.

基于深度学习的雾霾天气下的车牌号码识别方法

针对雾霾天气下车牌识别存在的精确度低、漏检等问题,提出了一种基于深度学习的雾霾天气下的车牌号码识别方法。首先用AOD-Net算法对车辆图像进行去雾预处理。然后,基于YOLOv5网络设计一种车牌检测网络ACG_YOLOv5s。ACG_YOLOv5s是在YOLOv5s网络的基础上,融入CBAM注意力机制,提高模型的抗干扰能力;引入自适应特征融合网络ASFF,根据模型自适应学习到的权重赋予网络不同特征层不同比重的权值,从而突出重要特征信息;使用Ghost卷积模块替换传统卷积,在保证模型效果的同时减少了网络训练过程中的参数量。最后通过LPRNet对检测到的车牌图像进行识别。实验结果表明,改进后的ACG_YOLOv5s网络车牌检测准确率达到99.6%,LPRNet识别准确率达96%且内存占比小。实验证明AOD-Net算法和YOLO算法结合可更加有效地检测雾霾天气下车牌图像中的车牌号码。

基于改进DBNet和SVTR算法的连铸板坯号检测与识别

针对钢铁连铸产线板坯号识别字符区域小、光照变化复杂、板坯号图像质量差等问题,提出了一种基于深度学习的连铸板坯号检测与识别两阶段算法。首先,基于采集的连铸产线板坯图像,制备用于板坯号检测与识别的数据集;其次,在板坯号检测阶段,基于DBNet算法设计一种AD-PAN特征融合结构,以增强检测算法的多尺度特征融合能力和扩大感受野,提高板坯号定位精度;再次,在板坯号识别阶段,引入SPIN矫正网络和SVTR板坯号识别网络进行端到端训练,使其能够主动转换输入亮度,并改善字符间以及字符与背景间色彩失真的问题。最后,在自制的板坯号检测与识别数据集上进行了对比实验。实验结果表明,本研究提出的算法能够有效定位辊道上不同位置的板坯,并且在复杂背景下对板坯号进行鲁棒识别。其中,板坯号检测Hmean数值为97.92%,板坯号识别的准确率为97.33%,验证了本文所提算法具有较高的板坯号检测与识别精度。

基于机器视觉的手写钢板号图像增强及矫正算法研究与应用

钢板号的正确识别检查是实现生产线自动化生产的重要基础条件之一。近年来,许多生产线在备料位置配备了喷印机用于自动标记物料编号。喷印的字迹清晰且耐高温,在没有涂抹的情况下使用钢板号识别设备可以实现接近100%的识别率。然而,由于喷印设备故障或受限于资金和空间等原因,有时无法安装喷印设备,只能依赖人工手写的方式在钢板表面标记编号。与喷印编号相比,手写编号存在书写随意、连笔、字迹歪斜扭曲等复杂情况,这些因素限制了识别系统的准确性。鉴于识别效果较差,通常需要依赖人工目测来辅助识别,从而影响了物料跟踪自动化的实施效果。为了提升手写钢板号的识别效果,对传统机器学习光学字符识别(OCR)文本区域检测算法进行改进研究,并针对手写钢板号的特征,提出一种图像增强和扭曲矫正处理的算法。应用结果表明,该算法可以改善手写钢板号的图像质量和形状,提高识别的准确性。该研究旨在提升手写钢板号识别效果,以解决自动化生产中的难题。通过图像增强和矫正处理,使识别系统更好地处理手写钢板号,推动物料跟踪的自动化实施。

面向水面无人艇的船舶舷号识别方法

[目的]针对水面船舶舷号检测问题,提出一种面向水面无人艇的实时船舶舷号检测方法。[方法]基于原始的单阶段目标检测模型(YOLO),引入注意力机制,利用空间信息交互模块和分割注意力融合方法,提升神经网络对重要目标区域的敏感度。考虑先验知识对模型精度的影响,结合自适应锚框算法和正样本增强策略提高回归精度。针对深度神经网络(DNN)收敛困难的问题,改进损失函数,在保证网络收敛速度的同时提高神经网络训练的稳定性。最后,将改进的目标检查模型部署在无人艇上进行有效性验证。[结果]结果表明,所提算法在3级海情下能够准确识别船舶目标及其标志舷号,相比于原模型,改进后的YOLO算法在全类平均精度(mAP)方面提高了14%,识别速度满足实时要求。[结论]研究证明了所提舷号检测方法满足无人艇实时识别舷号任务的要求,并在复杂海洋环境中仍然具备识别能力。

OCR人脸识别技术在工号管理领域的应用探索

本文以断卡行动为契机,提出了员工实人认证申请工号,实名登陆使用工号的方法,有效地保障了生产作业系统存储的业务数据安全。该方法依赖OCR人脸识别实现对员工工号的实名管理,通过建设平台化的支撑能力弥补登录方式单一、使用不便带来的安全漏洞,解决采用工号挪用、复用、盗用等方式进行盗办业务、盗取系统信息而导致的业务数据安全问题。

面向多复杂场景环境的敞车车号辨识研究

针对现有敞车车号定位识别方法存在的环境适应性差、定位和识别精度低的问题,本文提出一种面向多种复杂环境下的敞车车号精准定位和识别的方法。搭建融合多尺度特征信息的敞车车号定位模型框架,在此基础上,融合多尺度金字塔特征进行深度可分离卷积的敞车车号特征提取网络设计。提出基于改进卷积循环神经网络的车号定位识别模型,主要针对识别网络模型结构进行设计。通过不同环境下采集的敞车车厢图片对本文提出的方法进行验证。结果表明:本文提出的车号定位方法的准确率为0.94,车号识别的准确率为0.97。

‘热研2号’柱花草对不同邻近植物的识别与生物响应差异

为明确‘热研2号’柱花草(Stylosanthes guianensis‘Reyan No.2’)对邻近植物的识别与生物响应对策,研究了其分别与自我、近亲、远亲和非亲4个亲缘关系组的10种植物伴生时的生长情况。结果表明:亲缘关系对‘热研2号’柱花草的株高和分枝数均值无显著影响,但近亲组伴生使其总生物量和地上生物量均值显著增加而根冠比均值显著减小(P<0.05),远亲和非亲组伴生使其根长、根冠比、生物量均值均显著降低而侧根数均值显著增加(P<0.05)。生长指标变化结合主成分分析表明:与‘热研21号’(S.guianensis‘Reyan No.21’)、‘热研24号’(S.guianensis‘Reyan No.24’)、‘热研5号’(S.guianensis‘Reyan No.5’)和鬼针草(Bidens pilosa)为邻株时,‘热研2号’柱花草的地上部生长显著增强,且程度依次减弱;与西卡柱花草(S.scabra)、细茎柱花草(S.gracilis)、蒺藜草(Cenchrus echinatus)、羽芒菊(Tridax procumbens)和大叶柱花草(S.grandifolia)为邻株时,其地上部和地下部生长均变差,且程度依次增强。说明‘热研2号’柱花草与近亲缘品种伴生时地上部生长相对较强,与远亲缘和非亲缘种伴生时地上、地下生长均减弱。

舰载全自动挂弹车定位系统设计与实现

论文以图像识别技术和飞机全自动挂弹任务为应用背景,设计了一型基于OpenMV图像识别功能的舰载全自动挂弹定位系统。以OpenMV图像识别平台作为挂弹小车的机器视觉平台,通过阈值编辑器标定目标图形的颜色阈值,提取目标图形的中心坐标、距离等信息,然后将一系列动作指令发送给Arduino Mega 2560,从而驱动各个执行机构实现挂弹平台前进、后退、原地旋转、倾转、升降、夹取等功能,并创新地使用了双向通信功能,使得OpenMV与Arduino Mega2560进行数据交换,确保挂弹平台能够准确、高效地将导弹挂装至飞机机翼下方挂弹架上,实现全自动挂弹功能,同时该系统还设计了基本的LED人机交互功能,能够让操作人员清晰直观地观察到系统运行状态。该系统在大型舰船飞机挂弹任务保障场景中有较大的应用价值。

基于光谱角度匹配的多光谱图像目标识别研究

使用地面推扫的多光谱成像设备,获取了三种典型情形下目标区域的多光谱图像。通过辐射校正、几何校正和光谱特征增强等预处理后,采用光谱角度匹配技术可成功将图像中与已知光谱角度一致的目标识别出来。结果表明,利用先验的光谱角度信息可以实现对多光谱图像中未知目标的分类、识别和提取。

基于红外热成像的钢包监测系统设计

冶金行业在生产过程中钢包一直运行在恶劣的环境中,对钢包内壁材料的健康程度进行及时检测对安全生产具有非常重要的意义。本文运用机器视觉技术对钢包号码进行识别、运用红外热成像仪对钢包外壁温度进行在线测量,并结合软件编程技术设计基于红外热成像的钢包监测系统,实现了钢包外壁温度的远程在线测量,并通过红外历史图片与历史数据曲线判断钢包内壁材料的缺损程度。本系统对钢铁行业的安全生产和智能制造水平具有一定的推动作用。