Infrared Fault Detection Method for Dense Electrolytic Bath Polar Plate Based on YOLOv5s

Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal production.Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is proposed.Firstly,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample detection.On the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original network.The algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.

Vehicle detection based on information fusion of vehicle symmetrical contour and license plate position

An efficient vehicle detection approach is proposed for traffic surveillance images, which is based on information fusion of vehicle symmetrical contour and license plate position. The vertical symmetry axis of the vehicle contour in an image is. first detected, and then the vertical and the horizontal symmetry axes of the license plate are detected using the symmetry axis of the vehicle contour as a reference. The vehicle location in an image is determined using license plate symmetry axes and the vertical and the horizontal projection maps of the vehicle edge image. A dataset consisting of 450 images （15 classes of vehicles） is used to test the proposed method. The experimental results indicate that compared with the vehicle contour-based, the license plate location-based, the vehicle texture-based and the Gabor feature-based methods, the proposed method is the best with a detection accuracy of 90.7% and an elapsed time of 125 ms.

Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

Damage detection of a thin plate using modal curvature via macrostrain measurement

Plate structures are employed as important structural components in many engineering applications. Hence, assessing the structural conditions of in-service plate structures is critical to monitoring global structural health. Modal curvature-based damage detection techniques have recently garnered considerable attention from the research community, and have become a promising vibration-based structural health monitoring solution. However, computing errors arise when calculating modal curvatures from lateral mode shapes, which result from unavoidable measurement errors in the mode shapes as identified from lateral vibration signals; this makes curvature-based algorithms that use a lateral measurement only theoretically feasible, but practically infeasible. Therefore, in this study, long-gauge fiber Bragg grating strain sensors are employed to obtain a modal curvature without a numerical differentiation procedure in order to circumvent the computing errors. Several damage indices based on modal curvatures that were developed to locate beam damage are employed. Both numerical and experimental studies are performed to validate the proposed approach. However, although previous studies have reported relative success with the application of these damage indices on a simple beam, only one damage index demonstrated the capability to locate damage when the stiffness of the local region changed near the sensor.

An Efficient Method for Vehicle License Plate Detection in Complex Scenes

In this paper, we propose an efficient method for license plate localization in the images with various situations and complex background. At the first, in order to reduce problems such as low quality and low contrast in the vehicle images, image contrast is enhanced by the two different methods and the best for following is selected. At the second part, vertical edges of the enhanced image are extracted by sobel mask. Then the most of the noise and background edges are removed by an effective algorithm. The output of this stage is given to a morphological filtering to extract the candidate regions and finally we use several geometrical features such as area of the regions, aspect ratio and edge density to eliminate the non-plate regions and segment the plate from the input car image. This method is performed on some real images that have been captured at the different imaging conditions. The appropriate experimental results show that our proposed method is nearly independent to environmental conditions such as lightening, camera angles and camera distance from the automobile, and license plate rotation.

Nonlinear active control of damaged piezoelectric smart laminated plates and damage detection

Considering mass and stiffness of piezoelectric layers and damage effects of composite layers, nonlinear dynamic equations of damaged piezoelectric smart laminated plates are derived. The derivation is based on the Hamilton＇s principle, the higher- order shear deformation plate theory, von Karman type geometrically nonlinear straindisplacement relations, and the strain energy equivalence theory. A negative velocity feedback control algorithm coupling the direct and converse piezoelectric effects is used to realize the active control and damage detection with a closed control loop. Simply supported rectangular laminated plates with immovable edges are used in numerical computation. Influence of the piezoelectric layers＇ location on the vibration control is in- vestigated. In addition, effects of the degree and location of damage on the sensor output voltage are discussed. A method for damage detection is introduced.

Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone

Microscopy imaging of mouse growth plates is extensively used in biology to understand the effect of specific molecules on various stages of normal bone development and on bone disease. Until now, such image analysis has been conducted by manual detection. In fact, when existing automated detection techniques were applied, morphological variations across the growth plate and heterogeneity of image background color, including the faint presence of cells (chondrocytes) located deeper in tissue away from the image’s plane of focus, and lack of cell-specific features, interfered with identification of cells. We propose the first method of automated detection and morphometry applicable to images of cells in the growth plate of long bone. Through ad hoc sequential application of the Retinex method, anisotropic diffusion and thresholding, our new cell detection algorithm (CDA) addresses these challenges on bright-field microscopy images of mouse growth plates. Five parameters, chosen by the user in respect of image characteristics, regulate our CDA. Our results demonstrate effectiveness of the proposed numerical method relative to manual methods. Our CDA confirms previously established results regarding chondrocytes’ number, area, orientation, height and shape of normal growth plates. Our CDA also confirms differences previously found between the genetic mutated mouse Smad1/5CKO and its control mouse on fluorescence images. The CDA aims to aid biomedical research by increasing efficiency and consistency of data collection regarding arrangement and characteristics of chondrocytes. Our results suggest that automated extraction of data from microscopy imaging of growth plates can assist in unlocking information on normal and pathological development, key to the underlying biological mechanisms of bone growth.

Multi-Object Detection of Chinese License Plate in Complex Scenes

Multi-license plate detection in complex scenes is still a challenging task because of multiple vehicle license plates with different sizes and classes in the images having complex background.The edge features of high-density distribution and the high curvature features of stroke turning of Chinese character are important signs to distinguish Chinese license plate from other objects.To accurately detect multiple vehicle license plates with different sizes and classes in complex scenes,a multi-object detection of Chinese license plate method based on improved YOLOv3 network was proposed in this research.The improvements include replacing the residual block of the YOLOv3 backbone network with the Inception-ResNet-A block,imbedding the SPP block into the detection network,cutting the redundant Inception-ResNet-A block to suit for the multi-license plate detection task,and clustering the ground truth boxes of license plates to obtain a new set of anchor boxes.A Chinese vehicle license plate image dataset was built for training and testing the improved network,and the location and class of the license plates in each image were accurately labeled.The dataset has 62,153 pieces of images and 4 classes of China vehicle license plates,almost images have multiple license plates with different sizes.Experiments demonstrated that the multilicense plate detection method obtained 83.4%mAP,98.88%precision,98.17%recall,98.52 F1 score,89.196 BFLOPS and 22 FPS on the test dataset,and whole performance was better than the other five compared networks including YOLOv3,SSD,Faster-RCNN,EfficientDet and RetinaNet.

A New Algorithmic Approach for Detection and Identification of Vehicle Plate Numbers

This work proposes a method for the detection and identification of parked vehicles stationed. This technique composed many algorithms for the detection, localization, segmentation, extraction and recognition of number plates in images. It is acts of a technology of image processing used to identify the vehicles by their number plates. Knowing that we work on images whose level of gray is sampled with (120×180), resulting from a base of abundant data by PSA. We present two algorithms allowing the detection of the horizontal position of the vehicle: the classical method “horizontal gradients” and our approach “symmetrical method”. In fact, a car seen from the front presents a symmetry plan and by detecting its axis, that one finds its position in the image. A phase of localization is treated using the parameter MGD (Maximum Gradient Difference) which allows locating all the segments of text per horizontal scan. A specific technique of filtering, combining the method of symmetry and the localization by the MGD allows eliminating the blocks which don’t pass by the axis of symmetry and thus find the good block containing the number plate. Once we locate the plate, we use four algorithms that must be realized in order to allow our system to identify a license plate. The first algorithm is adjusting the intensity and the contrast of the image. The second algorithm is segmenting the characters on the plate using profile method. Then extracting and resizing the characters and finally recognizing them by means of optical character recogni-tion OCR. The efficiency of these algorithms is shown using a database of 350 images for the tests. We find a rate of lo-calization of 99.6% on a basis of 350 images with a rate of false alarms (wrong block text) of 0.88% by image.

THE PARALLEL CONFOCAL DETECTING SYSTEM USING OPTICAL FIBER PLATE

Objective Focusing on the problem such as slow scanning speed, complex system design and low light efficiency, a new parallel confocal 3D profile detecting method based on optical fiber technology, which realizes whole-field confocal detecting, is proposed. Methods The optical fiber plate generates an 2D point light source array, which splits one light beam into N2 subbeams and act the role of pinholes as point source and point detecting to filter the stray light and reflect light. By introducing the construction and working principle of the multi-beam 3D detecting system, the feasibility is investigated. Results Experiment result indicates that the optical fiber technology is applicable in parallel confocal detecting. Conclusion The equipment needn't mechanical rotation. The measuring parameters that influence the detecting can easily be adapted to satisfy different requirments of measurement. Compared with the conventional confocal method, the parallel confocal detecting system using optical fiber plate is simple in the mechanism, the measuring field is larger and the speed is faster.

雾霾环境下基于PLATE-YOLO的车牌检测方法

针对目前车牌识别领域中,雾霾环境下车牌检测准确率低的问题,本文提出一种基于深度学习的抗雾霾车牌检测方法,该方法能够检测民用车牌和机场民航车辆车牌。该方法首先利用一种基于卷积神经网络的去雾算法对车牌图片进行去雾预处理,然后将处理过的无雾霾图片送入PLATE-YOLO网络中检测车牌的位置。该PLATE-YOLO网络是本文针对车牌检测的特点,对YOLOv3网络做了修改后得到的适用于车牌检测的网络。主要改进点有两处:第一,提出了一种基于层次聚类算法的锚盒(Anchor Box)个数和初始簇中心的计算方法;第二,针对车牌目标较大的特点,对网络的多尺度特征融合做了优化。优化后的PLATE-YOLO网络更适合于车牌检测,且提高了检测速度。实验证明,PLATE-YOLO网络检测车牌的速度较YOLOv3提高了5 FPS;在雾霾环境下,经去雾预处理的PLATE-YOLO车牌检测方法比未经去雾处理的车牌检测方法准确率提高了9.2%。

瞬变电磁探测技术在小纪汗煤矿的应用与研究

煤炭作为我国国民经济的重要支柱,其深部开采面临复杂多变的地层特征,包括高地应力、构造应力变化及梯度差异,导致井下环境充满危险因素,水害尤为突出。瞬变电磁探测技术在近20年来取得显著进步,成为煤矿防治水工作的关键手段。本研究采用矿井瞬变电磁法,针对小纪汗煤矿特定煤层区域,详细探查了煤层顶底板含水情况。通过对瞬变电磁视电阻率数据的综合分析,我们成功绘制了等值线断面图和平面图,进而进行了地质推断,明确了煤层与上下含水层的分布情况。研究结果显示,煤矿充水强度较大,主要水源为顶板砂岩裂隙承压水。瞬变电磁法的应用,不仅精确界定了顶底板承压水聚集区,还为矿井开采与疏放水工程的科学布置提供了坚实依据,展现了该技术在实际应用中的高效与准确性。

纸片法与平板计数法在托幼机构环境菌落总数检测中的应用分析

目的分析纸片法与平板计数法检测托幼机构环境菌落总数的应用效果。方法随机选取新乡市50所托育机构作为研究对象,对机构中的工作人员皮肤、物品及室内空气进行纳入采样,分别为手部皮肤(n=100)、物品表面(n=100)、室内空气(n=50),随后对以上样本进行纸片法、平板计数法检测,分析其在检测结果、培养时间、室内空气采样时间方面的差异。结果两种检测方案在手部皮肤、物品表面的检测结果无显著性差异(P>0.05),但在室内空气评估检测时,纸片法检测的结果显著高于平板计数法的,差异有统计学意义(P<0.05);在菌落方面,平板计数法培养结果(工作人员手部皮肤、物品表面、室内空气)1 d与2 d之间,存在显著差异,且培养2 d后的结果显著高于1 d的结果,差异有统计学意义(P<0.05)。但是在纸片法培养结果中,1 d与2 d的结果情况基本一致,差异无统计学意义(P>0.05);不同检测方案室内空气采样时间方面,培养1 d的结果中,纸片法在3 min、5 min、10 min、15 min采样的结果均高于采样5 min的平板计数法,差异有统计学意义(P<0.05);但在培养2 d的结果中,纸片法仅在3 min、5 min采样结果高于采样5 min的平板计数法(P<0.05),一旦采样时间在10 min、15 min时,两者间无显著差异性(P>0.05)。结论纸片法进行托幼机构环境菌落总数检测的价值优于平板计数法,而且更加快捷、便利,具有推广应用意义。

License Plate Recognition via Attention Mechanism

License plate recognition technology use widely in intelligent trafficmanagement and control. Researchers have been committed to improving thespeed and accuracy of license plate recognition for nearly 30 years. This paperis the first to propose combining the attention mechanism with YOLO-v5and LPRnet to construct a new license plate recognition model (LPR-CBAMNet).Through the attention mechanism CBAM(Convolutional Block AttentionModule), the importance of different feature channels in license platerecognition can be re-calibrated to obtain proper attention to features. Forceinformation to achieve the purpose of improving recognition speed andaccuracy. Experimental results show that the model construction methodis superior in speed and accuracy to traditional license plate recognitionalgorithms. The accuracy of the recognition model of the CBAM model isincreased by two percentage points to 97.2%, and the size of the constructedmodel is only 1.8 M, which can meet the requirements of real-time executionof embedded low-power devices. The codes for training and evaluating LPRCBAM-Net are available under the open-source MIT License at: https://github.com/To2rk/LPR-CBAM-Net.

Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%.

基于YOLOv8-OCR的井下人员检测算法

为提高井下昏暗环境的目标检测性能,将行人属性应用到井下,将反光号码牌贴在安全帽和工作服上作为属性进行识别。针对井下小目标检测率低的缺点,提出了一种将YOLOv8检测算法与光学字符识别(Optical Character Recognation, OCR)技术相结合的方法。通过添加卷积块注意力模块(Convolutional Block Attention Module, CBAM),提高了网络对不同尺度的特征提取能力;将YOLOv8中的CBS模块改进为CBF模块,提高了目标的识别准确率;对检测到的反光号码牌区域用OCR技术对区域内数字进行识别,进一步提高了模型的检测精度。实验结果表明,提出的方法在自建数据集上获得了93.2%的识别准确率和每张24.4 ms的检测速度,相比YOLOv8模型有着更高的准确率,能够有效地应对井下环境中的光照变化和干扰因素并且满足实时检测的要求。

基于视觉传感的薄板对接焊缝检测方法研究

为了实现薄板自动对接焊过程中焊缝中心的准确检测,提出了基于主动视觉传感的薄板对接焊缝检测方法。通过对工业相机采集的图像进行颜色分割与亮度分割,消除环境光的干扰;利用轮廓检测与掩模操作消除图像噪声,应用灰度重心法提取激光条纹的中心线,并通过一阶差分方法实现焊缝左右边界及中心的检测。针对激光条纹存在的分断缺陷进行续断连接,以提高焊缝中心检测的准确性。实验结果表明,采用所提方法能够准确检测焊缝边界及中心位置,能够满足薄板对接焊缝的自动焊接需求。

基于顶点与主体区域同步检测的精准车牌定位

为应对非约束环境下的车牌精定位问题,提出一种基于顶点局部区域与主体区域同步检测策略的非约束性车牌定位算法。通过删减YOLOv5网络的输出结构,训练得到可同步检测车牌及顶点区域的车牌检测网络,在兼顾精度与计算速度的前提下,实现车牌顶点和主体区域的同步定位。针对一幅图中存在多个车牌区域及顶点区域存在少量漏检和误检的情况,分别设计了车牌顶点归类和单一缺失顶点预测后处理算法,借助顶点间的空间位置关系进行漏检目标预测和误检目标排查,有效改善了因场景复杂导致的个别顶点目标检测效果差的问题。所提算法在中国城市停车场数据集(CCPD)上的测试结果显示,平均精准率达99.25%,平均召回率达98.70%。所提算法不仅能够准确预测出车牌的4个顶点坐标,而且在中端GPU硬件平台上处理速度可达121帧/s,具有较好的应用价值。

基于超声信号的纵横复合式陶瓷膜板内部缺陷检测系统研究

针对陶瓷膜板内部缺陷检测的全覆盖扫查问题,设计了一种基于超声信号的纵横复合式陶瓷膜板内部缺陷检测系统.该系统主要由超声采集模块和超声检测模块两部分组成.超声采集模块通过控制器、驱动器和滚珠丝杠之间相互配合,控制超声探伤仪进行纵横复合运动,实现对陶瓷膜板的全覆盖扫查;超声检测模块通过计算机和可视化软件界面,实现对超声采集模块的控制,同时能够对超声信号进行分析和处理.最后,通过在陶瓷膜板试件上进行测试,结果表明:该系统通过基于超声信号的纵横复合式扫查,实现了对陶瓷膜板内部缺陷的全覆盖扫查,相较于传统人工扫查的方式,缺陷覆盖率提高了14.93%,效率提高了41.8%,保证了陶瓷膜板内部缺陷数据的完整性及效率,具有良好的工程应用价值.