A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.

Management of Penetrating Cranioencephalic Trauma Caused by Sharp Metal Objects—Therapeutic and Evolutionary Aspects: 12 Cases at the Renaissance University Hospital in N’Djamena

Introduction: Cranioencephalic trauma caused by bladed weapons is rare, and that caused by sharp objects is exceptional. The aim of our study was to describe the clinical, therapeutic and evolutionary aspects. Materials and method: This was a descriptive and analytical study over a 48-month period at CHU la Renaissance from January 1, 2018 to December 31, 2021, concerning patients admitted for penetrating cranioencephalic trauma by pointed object. Results: Twelve cases, all male, of penetrating cranioencephalic sharp-force trauma were identified. The mean age was 34 ± 7 years, with extremes of 11 and 60 years. Farmers and herders accounted for 31% and 25% of cases respectively. The average admission time was 47 hours. Brawls were the circumstances of occurrence in 81.2% of cases. Knives (33%), arrows (25%) and iron bars (16.6%) were the objects used. Altered consciousness was present in 43.8% of cases, and focal deficit in 50%. Scannographic lesions were fracture and/or embarrhment (12 cases), intra-parenchymal haematomas (6 cases) and presence of object in place (4 cases). Surgery was performed in 11 patients. Postoperative outcome was favorable in 9 patients. After 12 months, 2 patients were declared unfit. Conclusion: Penetrating head injuries caused by sharp objects are common in Chad. Urgent surgery can prevent disabling after-effects.

Patterns of Interactions of the Complex City System:Emotional Urban Objects as Triggering Agents-A Secondary Publication

This article presents an analysis of the patterns of interactions resulting from the positive and negative emotional events that occur in cities,considering them as complex systems.It explores,from the imaginaries,how certain urban objects can act as emotional agents and how these events affect the urban system as a whole.An adaptive complex systems perspective is used to analyze these patterns.The results show patterns in the processes and dynamics that occur in cities based on the objects that affect the emotions of the people who live there.These patterns depend on the characteristics of the emotional charge of urban objects,but they can be generalized in the following process:(1)immediate reaction by some individuals;(2)emotions are generated at the individual level which begins to generalize,permuting to a collective emotion;(3)a process of reflection is detonated in some individuals from the reading of collective emotions;(4)integration/significance in the community both at the individual and collective level,on the concepts,roles and/or functions that give rise to the process in the system.Therefore,it is clear that emotions play a significant role in the development of cities and these aspects should be considered in the design strategies of all kinds of projects for the city.Future extensions of this work could include a deeper analysis of specific emotional events in urban environments,as well as possible implications for urban policy and decision making.

Numerical Investigations on Harbor Oscillations Induced by Falling Objects

In this paper,the open-sourced computational fluid dynamics software,OpenFOAM~?,is used to study the fluctuation phenomenon of the water body inside a horizontally one-dimensional enclosed harbor basin with constant water depth triggered by falling wedges with various horizontal falling positions,initial falling velocities and masses.Based on both Fourier transfo rm analysis and wavelet spectrum analysis for the time series of the free surface elevations inside the harbor basin,it is found for the first time that the wedge falling inside the harbor can directly trigger harbor resonance.The influences of the three factors(including the horizontal falling position,the initial falling velocity,and the mass)on the response amplitudes of the lowest three resonant modes are also investigated.The results show that when the wedge falls on one of the nodal points of a resonant mode,the mode would be remarkably suppressed.Conversely,when the wedge falls on one of the anti-nodal points of a resonant mode,the mode would be evidently triggered.The initial falling velocity of the wedge mainly has a remarkable effect on the response amplitude of the most significant mode,and the latter shows a gradual increase trend with the increase of the former.While for the other two less significant modes,their response amplitudes fluctuate around certain constant values as the initial falling velocity rises.In general,the response amplitudes of all the lowest three modes are shown to gradually increase with the mass of the wedge.

时间差计算下的大型超临界发电机组转子绕组RSO检测方法

为提高大型超临界发电机组转子组状态检测效果,设计时间差计算下的大型超临界发电机组转子组循环周期性电脉冲(RSO)检测方法,即基于RSO检测法原理提取时间差脉冲信号特征,根据信号时间差特征构建线圈短路检测模型,利用小波变换方法进行时间差信号去噪处理,并固定转子位置与绕组参数,确定转子组故障位置,从而实现大型超临界发电机组转子组RSO检测。试验结果表明,该方法对电机初始转子位置、启动时转子位置、断条正常、断条故障时功率谱幅值的检测结果与实际值均相差较小,验证了所提方法的有效性。

基于FME Objects的空间数据格式转换方法研究

地理信息数据广泛应用于大测绘、自然资源、建设规划等行业领域的信息分析与存储。因其数据叠加分析与跨专业融合学习的需要,满足数据的定向需求与统一性。本文基于FME Objects的引用库,运用C#语言研究了如何精确、完整实现数据格式转换的程序方法,探寻了含非图形属性文件的转换函数表达方式,解析了程序实现的基础结构。经过诸多数据的转换验证,此系统能够提高数据格式的转换效率且能保证图形要素与属性的完整性,并可生成全面的转换日志与简易的质检文本,为各行业地理信息数据的统一整理提供便利。

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

The amount of needed control messages in wireless sensor networks(WSN)is affected by the storage strategy of detected events.Because broadcasting superfluous control messages consumes excess energy,the network lifespan can be extended if the quantity of control messages is decreased.In this study,an optimized storage technique having low control overhead for tracking the objects in WSN is introduced.The basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List(OBLL).Whenever an object passes over the sensor area,the recognizing sensor can immediately produce an OBLL along the object’s route.To retrieve the entire information,the OBLL can be traversed with logarithmic complexity which is much less than the traversing complexity of existing linked list structures.Performance evaluation and simulations were carried out to ensure that the suggested technique minimizes the number of messages and thus saving energy and extending the network life.

Application of 3D Scanned Big Data of Large-scale Cultural Heritage Objects Based on Noise-robust Transparent Visualization

Three-dimensional(3D) scanning technology has undergone remarkable developments in recent years.Data acquired by 3D scanning have the form of 3D point clouds.The 3D scanned point clouds have data sizes that can be considered big data.They also contain measurement noise inherent in measurement data.These properties of 3D scanned point clouds make many traditional CG/visualization techniques difficult.This paper reviewed our recent achievements in developing varieties of high-quality visualizations suitable for the visual analysis of 3D scanned point clouds.We demonstrated the effectiveness of the method by applying the visualizations to various cultural heritage objects.The main visualization targets used in this paper are the floats in the Gion Festival in Kyoto(the float parade is on the UNESCO Intangible Cultural Heritage List) and Borobudur Temple in Indonesia(a UNESCO World Heritage Site).

USTER RSO 3D质量控制系统的生产实践

在不同材料的差异化纱线生产过程中,按照同电清工艺,将开通与未开通USTER RSO 3D系统的络筒机切疵情况进行了对比实验,以探究USTER RSO 3D环锭纺纱优化系统在纺纱质量改善方面的作用。生产实践显示,USTER RSO 3D系统能够提供一个质量控制的闭环,减少质量不良管纱的产生,降低络筒切疵10%以上,纱线报警减少50%以上,坏纱剔纱减少80%以上,提高了工作效率。

New Fragile Watermarking Technique to Identify Inserted Video Objects Using H.264 and Color Features

The transmission of video content over a network raises various issues relating to copyright authenticity,ethics,legality,and privacy.The protection of copyrighted video content is a significant issue in the video industry,and it is essential to find effective solutions to prevent tampering and modification of digital video content during its transmission through digital media.However,there are stillmany unresolved challenges.This paper aims to address those challenges by proposing a new technique for detectingmoving objects in digital videos,which can help prove the credibility of video content by detecting any fake objects inserted by hackers.The proposed technique involves using two methods,the H.264 and the extraction color features methods,to embed and extract watermarks in video frames.The study tested the performance of the system against various attacks and found it to be robust.The evaluation was done using different metrics such as Peak-Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index Measure(SSIM),Bit Correction Ratio(BCR),and Normalized Correlation.The accuracy of identifying moving objects was high,ranging from 96.3%to 98.7%.The system was also able to embed a fragile watermark with a success rate of over 93.65%and had an average capacity of hiding of 78.67.The reconstructed video frames had high quality with a PSNR of at least 65.45 dB and SSIMof over 0.97,making them imperceptible to the human eye.The system also had an acceptable average time difference(T=1.227/s)compared with other state-of-the-art methods.

图像引导和点云空间约束的公路洒落物检测定位方法

公路洒落物是影响交通安全的重要因素之一,为了解决中小尺度公路洒落物检测中的漏检、误检以及难以定位等问题,本文提出了一种图像引导和点云空间约束的公路洒落物检测定位方法。该方法使用改进的YOLOv7-OD网络处理图像数据获取二维目标预测框信息,将目标预测框投影到激光雷达坐标系下得到锥形感兴趣区域(region of interest,ROI)。在ROI区域内的点云空间约束下,联合点云聚类和点云生成算法获得不同尺度的洒落物在三维空间中的检测定位结果。实验表明:改进的YOLOv7-OD网络在中尺度目标上的召回率和平均精度分别为85.4%和82.0%,相比YOLOv7网络分别提升6.6和8.0个百分点;在小尺度目标上的召回率和平均精度分别为66.8%和57.3%,均提升5.3个百分点;洒落物定位方面,对于距离检测车辆30~40 m处的目标,深度定位误差为0.19 m,角度定位误差为0.082°,实现了多尺度公路洒落物的检测和定位。

基于改进YOLOX与多级数据关联的行人多目标跟踪算法研究

目标跟踪是计算机视觉领域的基本问题,行人多目标跟踪在智能监控、智慧交通等多个领域有着广泛的应用前景。然而实际跟踪场景中存在频繁遮挡、尺度变化等情况,给多目标跟踪算法带来了极大的挑战。为了进一步提升跟踪精度,在DeepSORT的基础上,提出一种基于改进YOLOX与多级数据关联的行人多目标跟踪算法。对于检测器,为了增强网络的特征表达能力,提高检测精度,在YOLOX骨架网络与颈部网络分别引入ECA通道注意力模块与ASFF自适应特征融合模块。对于身份识别特征,为了减少数据关联步骤的错误匹配数量,提高跟踪效率,使用轻量的OSNet重识别网络与NSA卡尔曼滤波获取目标特征。对于数据关联,为了减少身份切换次数,避免目标丢失,将检测与跟踪都进行分类处理,使用不同的相似性计算方法,实现基于检测置信度与轨迹状态的多级数据关联。实验结果表明:与改进前YOLOX与DeepSORT简单结合的算法相比,在YOLOX中引入ECA模块与ASFF模块使误检数量大幅降低,使用YOLOX-s模型时降幅可达17%;结合OSNet模型与NSA卡尔曼滤波的特征提取方法能提高跟踪稳定性,IDF1指标提高0.77%,IDSW减少947;基于检测置信度与轨迹状态的多级数据关联算法可以明显改善跟踪性能,MOTA指标提升3.36%。算法最终在MOT17与MOT20测试集上的MOTA达80.4%与77.7%,IDF1达78.4%与76.7%。提出的行人多目标跟踪方法相较于其他先进算法在跟踪精度与跟踪速度上达到更好的平衡,可为工业上在线行人多目标跟踪应用提供参考。

CNN-Transformer特征融合多目标跟踪算法

在卷积神经网络(CNN)中,卷积运算能高效地提取目标的局部特征,却难以捕获全局表示;而在视觉Transformer中,注意力机制可以捕获长距离的特征依赖,但会忽略局部特征细节。针对以上问题,提出一种基于CNN-Transformer双分支主干网络进行特征提取和融合的多目标跟踪算法CTMOT(CNN-transformer multi-object tracking)。使用基于CNN和Transformer双分支并行的主干网络分别提取图像的局部和全局特征。使用双向桥接模块(two-way braidge module,TBM)对两种特征进行充分融合。将融合后的特征输入两组并行的解码器进行处理。将解码器输出的检测框和跟踪框进行匹配,完成多目标跟踪任务。在多目标跟踪数据集MOT17、MOT20、KITTI以及UADETRAC上进行评估,CTMOT算法的MOTP和IDs指标在四个数据集上均达到了SOTA效果,MOTA指标分别达到了76.4%、66.3%、92.36%和88.57%,在MOT数据集上与SOTA方法效果相当,在KITTI数据集上达到SOTA效果。由于同时完成目标检测和关联,能够端到端进行目标跟踪,跟踪速度可达35 FPS,表明CTMOT算法在跟踪的实时性和准确性上达到了较好的平衡,具有较大潜力。

基于YOLO v8n-seg和改进Strongsort的多目标小鼠跟踪方法

多目标小鼠跟踪是小鼠行为分析的基本任务,是研究社交行为的重要方法。针对传统小鼠跟踪方法存在只能跟踪单只小鼠以及对多目标小鼠跟踪需要对小鼠进行标记从而影响小鼠行为等问题,提出了一种基于实例分割网络YOLO v8n-seg和改进Strongsort相结合的多目标小鼠无标记跟踪方法。使用RGB摄像头采集多目标小鼠的日常行为视频,标注小鼠身体部位分割数据集,对数据集进行增强后训练YOLO v8n-seg实例分割网络,经过测试,模型精确率为97.7%,召回率为98.2%,mAP50为99.2%,单幅图像检测时间为3.5 ms,实现了对小鼠身体部位准确且快速地分割,可以满足Strongsort多目标跟踪算法的检测要求。针对Strongsort算法在多目标小鼠跟踪中存在的跟踪错误问题,对Strongsort做了两点改进:对匹配流程进行改进,将未匹配上目标的轨迹和未匹配上轨迹的目标按欧氏距离进行再次匹配;对卡尔曼滤波进行改进,将卡尔曼滤波中表示小鼠位置和运动状态的小鼠身体轮廓外接矩形框替换为以小鼠身体轮廓质心为中心、对角线为小鼠体宽的正方形框。经测试,改进后Strongsort算法的ID跳变数为14,MOTA为97.698%,IDF1为85.435%,MOTP为75.858%,与原Strongsort相比,ID跳变数减少88%,MOTA提升3.266个百分点,IDF1提升27.778个百分点,与Deepsort、ByteTrack和Ocsort相比,在MOTA和IDF1上均有显著提升,且ID跳变数大幅降低,结果表明改进Strongsort算法可以提高多目标无标记小鼠跟踪的稳定性和准确性,为小鼠社交行为分析提供了一种新的技术途径。

层级引导的增强型多目标萤火虫算法

针对多目标萤火虫算法在求解过程中易产生振荡和聚集现象,导致开发能力较弱、求解精度不佳的问题,提出一种层级引导的增强型多目标萤火虫算法(hierarchical guided enhanced multi-objective firefly algorithm,HGEMOFA)。构建层级引导模型,利用非支配排序获得不同层级个体,用优势层个体引导劣势层个体进化,明确引导方向,解决了进化过程中出现的振荡,减少了聚集现象的出现,增强了算法收敛性;引入莱维飞行扰动最优层个体,增强算法的全局搜索能力;每代进化完成后,对当前种群采用变异机制,增强算法的局部开发能力;把变异后的种群和前一代种群合并进行环境选择,筛选出和前一代种群规模相同的子代,避免优势解丢失。实验结果表明:HGEMOFA能有效增强解的收敛性和多样性。

基于LMIENet图像增强的矿井下低光环境目标检测方法

煤矿井下工作环境复杂,存在人造光源亮度低、粉尘多和水气密度大等不利因素,导致现有的目标检测算法在应用到煤矿井下时,存在提取特征困难、目标识别和定位精度低等问题。提出一种煤矿井下低照度环境目标检测算法,由矿井低光图像增强模块LMIENet和目标检测模块组成,使用图像增强模块对原始图像进行画质提升,恢复各类图像信息,再使用目标检测网络对增强图像进行特定目标检测,有效提高检测的精确度。在图像增强模块中,改进Zero-DCE算法设计轻量级增强参数预测网络,计算像素级增强参数矩阵,用于低光照图像的亮度调整和画质增强,该网络通过设计的非参考损失函数隐性衡量图像的增强效果,引导网络进行无监督学习,使网络能够不依赖配对数据集对原始图像进行自适应的画质增强。目标检测模块中,采用YOLO v8n目标检测模型,其轻量化的模型尺寸和高灵活性可避免模型整体复杂度过高;采用Focal-EIoU Loss改进回归损失函数,有效加速模型收敛并提升模型检测精度。实验结果显示:与经典目标检测算法Faster R–CNN,SSD,RetinaNet,FCOS等相比,提出算法在自建矿井人员数据集上表现出色,低光照环境下目标检测的mAP@0.5达到98.0%,mAP@0.5∶0.95达64.8%,在实验环境中单帧图像推理时间仅11 ms,优于其他对比方法,证明提出算法能够有效实现在煤矿井下低照度复杂环境下的目标检测,且耗时短、计算效率高。

基于改进YOLOv8的无人机航拍图像目标检测算法

针对现存无人机航拍图像目标检测算法检测精度较低、模型较为复杂的问题,提出一种改进YOLOv8的目标检测算法。在骨干网络引入多尺度注意力EMA,捕捉细节信息,以提高模型的特征提取能力;改进C2f模块,减小模型的计算量。提出了轻量级的Bi-YOLOv8特征金字塔网络结构改进YOLOv8的颈部,增强了模型多尺度特征融合能力,改善网络对小目标的检测精度。使用WIoU Loss优化原网络损失函数,引入一种动态非单调聚焦机制,提高模型的泛化能力。在无人机航拍数据集VisDrone2019上的实验表明,提出算法的mAP50为40.7%,较YOLOv8s提升了1.5%,参数量降低了42%,同时相比于其他先进的目标检测算法在精度和速度上均有提升,证明了改进算法的有效性和先进性。

边缘信息增强的显著性目标检测网络

针对显著性目标检测任务中识别结果边缘模糊的问题,提出了一种能够充分利用边缘信息增强边缘像素置信度的新模型。该网络主要有两个创新点:设计三重注意力模块,利用预测图的特点直接生成前景、背景和边缘注意力,并且生成注意力权重的过程不增加任何参数;设计边缘预测模块,在分辨率较高的网络浅层进行有监督的边缘预测,并与网络深层的显著图预测融合,细化了边缘。在6种常用公开数据集上用定性和定量的方法评估了该模型,并且与其他模型进行充分对比,证明设计的新模型能够取得最优的效果。此外,该模型参数量为30.28 M,可以在GTX 1080 Ti显卡上达到31帧·s^(-1)的预测速度。

OBE理念下自动化一流本科专业课程教学改革与实践

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