Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

Relationship between mitochondrial respiratory dysfunction of blood mononuclear cells and heart failure severity

Chronic heart failure(CHF)is a clinical syndrome manifested by reduced pumping ability of the heart,increased pressure in heart chambers in both physical activity and at rest.The symptoms of this syndrome are dyspnea,undue fatigability,peripheral edema,which follow structural and functional changes of the myocardium.[1]The growing incidence of CHF,especially among elderly people,is an urgent problem for medicine in the vast majority of industrialized countries.For instance,in Russian Federation,CHF is diagnosed in about 7%of cardiovascular patients.At the same time,this indicator varies from 0.3%in young people(20-29 years old)to 70%in the older age group.[2,3].

Workflow for treating inadvertent arterial placement of the central venous catheter

We read the article“How to manage the malposition of deep vein catheterization into the artery”[1]with keen interest.However,we have several concerns with the proposed algorithm.First,the site of catheter misplacement is assumed to be the subclavian artery,the most frequent site of misplacement during internal jugular vein catheterization.[2]However,catheter misplacement can occur in the common carotid and vertebral arteries during internal jugular vein catheterization.[2,3]If a catheter is misplaced in one of these arteries,preventing cerebral ischemia is a priority.[2,4,5]For example,if a thrombus forms around the catheter,a method is chosen to resolve it while preventing dispersion and closing the perforation.[2,6]Therefore,open surgical closure must be selected.Second,the algorithm may not handle instances of realistic catheter misplacement in the arteries.We assume a case where an internal jugular venous catheter(5Fr double-lumen catheter)is inserted but accidentally penetrates the subclavian artery and is placed in the thoracic cavity.Suppose that the injured site is about 5 mm from the confluence of the right common carotid or vertebral arteries.

MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.

Dislocation of implantable collamer phakic lens after blunt trauma

Dear Editor,I mplantable collamer lens(ICL)surgery demonstrates longterm stability and favorable refractive outcome[1-2].An increasing number of individuals across all age groups opt for refraction removal through ICL surgery.Currently,instances of ICL displacement resulting from trauma remain rare,and there are no documented cases of ICL damage due to blunt trauma.Postoperative ICL dislocations were found in 7 eyes(9775 total,equating to 0.072%of ICL implants),averaging 28.6mo(11-82mo)[3].

Improved Convolutional Neural Network for Traffic Scene Segmentation

In actual traffic scenarios,precise recognition of traffic participants,such as vehicles and pedestrians,is crucial for intelligent transportation.This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants.The algorithmincorporates long and shortterm memory networks and the fused attention module(GSAM,GCT,and Spatial Attention Module)to enhance the algorithm’s capability to process both global and local information.Additionally,to increase the network’s initial operation stability,the original network activation function was replaced with Gaussian error linear unit.Experiments were conducted using the publicly available Cityscapes dataset.Comparing the test results,it was observed that the revised algorithmoutperformed the original algorithmin terms of AP_(50),AP_(75),and othermetrics by 8.7%and 9.6%for target detection and 12.5%and 13.3%for segmentation.

Dynamic SLAM Visual Odometry Based on Instance Segmentation:A Comprehensive Review

Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map construction.With the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM.Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s positioning.This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and disadvantages.Through comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments.However,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems.In recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation.Finally,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.

Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.

Real-time instance segmentation based on contour learning

Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial contour.To solve these problems,a real-time instance segmentation algorithm based on contour learning was proposed.Firstly,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution kernel.Secondly,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more comprehensively.Thirdly,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real contour.Finally,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour.The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s−1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU.The proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.

China’s Third Plenum:Implications for Indonesia’s Education

The Third Plenum holds a unique place in the Communist Party of China(CPC)’s political calendar because the Party often introduces significant reforms and outlines strategic priorities during the event.For instance,the Third Plenary Session of the 11th Central Committee of CPC in 1978 marked the beginning of China’s reform and opening-up drive under Deng Xiaoping,after which the country underwent historic economic development.

Research on the Preservation Method of Traditional Village Roof Information:A Case Study of Gubeikou Village

Traditional Chinese villages serve as crucial repositories of traditional culture.However,In China,the urgent task of preserving information about traditional village architecture has arisen due to the degradation of these villages’appearance caused by rapid urbanization in recent years.This paper proposes a method for preserving information about traditional village rooftops based on high spatial resolution remote sensing imagery.Leveraging an improved Mask R-CNN model,the method conducts target recognition on the rooftops of traditional village buildings and generates vectorized representations of these rooftops.The precision rate,recall rate,and F1-score achieved in the experimental results are 93.26%,86.33%,and 92.02%,respectively.These findings indicate the effectiveness of the proposed method in preserving information about traditional village architecture and providing a viable approach to support the sustainable development of traditional villages in China.

CORRIGENDUM

Correction.The corresponding author of“A Multi-Center Randomized Controlled Study UsingΔP_(CO2)/Ca-v_(O2) as the Target to Guide Early Tissue Hypoperfusion in Sepsis in Plateau Areas,”published in Volume 8 Issue 1(2024)of Journal of Clinical and Nursing Research(https://www.doi.org/10.26689/jcnr.v8i1.5944),wrote to the editors about a typographical error in the article.All instances of“>1.84”(found in the abstract and conclusion)should be corrected to“<1.84.”

Stroke:challenges and opportunities--Perspectives on clinical research advances and future outlook in 2024

Dear readers,In the field of stroke research and treatment,cutting-edge technologies and studies continue to emerge,providing new perspectives and strategies for exploring the mechanisms and treatment methods of stroke.According to the latest advancements,neuroimaging techniques play a crucial role in stroke research.MRI technology,for instance,is essential for evaluating stroke patients.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.

基于MultiGen Creator构建三维数字小区的理论与实践

在阐述数字小区构建意义的基础上,结合MultiGen Creator专业建模软件和Vega可视化仿真应用软件,详细论述了构建三维数字小区的理论与实践,重点阐述了小区场景中对象的分类以及各自的建模理论,并对典型的两种实体在实践上的构建技术具体描述了其在Creator中的实现步骤,体现了该软件在建模方面显著节约面片的功能以及所建模型在Vega系统中实时顺畅显示的优越性。

基于Web Service的OGSA研究

简单介绍了网格技术、Web Service技术,并且在此两种技术的基础上,对OGSA进行了研究,分析了OGSA的架构、支撑技术和其对Web Service的扩展继承。

现实主义、现代主义、后现代主义的继往共生——读“A Modern Instance”《一个现代的例证》之联想

小说《一个现代的例证》是威廉·迪安·豪威尔斯 ( William Dean Howells,1837—192 0 )的杰作之一。在后现代主义空前繁荣和发展的时代 ,重读 Howells的小说 ,发现它依然余音绕梁 ,意味犹存。纵观文学发展的历史 ,不难发现文学创作始终处于不断演变与进化之中 ,各阶段的发展相互渗透 。

实例化方法在L-System树木建模中的应用

本文提出一种针对使用L-System建立的树木模型的实例化渲染方案。该方案首先获取L-System树木的完整模型,然后再使用实例化方法优化树木的批量渲染。

河海大学:基于GLASSFISH的负载均衡设计

利用负载均衡集群可以使多个应用服务器实例并行工作,提高高性能服务器的资源利用率和应用系统的负载能力。针对目前数字校园中管理信息系统运行中存在的问题,本文提出了基于Glassfish应用服务器的负载均衡设计方案,以求达到节约成本、提高性能的目标。