Improved Segmented Belief Propagation List Decoding for Polar Codes with Bit-Flipping

Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved segmented belief propagation list decoding based on bit flipping(SBPL-BF) is proposed. On the one hand, the proposed algorithm makes use of the cooperative characteristic in BPL decoding such that the codeword is decoded in different BP decoders. Based on this characteristic, the unreliable bits for flipping could be split into multiple subblocks and could be flipped in different decoders simultaneously. On the other hand, a more flexible and effective processing strategy for the priori information of the unfrozen bits that do not need to be flipped is designed to improve the decoding convergence. In addition, this is the first proposal in BPL decoding which jointly optimizes the bit flipping of the information bits and the code bits. In particular, for bit flipping of the code bits, a H-matrix aided bit-flipping algorithm is designed to enhance the accuracy in identifying erroneous code bits. The simulation results show that the proposed algorithm significantly improves the errorcorrection performance of BPL decoding for medium and long codes. It is more than 0.25 d B better than the state-of-the-art BPL decoding at a block error rate(BLER) of 10^(-5), and outperforms CA-SCL decoding in the low signal-to-noise(SNR) region for(1024, 0.5)polar codes.

Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV

As a typical transportation tool in the intelligent manufacturing system,Automatic Guided Vehicle(AGV)plays an indispensable role in the automatic production process of the workshop.Therefore,integrating AGV resources into production scheduling has become a research hotspot.For the scheduling problem of the flexible job shop adopting segmented AGV,a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function,and an improved genetic algorithmis designed to solve the problem in this study.The algorithmdesigns a two-layer codingmethod based on process coding and machine tool coding and embeds the task allocation of AGV into the decoding process to realize the real dual resource integrated scheduling.When initializing the population,three strategies are designed to ensure the diversity of the population.In order to improve the local search ability and the quality of the solution of the genetic algorithm,three neighborhood structures are designed for variable neighborhood search.The superiority of the improved genetic algorithmand the influence of the location and number of transfer stations on scheduling results are verified in two cases.

Petroleum Retention,Intraformational Migration and Segmented Accumulation within the Organic-rich Shale in the Cretaceous Qingshankou Formation of the Gulong Sag,Songliao Basin,Northeast China

In this study,organic geochemical and petrological analyses were conducted on 111 shale samples from a well to understand the retention,intraformational migration and segmented accumulation(shale oil enrichment in different intervals is unconnected)features of shale oil within the organic-rich shale in the Qingshankou Formation of the Gulong Sag.Our study shows that retained petroleum characteristics in the investigated succession are mainly influenced by three factors:organic richness,intraformational migration and segmented accumulation.Organic matter richness primarily controls the amount of retained petroleum,especially the‘live’component indicated by the S_(2)value rather than the total organic carbon(TOC)figure alone.The negative expulsion efficiencies determined by mass-balance calculations of hydrocarbons reveal that petroleum from adjacent organic-rich intervals migrates into the interval of about 2386-2408 m,which is characterized by high free hydrocarbon(S_(1)),OSI and saturated hydrocarbons content,along with a greater difference inδ^(13)C values between polar compounds(including resins and asphaltenes)and saturated hydrocarbons.The depth-dependent heterogeneity of carbon isotope ratios(δ^(13)C)of mud methane gas,δ^(13)C of extracts gross composition(SARA),δ^(13)C of kerogen and SARA content of extracts suggest that the studied succession can be subdivided into four intervals.The shale oil sealing enrichment character in each interval is further corroborated by the distinctδ^(13)C values of mud methane gas in different intervals.Due to the migration of petroleum into the 2386-2408 m interval,the S_(1),OSI and saturated hydrocarbons content of the interval show higher relative values.The maturity of organic matter in the 2471-2500 m interval is at the highest with the smaller size molecular components of the retained petroleum.Thus,favorable‘sweet spots’may be found in the 2386-2408 m interval and the 2471-2500 m interval,according to the experiment results in this study.

Visual Semantic Segmentation Based on Few/Zero-Shot Learning:An Overview

Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block,and it plays a crucial role in environmental perception.Conventional learning-based visual semantic segmentation approaches count heavily on largescale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories.This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning.The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen categories from a few labeled or even zero-labeled samples,which advances the extension to practical applications.Therefore,this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances.Specifically,the preliminaries on few/zeroshot visual semantic segmentation,including the problem definitions,typical datasets,and technical remedies,are briefly reviewed and discussed.Moreover,three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation,including image semantic segmentation,video object segmentation,and 3D segmentation.Finally,the future challenges of few/zero-shot visual semantic segmentation are discussed.

Multilevel Attention Unet Segmentation Algorithmfor Lung Cancer Based on CT Images

Lung cancer is a malady of the lungs that gravely jeopardizes human health.Therefore,early detection and treatment are paramount for the preservation of human life.Lung computed tomography(CT)image sequences can explicitly delineate the pathological condition of the lungs.To meet the imperative for accurate diagnosis by physicians,expeditious segmentation of the region harboring lung cancer is of utmost significance.We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner,erect an interpretable model,and attain segmentation of lung cancer.The specific advancements can be encapsulated as follows:1)Concentration on the lung parenchyma region:Based on 16-bit CT image capturing and the luminance characteristics of lung cancer,we proffer an intercept histogram algorithm.2)Focus on the specific locus of lung malignancy:Utilizing the spatial interrelation of lung cancer,we propose a memory-based Unet architecture and incorporate skip connections.3)Data Imbalance:In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples,we scrutinize the existing loss function and suggest a mixed loss function.Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy,measured as Area Overlap Measure(AOM)is superior to 0.81,which markedly ameliorates in comparison with conventional algorithms,thereby facilitating physicians in diagnosis.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Micro segment analysis of supercritical methane thermal-hydraulic performance and pseudo-boiling in a PCHE straight channel

The printed circuit heat exchanger(PCHE) is receiving wide attention as a new kind of compact heat exchanger and is considered as a promising vaporizer in the LNG process. In this paper, a PCHE straight channel in the length of 500 mm is established, with a semicircular cross section in a diameter of 1.2 mm.Numerical simulation is employed to investigate the flow and heat transfer performance of supercritical methane in the channel. The pseudo-boiling theory is adopted and the liquid-like, two-phase-like, and vapor-like regimes are divided for supercritical methane to analyze the heat transfer and flow features.The results are presented in micro segment to show the local convective heat transfer coefficient and pressure drop. It shows that the convective heat transfer coefficient in segments along the channel has a significant peak feature near the pseudo-critical point and a heat transfer deterioration when the average fluid temperature in the segment is higher than the pseudo-critical point. The reason is explained with the generation of vapor-like film near the channel wall that the peak feature related to a nucleateboiling-like state and heat transfer deterioration related to a film-boiling-like state. The effects of parameters, including mass flow rate, pressure, and wall heat flux on flow and heat transfer were analyzed.In calculating of the averaged heat transfer coefficient of the whole channel, the traditional method shows significant deviation and the micro segment weighted average method is adopted. The pressure drop can mainly be affected by the mass flux and pressure and little affected by the wall heat flux. The peak of the convective heat transfer coefficient can only form at high mass flux, low wall heat flux, and near critical pressure, in which condition the nucleate-boiling-like state is easier to appear. Moreover,heat transfer deterioration will always appear, since the supercritical flow will finally develop into a filmboiling-like state. So heat transfer deterioration should be taken seriously in the design and safe operation of vaporizer PCHE. The study of this work clarified the local heat transfer and flow feature of supercritical methane in microchannel and contributed to the deep understanding of supercritical methane flow of the vaporization process in PCHE.

Classification of hepatobiliary scintigraphy patterns in segmented gallbladder according to anatomical discordance

BACKGROUND Hepatobiliary scintigraphy(HBS)is a useful diagnostic imaging technique that uses radiotracers to evaluate the function of the gallbladder(GB)and biliary system.In segmented GB,some HBS images reveal a discordant GB boundary as compared to anatomical images.AIM To evaluate the characteristics of HBS in segmented GB and determine the clinical relevance according to HBS characteristics.METHODS A total of 268 patients with chronic cholecystitis,gallstones,or biliary colic symptoms who underwent HBS between 2011 and 2020 were enrolled.Segmented GB was defined as segmental luminal narrowing of the GB body on computed tomography(CT)or magnetic resonance(MR)images,and HBS was examined 1 mo before or after CT or MR.Segmented GB was classified into 3 types based on the filling and emptying patterns of the proximal and distal segments according to the characteristics of HBS images,and GB ejection fraction(GBEF)was identified:Type 1 was defined as a normal filling and emptying pattern;Type 2 was defined as an emptying defect on the distal segment;and Type 3 was defined as a filling defect in the distal segment.RESULTS Segmented GB accounted for 63 cases(23.5%),including 36 patients(57.1%)with Type 1,18 patients(28.6%)with Type 2,and 9 patients(14.3%)with Type 3 emptying pattern.Thus,approximately 43%of HBS images showed a discordant pattern as compared to anatomical imaging of segmented GB.Although there were no significant differences in clinical symptoms,rate of cholecystectomy,or pathological findings based on the type,most gallstones occurred in the distal segment.Reported GBEF was 62.50%±24.79%for Type 1,75.89%±17.21%for Type 2,and 88.56%±7.20%for Type 3.Type 1 showed no difference in reported GBEF compared to the non-segmented GB group(62.50%±24.79%vs 67.40%±21.78%).In contrast,the reported GBEF was higher in Types 2 and 3 with defective emptying and filling when compared to Type 1(80.11%±15.70%vs 62.57%±24.79%;P=0.001).CONCLUSION In segmented GB,discordance in the filling patterns detected by HBS and anatomical imaging could lead to misinterpretation of GBEF.For this reason,clinicians should be cautious when interpreting HBS results in patients with segmented GB.

Micro-Sized Pinhole Inspection with Segmented Time Reversal and High-Order Modes Cluster Lamb Waves Based on EMATs

Pinhole corrosion is difficult to discover through conventional ultrasonic guided waves inspection,particularly for micro-sized pinholes less than 1 mm in diameter.This study proposes a new micro-sized pinhole inspection method based on segmented time reversal(STR)and high-order modes cluster(HOMC)Lamb waves.First,the principle of defect echo enhancement using STR is introduced.Conventional and STR inspection experiments were conducted on aluminum plates with a thickness of 3 mm and defects with different diameters and depths.The parameters of the segment window are discussed in detail.The results indicate that the proposed method had an amplitude four times larger than of conventional ultrasonic guided waves inspection method for pinhole defect detection and could detect micro-sized pinhole defects as small as 0.5 mm in diameter and 0.5 mm in depth.Moreover,the segment window location and width(5-10 times width of the conventional excitation signal)did not affect the detection sensitivity.The combination of low-power and STR is more conducive to detection in different environments,indicating the robustness of the proposed method.Compared with conventional ultrasonic guided wave inspection methods,the proposed method can detect much smaller defect echoes usually obscured by noise that are difficult to detect with a lower excitation power and thus this study would be a good reference for pinhole defect detection.

Cascading multi-segment rupture process of the 2023 Turkish earthquake doublet on a complex fault system revealed by teleseismic P wave back projection method

In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back projection analysis.Data in two frequency bands(0.5-2 Hz and 1-3 Hz)are used in the imaging processes.The results show that the rupture of the first event extends about 200 km to the northeast and about 150 km to the southwest,lasting~90 s in total.The southwestern rupture is triggered by the northeastern rupture,demonstrating a sequential bidirectional unilateral rupture pattern.The rupture of the second event extends approximately 80 km in both northeast and west directions,lasting~35 s in total and demonstrates a typical bilateral rupture feature.The cascading ruptures on both sides also reflect the occurrence of selective rupture behaviors on bifurcated faults.In addition,we observe super-shear ruptures on certain fault sections with relatively straight fault structures and sparse aftershocks.

Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisation

Cancer is one of the leading causes of death in the world,with radiotherapy as one of the treatment options.Radiotherapy planning starts with delineating the affected area from healthy organs,called organs at risk(OAR).A new approach to automatic OAR seg-mentation in the chest cavity in Computed Tomography(CT)images is presented.The proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder,which is the baseline adopted in this work.The new two‐branch CS‐SA U‐Net architecture is proposed,which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function(CS‐SA)blocks are inserted between the encoder and decoder,which enabled the use of con-sistency regularisation.The proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient(oesophagus-0.8714,heart-0.9516,trachea-0.9286,aorta-0.9510)and Hausdorff distance(oesophagus-0.2541,heart-0.1514,trachea-0.1722,aorta-0.1114)and significantly outperforms the baseline.The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presenceof occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficultyof segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scalefeatures based onDeepLabv3+is designed to address the difficulties of small object segmentation and blurred targetedge segmentation. First,we use CrossFormer as the backbone feature extraction network to achieve the interactionbetween large- and small-scale features, and establish self-attention associations between features at both large andsmall scales to capture global contextual feature information. Next, an improved atrous spatial pyramid poolingmodule is introduced to establish multi-scale feature maps with large- and small-scale feature associations, andattention vectors are added in the channel direction to enable adaptive adjustment of multi-scale channel features.The proposed networkmodel is validated using the PotsdamandVaihingen datasets. The experimental results showthat, compared with existing techniques, the network model designed in this paper can extract and fuse multiscaleinformation, more clearly extract edge information and small-scale information, and segment boundariesmore smoothly. Experimental results on public datasets demonstrate the superiority of ourmethod compared withseveral state-of-the-art networks.

Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic discrimination.To tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding network.Concretely,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their wholes.Secondly,we designed a multi-prototype enhancement module to enhance the prototype discriminability.Specifically,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich semantics.Besides,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category similarity.Ablation studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic discrimination.Moreover,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

Adaptive Segmentation for Unconstrained Iris Recognition

In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture device.When these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction problems.Herein,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments.First,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera region.The image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous characteristics.The iris images are accordingly classified into seven categories based on their global RGB intensities.After the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring.Finally,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring.The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.

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.

A semantic segmentation-based underwater acoustic image transmission framework for cooperative SLAM

With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes.Various technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene comprehension.This paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an image.The methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet architecture.Afterward,we use an annotation tool to accurately label the segmented regions.Upon labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob characteristics.For the classification stage,a convolution neural network(CNN)is employed.This comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in images.The experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been documented.After analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

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.

Artificial intelligence in the anterior segment of eye diseases

Ophthalmology is a subject that highly depends on imaging examination.Artificial intelligence(AI)technology has great potential in medical imaging analysis,including image diagnosis,classification,grading,guiding treatment and evaluating prognosis.The combination of the two can realize mass screening of grass-roots eye health,making it possible to seek medical treatment in the mode of“first treatment at the grass-roots level,two-way referral,emergency and slow treatment,and linkage between the upper and lower levels”.On the basis of summarizing the AI technology carried out by scholars and their teams all over the world in the field of ophthalmology,quite a lot of studies have confirmed that machine learning can assist in diagnosis,grading,providing optimal treatment plans and evaluating prognosis in corneal and conjunctival diseases,ametropia,lens diseases,glaucoma,iris diseases,etc.This paper systematically shows the application and progress of AI technology in common anterior segment ocular diseases,the current limitations,and prospects for the future.