A semi-analytical model for coupled flow in stress-sensitive multi-scale shale reservoirs with fractal characteristics

A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.

Achieving high ductility and low in-plane anisotropy in magnesium alloy through a novel texture design strategy

Texture regulation is a prominent method to modify the mechanical properties and anisotropy of magnesium alloy.In this work,the Mg-1Al-0.3Ca-0.5Mn-0.2Gd(wt.%)alloy sheet with TD-tilted and circular texture was fabricated by unidirectional rolling(UR)and multidirectional rolling(MR)method,respectively.Unlike generating a strong in-plane mechanical anisotropy in conventional TD-tilted texture,the novel circular texture sample possessed a weak in-plane yield anisotropy.This can be rationalized by the similar proportion of soft grains with favorable orientation for basalslip and{10.12}tensile twinning during the uniaxial tension of circular-texture sample along different directions.Moreover,compared with the TD-tilted texture,the circular texture improved the elongation to failure both along the rolling direction(RD)and transverse direction(TD).By quasi-in-situ EBSD-assisted slip trace analysis,higher activation of basal slip was observed in the circular-texture sample during RD tension,contributing to its excellent ductility.When loading along the TD,the TD-tilted texture promoted the activation of{10.12}tensile twins significantly,thus providing nucleation sites for cracks and deteriorating the ductility.This research may shed new insights into the development of formable and ductile Mg alloy sheets by texture modification.

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.

The Mechanism of Cube Texture Formation in Ni-7 at%W Substrate

In order to obtain the sharp cube texture,a new process,the intermediate annealing rolling technique,has been introduced to prepare the Ni7W substrate.In this paper,a cubic texture content up to 98.5%within 10°of the standard cubic orientation is obtained in the final substrate and the influence of this improved rolling technique on the cube texture formation has been discussed.The results show that the increased cube texture in the Ni7W substrate is caused by the optimized deformation texture and the increased nucleated fraction of the cube grains.

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

Multi-scale context-aware network for continuous sign language recognition

The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information.In addition,the signs have different lengths,whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling,which disturbs the perception of complete signs.In this study,we propose a Multi-Scale Context-Aware network(MSCA-Net)to solve the aforementioned problems.Our MSCA-Net contains two main modules:(1)Multi-Scale Motion Attention(MSMA),which uses the differences among frames to perceive information of the hands and face in multiple spatial scales,replacing the heavy feature extractors;and(2)Multi-Scale Temporal Modeling(MSTM),which explores crucial temporal information in the sign language video from different temporal scales.We conduct extensive experiments using three widely used sign language datasets,i.e.,RWTH-PHOENIX-Weather-2014,RWTH-PHOENIX-Weather-2014T,and CSL-Daily.The proposed MSCA-Net achieve state-of-the-art performance,demonstrating the effectiveness of our approach.

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.

Crystal plasticity finite element simulations on extruded Mg-10Gd rod with texture gradient

The mechanical properties of an extruded Mg-10Gd sample, specifically designed for vascular stents, are crucial for predicting its behavior under service conditions. Achieving homogeneous stresses in the hoop direction, essential for characterizing vascular stents, poses challenges in experimental testing based on standard specimens featuring a reduced cross section. This study utilizes an elasto-visco-plastic self-consistent polycrystal model(ΔEVPSC) with the predominant twinning reorientation(PTR) scheme as a numerical tool, offering an alternative to mechanical testing. For verification, various mechanical experiments, such as uniaxial tension, compression, notched-bar tension, three-point bending, and C-ring compression tests, were conducted. The resulting force vs. displacement curves and textures were then compared with those based on the ΔEVPSC model. The computational model's significance is highlighted by simulation results demonstrating that the differential hardening along with a weak strength differential effect observed in the Mg-10Gd sample is a result of the interplay between micromechanical deformation mechanisms and deformation-induced texture evolution. Furthermore, the study highlights that incorporating the axisymmetric texture from the as-received material incorporating the measured texture gradient significantly improves predictive accuracy on the strength in the hoop direction. Ultimately, the findings suggest that the ΔEVPSC model can effectively predict the mechanical behavior resulting from loading scenarios that are impossible to realize experimentally, emphasizing its valuable contribution as a digital twin.

A texture-modified dessert with high nutritional value designed for people with dysphagia:effect of refrigeration and frozen storage

Dysphagia is commonly associated with malnutrition and an increased choking risk.To overcome these complications,food designed for people with dysphagia should have an appropriate texture and a high nutritional value.In this study,six formulations of a strawberry dessert enriched in protein(calcium caseinate)and fiber(wheat dextrin or inulin)were developed using different hydrocolloids(xanthan gum,carboxymethyl cellulose or modified starch)to provide desirable texture and stability.Nutritional value was calculated and total phenolic content and antioxidant activity of the samples were analyzed.Back-extrusion test,rheological measurements and sensory analysis were performed in refrigerated and frozen samples to characterize their textural and viscoelastic properties.The high content in protein(14.7 g/100 g)and fiber(7.9-8.7 g/100 g)made possible to use the claims“high protein”and“high fiber”.Phytochemicals supplied by strawberries contributed to the antioxidant properties of the dessert.Loss tangent ranged 0.28-0.35 for all the formulations,indicating a weak gel behavior,which could be considered safe to swallow.The formulations with dextrin in combination with carboxymethyl cellulose or xanthan gum seemed to be less susceptible to structural changes during frozen storage.This work provides insights for the development of a nutrient-dense dessert that meets the requirements of people with dysphagia.

MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

Evolution of microstructure and texture of AZ80 magnesium alloy under hot torsion with constant decreasing temperature rate

Hot torsion tests for AZ80 magnesium alloy were carried out in the temperature range of 380℃-260℃,with a constant decreasing temperature rate of 10℃/s in order to weaken the basal texture and refine the grains.The results indicated that the average grain sizes were refined forming gradient structure with increasing specimen radial position from center(12.2-5.4μm),and that the initial basal texture intensity of the extruded magnesium alloy was weakened from 46.2 to 8.3.Furthermore,the extension twins(ETs)could be disintegrated from the twins forming separated twins with smaller sizes.Interestingly,ETs with the same twin variant intersecting with each other could be coalesced forming grains with similar orientation,while ETs with different twin variants were separated by twins boundaries contributing to grain refinement.Moreover,in addition to the conventional continuous dynamic recrystallized(CDRX)grains with 30˚orientation rotated around C-axis of the parent grains,CDRXed grains with 30˚rotation around a-axis and random rotation axis were also discerned.Besides,the CDRX evolution induced twins were also elaborated,exhibiting the complex competition between CDRX and twining.Hot torsion deformation with constant decreasing temperatures rate is an effective way of grain refinement and texture modification.

Unveiling the rolling texture variations ofα-Mg phases in a dual-phase Mg-Li alloy

LZ91 Mg-Li alloy plates with three types of initial texture were rolled by 70%reduction at both room temperature and 200℃to explore the rolling texture formation ofα-Mg phase.The results showed that the rolling texture is largely affected by the initial texture.All the samples exhibited two main texture components as RD-split double peaks texture and TD-split double peaks texture after large strain rolling.The intensity of the two texture components was strongly influenced by the initial orientation and rolling temperature.Extension twinning altered the large-split non-basal orientation to a near basal one at low rolling strain.The basal orientation induced by twinning is unstable,which finally transmitted to the RD-split texture.The strong TD-split texture formed due to slip-induced orientation transition from its initial orientation.The competition between prismatic and basal slip determined the intensity and tilt angle of the TD-split texture.By increasing the rolling temperature,the TD-split texture component was enhanced in all three samples.Limitation of extension twinning behavior and the promotion of prismatic slip at elevated temperature are the main reasons for the difference in hot and cold rolling texture.

On dry machining of AZ31B magnesium alloy using textured cutting tool inserts

Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.

Multi-scale Modeling and Finite Element Analyses of Thermal Conductivity of 3D C/SiC Composites Fabricating by Flexible-Oriented Woven Process

Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites.An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture.The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions.Three RVE models of cuboid,hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale.Besides,the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models.The prediction results at the mesoscale correspond closely to the experimental values.The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration.The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.

Tailoring the texture and mechanical properties of 3%Y_(2)O_(3)p/ZGK200 composites fabricated by unidirectional and cross rolling followed by annealing

3%Y_(2)O_(3)p/ZGK200 composites were subjected to unidirectional rolling(UR)and cross rolling(CR)at 400℃and 350℃followed by annealing at 300℃for 1 h.The microstructure,texture and mechanical properties of rolled and annealed composites were systematically studied.The rolled composites exhibited a heterogeneous microstructure,consisting of deformed grains elongated along rolling direction(RD)and Y_(2)O_(3)particles bands distributed along RD.After annealing,static recrystallization(SRX)occurred and most deformed grains transformed into equiaxed grains.A non-basal texture with two strong T-texture components was obtained after UR while a non-basal elliptical/circle texture with circle multi-peaks was obtained after CR,indicating that rolling path had great influences on texture of the composites.After annealing process,R-texture component disappeared or weakened,as results,a non-basal texture with double peaks tilting from normal direction(ND)to transverse direction(TD)and a more random non-basal texture with circle multi-peaks were obtained for UR and CR composites,respectively.The yield strength of rolled composites after UR showed obvious anisotropy along RD and TD while a low anisotropic yield strength was obtained after CR.Some Y_(2)O_(3)particles broke during rolling.The fracture of the composites was attributed to the existence of Y_(2)O_(3)clusters and interfacial debonding between particles and matrix during tension,as a result,the ductility was not as superior as matrix alloy.

Automatic area estimation of algal blooms in water bodies from UAV images using texture analysis

Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solution for detection and monitoring.Unmanned aerial vehicles(UAVs)have recently emerged as a tool for algal bloom detection,efficiently providing on-demand images at high spatiotemporal resolutions.This study developed an image processing method for algal bloom area estimation from the aerial images(obtained from the internet)captured using UAVs.As a remote sensing method of HAB detection,analysis,and monitoring,a combination of histogram and texture analyses was used to efficiently estimate the area of HABs.Statistical features like entropy(using the Kullback-Leibler method)were emphasized with the aid of a gray-level co-occurrence matrix.The results showed that the orthogonal images demonstrated fewer errors,and the morphological filter best detected algal blooms in real time,with a precision of 80%.This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.

Few-shot image recognition based on multi-scale features prototypical network

In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.

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.