5398 results about "Image segmentation" patented technology

The invention discloses a Mama-based spectrum dynamic fusion and double-attention enhancement medical image segmentation method, which comprises the following steps of: firstly, constructing a Mama integrated spectrum domain and attention pyramid module, fusing spectrum dynamic characteristics and a self-attention pooling mechanism, and performing frequency domain information compensation and local characteristic enhancement to obtain a spectrum dynamic fusion image; the spatial correlation loss caused by image blocking processing is relieved; secondly, designing a layered enhanced U-shaped architecture, deploying an MISAP module in a shallow layer of an encoder to capture multi-scale global context features, introducing a bipolar routing attention mechanism in a deep layer, and dynamically allocating sparse attention weights to focus a key pathological region; according to the method, the segmentation precision of complex edge textures and tiny lesions in medical images can be remarkably improved, and the Dice coefficient in breast tumor, polyp and abdominal organ segmentation tasks is averagely improved by 6.5%.

SOLUTION: A semi-supervised medical image segmentation method based on a visual language model includes the steps of: obtaining a medical image; inputting an unlabeled image and a text description into a visual language model, and obtaining a text-guided mask based on obtained dense image embedding and text embedding; inputting a labeled image into a student model, and calculating supervised loss by using obtained labeled image prediction; respectively inputting the unlabeled image into the student model and a teacher model to obtain unlabeled image prediction and a pseudo label, merging the text-guided mask with the pseudo label, and calculating semi-supervised loss by using the merged pseudo label and unlabeled image prediction; and performing medical image segmentation by using a trained student model on the basis of the supervised loss and the semi-supervised loss.EFFECT: A target segmentation region can be accurately identified by using advantages of text descriptions.SELECTED DRAWING: Figure 1

The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based image segmentation and dynamic target recognition method, which comprises the following steps of: accurately positioning a candidate region through multi-modal space-time fusion and dynamic confidence coefficient screening; strengthening spatial-temporal feature expression in a layering manner through a multi-level feature decoupler, and generating a multi-dimensional feature enhanced spatial-temporal candidate region; through a deformable segmentation network, a deformation convolution kernel and edge motion matching loss are combined, joint optimization of a geometric boundary and motion continuity is realized, and the segmentation robustness of a flexible target is improved; through optical flow back propagation dynamic correction and confidence coefficient propagation, high-precision segmentation masks with consistent time and space are output; and through a target trajectory re-identification and completion mechanism driven by a graph attention network, and in combination with optical flow deformation prediction, stable tracking in a shielding scene is realized.

The invention relates to the technical field of medical image segmentation, in particular to a few-sample medical image segmentation method based on edge perception multi-prototype learning, and the method comprises the steps: inputting support and query images into a feature encoder, and extracting support and query feature maps of different sizes; inputting into a local attention fusion prototype generator to generate a support foreground prototype; processing the support mask through dynamic corrosion operation to generate an inner boundary prototype; generating a multi-foreground local prototype through a multi-layer perceptron; local and global information is optimized through multi-scale feature extraction, and a multi-scale prototype is obtained; fusing to obtain a multi-prototype foreground prototype; dynamic calculation weighting is carried out on the multi-prototype foreground prototype by using a double-stage prototype optimization network, and automatic calibration is carried out; then prediction is carried out through a prototype prediction module, and finally collaborative optimization is carried out through a loss calculation module; the method can effectively solve the problem of edge detail loss involved in the background technology.

The invention provides an image segmentation method based on a deep learning remote sensing image, and relates to the field of image processing, and the method comprises the steps: S1, collecting a remote sensing image, carrying out the construction of a data set through the remote sensing image, and completing the data preprocessing; s2, constructing a remote sensing image semantic segmentation model; and S3, training, verifying and optimizing the remote sensing image semantic segmentation model constructed in the step S2 by adopting the data set in the step S1 to complete model construction. According to the method, segmentation precision and expression consistency are improved through multi-branch deep collaborative modeling, so that robustness of the model in boundary fuzzy, small target and label defect areas is improved; meanwhile, the method adopts multi-scale path segmentation, adaptive attention fusion and a residual error inverse MLP structure, more efficient multi-level feature representation is realized, and dynamic balance of global and local modeling relations is realized.

The invention relates to a lung focus medical image segmentation method based on graphics and text information and knowledge embedding. The method comprises the following steps: acquiring a lung medical image of a patient and a corresponding clinical diagnosis report; preprocessing the lung medical image to obtain an enhanced image; inputting the lung medical image and the clinical diagnosis report into the medical visual language model to obtain a focus prompt embedding vector; and inputting the lung medical image, the enhanced image and the focus prompt embedding vector into the medical image segmentation model to obtain a lung focus region segmentation image. By adopting the method, the lung focus can be quickly positioned by the segmentation model through the focus prompt embedding vector, the interference of a non-target area is reduced, and the segmentation accuracy and the target concentration are improved.

The invention discloses a medical image segmentation method based on high-resolution modal guidance and cross-modal boundary perception, and the method comprises the steps: carrying out the data preprocessing and enhancement of multi-contrast magnetic resonance imaging data, obtaining a boundary mask through a Canny operator and a Dilatation operation, constructing a multi-modal low-resolution data set, and carrying out the recognition of the multi-modal low-resolution data set; meanwhile, a high-resolution T2f modal data set is reserved, and the data set is divided into a training set, a verification set and a test set; a segmentation model is constructed, and the segmentation model comprises a high-resolution mode-guided double-encoder architecture module, a cross-level attention collaboration mechanism module, and a segmentation branch and boundary prediction branch decoder module; designing a training strategy of joint optimization of boundary contour detection and region segmentation, training the segmentation model by using a training set, and storing optimal model parameters on a verification set; and carrying out model performance verification in the test set, and segmenting a to-be-tested medical image by using the verified segmentation model.

The invention belongs to the technical field of medical image processing, and discloses a CT image segmentation and classification system based on segmentation feature guidance, and the specific technical scheme is as follows: the system adopts a shared encoder to extract general features, realizes collaborative optimization of segmentation and classification through a double decoding path, adopts a partial decoder in a segmentation path, and adopts a partial decoder in the segmentation path; in combination with a local feature attention module, through multi-scale feature fusion and a boundary perception mechanism, the region consistency of a global segmentation map is gradually optimized, edge detail information is supplemented, and a classification path generates a space attention weight through a segmentation feature guide module by utilizing segmentation prediction; the classification network is guided to focus on a focus area and suppress background interference, a self-adaptive loss weighting strategy based on multi-task learning is adopted, the double-task gradient flow is dynamically balanced, and the gradient competition problem in the multi-task learning is effectively relieved.

The invention discloses a CAD drawing engineering quantity automatic identification and calculation method based on a large language model and image segmentation, and the method comprises the steps: extracting project annotation information in a CAD drawing, understanding and reasoning a project introduction through a large language model, and carrying out the structural expression of the project annotation information; the method comprises the following steps: preprocessing a graph in a CAD drawing by utilizing computer vision, analyzing a pipeline drawing of the CAD drawing by utilizing machine learning, identifying the type and position of a component in the pipeline drawing, and realizing semantic segmentation and instance segmentation; and fusing the structured project annotation information, the semantic segmentation result and the instance segmentation result, counting and calculating the project amount of each pipeline component in the CAD drawing image, and outputting a report according to classification. According to the method, OCR recognition, natural language processing, graph semantic recognition, engineering logic calculation and other technologies are integrated, the CAD drawing processing efficiency and the engineering quantity calculation accuracy are greatly improved, and intelligent support is provided for design, construction, drawing examination, budget and other links.

The invention discloses a semi-supervised medical image segmentation method based on uncertainty-driven dynamic correction and multi-scale consistency learning, and the method comprises the steps: carrying out the preprocessing of a medical image data set, and dividing the medical image data set into a training set and a test set; constructing a semi-supervised segmentation model of dynamic correction and multi-scale consistency learning based on uncertainty driving; inputting the training set into a semi-supervised segmentation model, and performing iterative training and parameter optimization to obtain a trained semi-supervised segmentation model; inputting the test set into the trained semi-supervised segmentation model to obtain a medical image segmentation result; wherein the semi-supervised segmentation model adopts a mean teacher model of a V-Net network, a prediction block is added behind each up-sampling block of a V-Net decoder, and a dropout layer is added; according to the method, the problems that the existing semi-supervised learning method is difficult to adapt to the complexity of annotated data and unannotated data distribution, so that effective information is lost; meanwhile, a traditional uncertainty estimation method needs multiple times of forward transmission, and the calculation cost is high.

The invention relates to a medical image segmentation method and system based on residual Mama and multi-scale boundary enhancement. The method comprises the following steps: acquiring and preprocessing a medical image; inputting the image into a segmentation model based on an encoder-decoder architecture; the encoder synchronously extracts local texture features and models long-range spatial dependence through residual error convolution blocks and residual error Mama blocks which are alternately connected; fusing and enhancing the jump connection features between the encoder and the decoder through a boundary enhancement module to optimize boundary characterization; integrating a multi-scale gating attention module in a decoding path, and adaptively selecting and fusing multi-scale context features; and finally outputting the high-precision segmentation mask. The method effectively solves the problems that in the prior art, long-range dependence and local details are difficult to consider, the multi-scale feature fusion capability is insufficient, boundary segmentation is fuzzy and the like, and the segmentation accuracy, the boundary continuity and the clinical practicability are remarkably improved.

The invention provides a medical image segmentation method based on geometry and topology cooperative guidance. The medical image segmentation method comprises the following steps of image preprocessing and data enhancement; a shared encoder; a dual-path cooperative decoder; carrying out multi-mode deformation iterative refining; and a multi-objective composite loss function and an optimization strategy. The method has the beneficial effects that the performance can be remarkably improved: through a unique geometry and topology collaborative refining mechanism, the segmentation precision and the boundary definition are far superior to those in the prior art, the topology correctness of an anatomical structure can be actively maintained and repaired, clinically unacceptable errors are remarkably reduced, and the reliability of a result is improved; in addition, operation can be simplified, stability and generalization are enhanced, and advanced application is promoted.

The invention relates to the field of image recognition, in particular to a wind power construction intelligent safety management method and system based on intelligent AI monitoring. The method comprises the following steps: obtaining an omnibearing real-time image flow of a wind power construction area, carrying out super-resolution deep convolution optimization and operator three-dimensional image segmentation, and extracting an operator three-dimensional image frame; three-dimensional point cloud modeling of the construction area is carried out based on the image flow, real-time image frame position positioning rendering is carried out according to a three-dimensional image frame, and a real-time twinborn model of the construction area is constructed; performing operation dynamic behavior analysis and behavior deviation degree quantitative analysis based on a twin model to obtain the behavior deviation degree of the operator; and according to the behavior deviation degree, carrying out early prediction analysis on illegal behaviors, making an adaptive risk early warning decision, and constructing an operation behavior risk early warning strategy. According to the invention, through real-time operation behavior identification and environmental risk analysis, the intelligence and safety level of wind power construction are improved.

The invention discloses an adaptive mask medical image segmentation method based on a self-supervised mask and deep reinforcement learning, and the method comprises the steps: employing a classic encoder-decoder architecture for a self-supervised mask reconstruction network, fusing a Swin Transform encoder, and carrying out the feature fusion of local image blocks through a self-attention mechanism; according to the self-adaptive mask model, a PPO deep reinforcement learning algorithm is adopted, a strategy network and a value network are constructed, mask actions are dynamically regulated and controlled, reconstruction errors are gradually reduced, a mask strategy is continuously optimized in multiple times of strategy updating for self-adaptive optimization, and high-quality reconstruction of a medical image influenced by missing information is achieved; according to the method, high-quality feature representation can be obtained in an unlabeled data environment, and relatively high precision and accuracy are presented on a public data set.

The invention provides a mechanical arm grabbing method and system based on multi-modal information fusion, and belongs to the technical field of robot intelligent control. Comprising the steps that the conversion relation between a camera coordinate system and a mechanical arm base coordinate system is established through camera calibration, a deep learning neural network is used for conducting grabbing pose estimation on an obtained RGB-D image, and multiple candidate grabbing poses are determined; analyzing a natural language instruction input by a user based on a multi-modal large model, and recognizing a target object region from the RGB-D image by combining a target detection and image segmentation technology; based on the obtained candidate grabbing poses and the target object area, an optimal grabbing pose is screened through a scoring mechanism and mapped to a mechanical arm base coordinate system; and then a dynamic grabbing path is generated by adopting an imitation learning algorithm, and the mechanical arm is controlled to execute grabbing operation. Through multi-modal semantic understanding, accurate grabbing of the mechanical arm in a complex environment can be achieved.

The invention provides a medical image segmentation method based on adaptive anisotropic convolution, and the method comprises the steps: obtaining a three-dimensional medical CT data set comprising images and labels of a plurality of abdominal organs and kidney tumors, and carrying out the preprocessing of the data set; dividing a data set into a training set and a test set for model training and evaluation; designing a three-dimensional medical image segmentation network model based on an adaptive anisotropic convolutional layer, and inputting the preprocessed training set into the three-dimensional medical image segmentation network model, the three-dimensional medical image segmentation network model is trained through parallel multi-modal convolution, adaptive attention weight generation, weighted feature dynamic fusion and multi-stage deep supervision, and model parameters are optimized; and applying the optimized three-dimensional medical image segmentation network model to a test set, generating a three-dimensional segmentation result with clear boundary and complete reserved details, and providing support for clinical diagnosis and treatment planning.

The embodiment of the invention discloses an image annotation method and system applied to brain MRI image segmentation, and the method comprises the steps: obtaining a brain MRI image data set of a target object, and the brain MRI image data set comprises original image sequences of a plurality of scanning levels; performing multi-modal feature fusion processing on the original image sequence to generate an enhanced image feature set; calling a multi-layer cascade segmentation network to perform hierarchical feature extraction on the enhanced image feature set to obtain a multi-scale anatomical structure feature map; and performing region boundary optimization processing based on the multi-scale anatomical structure feature map, and generating a marked brain structure segmentation image. Therefore, the boundary of each structure of the brain can be accurately defined, the segmented image is more accurate and clearer, and the image segmentation and marking of the brain MRI image can be accurately and clearer realized.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

A clinical vertebra image segmentation method for assisting pedicle screw placement surgery, said method comprising: constructing a VerseDiff-UNet end-to-end framework, the framework being integrated with a denoising diffusion probabilistic model (DDPM); combining a noise-added image with a marked mask by using the VerseDiff-UNet framework, and guiding a diffusion direction toward a target region; and introducing a shape priors module on the basis of the DDPM, and extracting structural semantic information from an input spine image. In order to capture specific anatomical prior information in a medical image, the shape priors module is combined and the module effectively extracts the structural semantic information from the input spine image, thereby enabling more accurate anatomical structure segmentation, and facilitating accurate diagnosis and treatment of spinal disorders.

The invention discloses an intelligent manufacturing defect automatic detection and classification method based on machine vision, and particularly relates to the technical field of defect automatic detection and classification, by constructing a high-resolution multi-source sample data set and introducing image preprocessing operation, defect expressions under different manufacturing batches, surface states and illumination conditions are covered, and the defect detection and classification accuracy is improved. Generating a defect probability heat map through an image segmentation network, extracting a primary defect candidate region, calculating a pseudo defect high-frequency interference coefficient by combining a high-frequency pseudo defect feature tensor, and calculating a multi-class defect overlapping coupling coefficient based on multi-classification confidence distribution and semantic adjacency; pseudo defect interference intensity and multi-class defect boundary fuzzy degree in the defect candidate area are accurately described, a sample label pollution risk assessment model is constructed to realize automatic identification and screening of high pollution risk samples in training data, and interference of mistakenly labeled samples on deep model training is significantly reduced; and erosion of error feature-label mapping on the generalization ability of the model is effectively prevented.

The invention relates to the technical field of image segmentation, in particular to a plant salt tolerance response modeling prediction method and system based on a time sequence image. The method comprises the following steps: preprocessing acquired plant sample image data; performing image segmentation on the preprocessed image data by using a plant semantic segmentation model based on U-Net; a plant salt tolerance response prediction model based on TimeSform is constructed; and predicting the plant salt tolerance response grade by using the plant salt tolerance response prediction model. According to the time sequence image-based plant salt tolerance response prediction modeling method provided by the invention, a prediction process integrating image acquisition, preprocessing, dynamic feature extraction and depth time sequence modeling is constructed, so that the efficiency, precision and automation level of plant salt tolerance phenotype recognition are remarkably improved.

The invention provides a point cloud welding seam recognition method combining a 2D segmentation model and spatial features, relates to the field of welding automation, and solves the technical problem of low welding seam recognition precision in the prior art. The method comprises the following steps: acquiring point cloud data of the surface of a to-be-identified workpiece by utilizing three-dimensional laser scanning equipment; preprocessing the point cloud data, and projecting the preprocessed point cloud data to a two-dimensional image to obtain two-dimensional data; performing semantic segmentation on the two-dimensional data by adopting a pre-trained image segmentation model, and identifying a welding seam region; back-projecting the weld joint area to a point cloud space, and extracting spatial features of adjacent planes of the weld joint; calculating an included angle and a distance between the adjacent planes based on the spatial characteristics of the adjacent planes, and judging the welding seam type; and according to the welding seam type and the spatial characteristics of the adjacent planes, calculating to obtain end point coordinates of the welding seam. The device is used in the industrial welding automatic production process.

The invention relates to a medical image segmentation method based on wavelet boundary enhancement and multi-scale perception, and the method comprises the steps: firstly carrying out the preprocessing of an input medical image, including size standardization, intensity normalization and data enhancement; then, inputting the processed image into a deep fusion segmentation network, extracting high-frequency boundary features through wavelet transform and generating a boundary attention map, and capturing global context information in combination with a multi-scale dynamic sparse attention mechanism; and finally, fusing the multi-scale features through a boundary enhancement up-sampling module in a decoder stage, and optimizing a segmentation result by adopting multi-scale supervision and a mixed loss function. According to the method, the boundary precision and the detail retention capability of medical image segmentation are effectively improved, and the segmentation performance under a fuzzy boundary, a multi-scale structure and a complex background is remarkably improved.

The invention relates to the technical field of electronic component detection, in particular to an IC (integrated circuit) carrier plate detection method based on surface state image extraction, which comprises the following steps: acquiring a gray image, analyzing structural parameters, extracting gradient features, detecting boundary disturbance, integrating the image, calculating an abnormal score, identifying a defect position area, extracting features and outputting an identification result. According to the invention, by analyzing the structure parameters of the bonding pad in the gray level image, calculating the edge line segment, the center coordinate and the spacing, and constructing the two-dimensional coordinate system, the regional positioning reference is enabled to have geometric consistency, the coordinate mapping is combined with the gradient direction change frequency and the continuous aggregation point, the boundary disturbance identification precision is improved, and the image division is executed based on the disturbance region. According to the method, non-functional region mixing is effectively avoided, a clustering and probability model is introduced after region gray level statistics, a deviation scoring mechanism is constructed, gray level feature abnormity is accurately recognized, the discrimination capability of small-amplitude and low-contrast defects is improved, and the selectivity and target focusing performance of feature detection are enhanced.

The invention provides a medical image segmentation method based on an AFMHiFormer. The method comprises the steps that firstly, a multiple data enhancement module is provided, and the data distribution diversity is improved while the enhancement stability is guaranteed; secondly, a segmentation model AFHiMFormer is constructed, and the model architecture adopts a double-branch encoder and a multi-scale decoder; thirdly, a feature enhancement module is provided to construct a dynamic complementation mechanism of semantic enhancement and boundary modeling; fourthly, a multi-scale feature fusion module is introduced, multi-scale context information is captured through parallel hole convolution with different expansion rates, and self-adaptive fusion of global and local features is achieved; and fifth, a cross-scale fusion module is designed in the multi-scale decoder, so that the deep layer branch and the shallow layer branch are efficiently fused in a multi-level feature space. According to the method, the advantages of CNN and Transform are combined, dynamic fusion of local and global features is realized by providing a new module, and a remarkable performance advantage is shown in a medical image segmentation task.

The invention discloses a medical image segmentation method based on TransUNet, and belongs to the technical field of medical image segmentation. The method comprises the steps of firstly performing data preprocessing on an original image to obtain preprocessed data; and a DCA attention module is used at a jump joint, so that the problem that a semantic gap exists between characteristics of an encoder and a decoder due to the fact that a simple jump connection scheme is difficult to capture a multi-scale context is solved. The semantic difference leads to redundancy between low-level and high-level features, and finally the segmentation performance is limited. Secondly, a multi-scale boundary sensing module is added to the top layer of the encoder, so that the neural network can better segment the boundary of the target image in the training process; and inputting the preprocessed data into the improved TransUNet model to train the medical image, and outputting an image segmentation result.

The invention discloses a hydraulic engineering dam body crack detection method and system based on machine vision, and relates to the technical field of computer vision. A high-definition camera is used for shooting a dam body image, obtaining a sample data set, extracting feature parameters of different noise types, and constructing a feature database; filtering the current dam body image to obtain a first dam body image, enhancing the crack gray scale difference of the first dam body image, obtaining a second dam body image, segmenting the second dam body image, screening candidate cracks in the crack image, and identifying real cracks according to the edge features of the candidate cracks. According to the method, a feature database is constructed, a targeted dam body image processing flow is combined, a high-definition camera collection and automatic processing flow is used, filtering parameters are dynamically adjusted by constructing the feature database, gray level enhancement and precise segmentation are combined, real cracks are precisely recognized through edge feature analysis and comparison, and quantitative parameters are output; and a reliable basis is provided for dam body safety assessment.

The invention discloses a melanoma lesion area segmentation method based on a CLIP multi-modal fusion network. The method comprises the following steps: 1, constructing an MA-CLIP model; 2, a BLIP language model is finely adjusted through a manually-labeled text-image pair, a large-scale multi-modal data set is constructed, a training set, a test set and a verification set are divided, and preprocessing is carried out; 3, training the MA-CLIP model; 4, evaluating the performance of the MA-CLIP model and optimizing parameters; and 5, inputting a to-be-segmented melanoma clinical image into the trained MA-CLIP model, and outputting a segmentation result. According to the method, the problem of insufficient traditional medical data annotation can be solved, accurate guidance of clinical semantics on image segmentation is realized, and the recognition precision and boundary segmentation capability of a focus in a complex form are improved.

The invention relates to an automatic image segmentation technology based on a convolutional neural network, and is suitable for the field of medical image and industrial detection. In order to solve the problems of rigid feature fusion, insufficient context capture, low efficiency of boundary optimization and poor small target segmentation precision in the existing method, an adaptive multi-scale feature fusion network is constructed: an encoder adopts a progressive expansion strategy and gated attention to intensify cross-scale features; the decoder optimizes hierarchical feature contribution through a dynamic weighted fusion module; the end-to-end boundary optimization is realized by integrating the lightweight differentiable CRF; and designing a composite loss function balance category weight. The segmentation recall rate of the fine structure is obviously improved by more than 18%, the boundary sawtooth rate is reduced by 41%, and the calculation efficiency is improved by 76%.