110results about How to "Improve segmentation" patented technology

The invention provides an auricle anaphora segmentation method and system, and the method comprises the steps: inputting a to-be-segmented human ear image and text description into an auricle anaphora segmentation model which comprises a text coding module, a text-guided visual coding module and visual decoding module, and an angle transformation module; the text coding module obtains a text feature # imgabs0 #; a visual coding module realizes fusion of text features and image features through a structure organized into four stages, a visual encoder in each stage generates visual features # imgabs1 #, a cross-modal sensing module aligns # imgabs2 # and # imgabs3 # to obtain multi-modal features # imgabs4 # and # imgabs5 #, each element in the multi-modal features # imgabs4 # and # imgabs5 # is weighted by an attention gating module to obtain weighted multi-modal features # imgabs6 #, and the weighted multi-modal features # imgabs6 # and # imgabs5 # are weighted by an attention gating module. Adding the elements and # imgabs7 # to generate an enhanced visual feature # imgabs8 #, inputting # imgabs9 # into a visual decoding module to gradually recover the spatial resolution of the image, and further fusing the text and the visual feature to output a multi-scale feature; the angle transformation module carries out angle transformation on the multi-scale features and outputs a segmentation mask of a text description related region. According to the invention, the auricle can be subjected to anaphora segmentation.

The invention discloses a lightweight semantic segmentation network and method for power grid inspection, and the method comprises the steps: employing an encoder-decoder architecture, inputting original data into a lightweight MobileNet v2 backbone network for feature extraction at an encoder part, inputting an obtained feature map into a lightweight large-scale ASPP module, and carrying out the feature extraction; further performing multi-scale feature extraction on the high-dimensional feature map; and in the decoder part, high-dimensional features and low-dimensional features obtained by the backbone network are respectively input into a cross attention wavelet fusion module, after an obtained feature map is fused with the output of a decoder, the feature map is recovered to the size of original data through down-sampling operation by a full-dimensional dynamic convolution post-processing module, and a final prediction mask is obtained. According to the invention, by optimizing a coding and decoding structure, the receptive field of the model and the modeling capability of a narrow and long target are improved, the balance between the precision and the complexity can be effectively realized, and the method is suitable for an unmanned aerial vehicle inspection system with limited resources.

The invention discloses a passive unsupervised domain adaptive medical image segmentation method and device based on pseudo labels, and relates to the technical field of computers, and the method comprises a supervised source domain model training stage, an unsupervised adaptive target domain model training stage and a segmentation stage. The method specifically comprises the following steps of: performing pixel-level self-marking on a source domain medical image by applying pseudo-tag pixel-level uncertainty estimation to generate a reliable pseudo-tag; and on this basis, a strong and weak enhanced teacher-student target domain model is used, and a high-quality pseudo tag is fully utilized to train a target domain medical image, so that the network adapts to representation specific to the target domain medical image. According to the method, unsupervised segmentation is carried out on the target domain data graph on the premise that the source domain model and the target domain unmarked data are known, source domain data do not need to be accessed, the features of the source domain model can be reasonably utilized, and the problem existing in SFUDA can be well relieved; and the accurate segmentation capability on the target domain is still maintained under the condition that less data is available compared with the UDA method.

The invention belongs to the technical field of image recognition, and discloses a few-sample defect detection method based on a prototype prompt fine-tuning visual basic model SAM. Prototype prior information about an image to be segmented is extracted from a small number of example image-mask pairs, so that prior guidance of human beings is replaced; for defect data, a representative foreground prompt point, a high-confidence background prompt point (sparse prompt) and a foreground Mask (dense prompt) are extracted from prototype prior to serve as prototype prompt, and a visual basic model SAM is guided to achieve more accurate segmentation. A visual basic model SAM is finely adjusted by using prototype prompt and a small amount of defect data, a prompt encoder and an image encoder of the SAM are frozen, an LoRA bypass is injected into the image encoder, and the SAM is enabled to better adapt to defect data distribution by updating parameters of the LoRA bypass and a lightweight mask decoder.

The invention discloses a multi-mode unmanned aerial vehicle image segmentation method. The method comprises the steps of 1, constructing an unmanned aerial vehicle image semantic segmentation base network; 2, capturing high-resolution local features in a shallow layer through a convolutional neural network (CNN) module, extracting from deep features of the image through a multi-head self-attention mechanism in a transformer module, and fusing the local features with deep global features through jump connection; 3, constructing a parallel convolution module, and optimizing the features fused in the step 2; 4, combining feature extraction results in the step 2 and the step 3, fusing feature information through a cross attention mechanism, and improving a segmentation effect in a complex scene; and 5, constructing a double attention module, and optimizing local and global information processing of the image. The method has the characteristics that the global and local features are efficiently fused, the multi-scale target is accurately segmented, and the robustness in a complex scene is remarkably improved.

The invention discloses a cross-model knowledge distillation method for a remote sensing semantic segmentation task, and relates to a cross-model knowledge distillation method. The method aims at solving the problems that an existing remote sensing image semantic segmentation model is large in parameter quantity and large in resource consumption. The method comprises the steps of selecting a student model and a teacher model which are complementary in ability, selecting a data set to train the teacher model, and using a feature map output by the trained teacher model as knowledge to be input into the student model; carrying out attention weighting on the feature map input into the student model according to channels, and calculating loss; the knowledge transmitted by the teacher model is decoupled into a target area and a non-target area, the two independent target areas are subjected to prediction and knowledge distillation, and loss is calculated; and carrying out weighted summation on the loss of the semantic segmentation of the student model and the loss of the above steps, calculating the total loss, updating the parameters of the student model through the total loss, and finally completing the training of the student model. The invention belongs to the technical field of remote sensing image processing.

The invention discloses a blood vessel segmentation method and device based on large model fine tuning, equipment and a medium, and the method comprises the steps: obtaining a blood vessel image, carrying out the preprocessing of the obtained blood vessel image, and obtaining a training set; performing fine tuning on a pre-trained image segmentation model by adopting the training set, so that the image segmentation model learns related features of blood vessel segmentation; wherein the low-rank adaptive fine tuning technology is adopted to perform fine tuning on the image segmentation model: two parallel low-rank matrixes are inserted into a Transform layer in the image encoder, the input is processed by the two low-rank matrixes in sequence, and the processing result and the original output are added to obtain the final output. According to the method, the defects of a large model in a blood vessel segmentation task are overcome through efficient parameter fine tuning and a multi-scale feature fusion module. Accurate blood vessel segmentation can be realized under limited annotation data, the difficulty and cost of medical image segmentation tasks are greatly reduced, and the method can be widely applied to the technical field of medical image processing.

The invention provides a pavement crack detection method and system. The method comprises the following steps: acquiring a plurality of pavement crack images; a Swinin-Unet model is constructed; determining the total loss of the Swinin-Unet model by using the plurality of pavement crack images; using the total loss of the Swinin-Unet model to train the Swinin-Unet model, and using the trained Swinin-Unet model as a pavement crack detection model; and inputting a to-be-detected pavement crack image into the pavement crack detection model to obtain a pavement crack detection result output by the pavement crack detection model. According to the method, the comprehensive capture capability on the global and local features of the crack and the segmentation effect on crack details are improved.

The embodiment of the invention discloses an image segmentation method and device for a small-scale component, equipment and a storage medium. The method comprises the steps of obtaining a to-be-segmented image with a to-be-segmented small-scale component; identifying and positioning a small-scale component in the to-be-segmented image by using a multi-scale analysis technology to obtain a target image area of the small-scale component; performing boundary optimization processing on the target image area by using a preset adaptive threshold adjustment mechanism to obtain a preliminary image segmentation result; correcting the preliminary image segmentation result by using pre-collected priori knowledge and context information of the small-scale component to obtain a corrected image segmentation result; performing morphological operation processing on the corrected image segmentation result to obtain an optimized image segmentation result; and re-projecting the optimized image segmentation result into the original image coordinate system to generate a final image segmentation result of the small-scale component. Through the above mode, the precision and reliability of image segmentation of small-scale components are improved.

The invention discloses an ironmaking product quality evaluation method and device based on multi-source data, and relates to the field of image processing, and the method comprises the steps: inputting a collected feature map of a to-be-evaluated ironmaking product into an encoder, obtaining a plurality of intermediate coding features and target coding features, inputting the plurality of intermediate coding features into a multi-scale feature fusion module in a jumping connection manner to obtain a plurality of fusion coding features, and inputting the target coding features into a parallel information focusing module to obtain parallel information focusing features, connecting the plurality of fused coding features in a jumping manner, inputting the fused coding features and parallel information focusing features into a decoder to obtain a plurality of candidate product features, analyzing according to the plurality of candidate product features to obtain an output image of the ironmaking product to be evaluated, obtaining a wrinkle smooth area ratio in the output image, and deciding the smooth quality of the ironmaking product to be evaluated. And performing flaw evaluation statistics on a plurality of continuous smooth qualities acquired by the video to obtain the quality flaw evaluation of the ironmaking product. The segmentation performance is improved.

The invention discloses a cardiac ultrasound image segmentation method based on a UCT-UNet model, and relates to the technical field of image segmentation, a Transform module is fused on a unit layer of an encoder, and detail information is captured by using global characteristics of Transform and local characteristic advantages of convolution. In the decoder stage, a Transform block (UCTBlock) is guided through uncertainty to focus on uncertainty estimation to guide a model to pay attention to an area which is difficult to accurately segment by the CNN, so that a global dependency relationship is established, and functional overlapping of local features and global information is reduced. The method effectively avoids the wrong segmentation of the complex structure by the model, improves the feature fusion, and improves the segmentation accuracy. Besides, by adding a feature extraction module FEM in each jump connection, the feature extraction capability is enhanced, the output quality of the model is further improved, and finally the segmentation performance of the cardiac ultrasound image is improved.

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing ground feature classification extraction network and drawing method in a complex scene, and the method comprises the following steps: S1, obtaining high-resolution satellite remote sensing image data, carrying out the preprocessing of an image, and generating a multi-scene ground feature data set; s2, constructing a two-layer U-shaped network comprising an encoder and a decoder, wherein the two-layer U-shaped network is used for multi-scale feature extraction and ground feature boundary segmentation; s3, inputting the preprocessed remote sensing image into a two-layer U-shaped network, and carrying out ground feature classification and segmentation; s4, performing splicing processing on the segmentation result, and generating a ground feature type spatial distribution map; and S5, performing noise elimination and post-processing operation on the generated distribution map. The multi-scale features are selectively extracted through the feature selection module (FSM), the attention of the network to small target ground features can be enhanced, and the segmentation precision of the small-scale ground features is remarkably improved by selecting the features containing important details.

The invention discloses a global and local feature fusion-based semi-supervised video target segmentation method and a global and local feature fusion-based semi-supervised video target segmentation system. According to the implementation scheme, the method comprises the following steps: firstly, acquiring required data sets including image and video data sets and corresponding segmentation labels, expanding the image data set, and sampling the video data set to obtain a training data set for training a segmentation model; and secondly, designing a segmentation model, constructing a loss function to train the segmentation model, and finally performing video target segmentation by using the trained segmentation model. According to the invention, a local-global branch fusion module is designed to carry out global context modeling and local detail enhancement on features used for non-local matching, so that the distinguishing capability of the model for distinguishing similar features is improved; therefore, the network can effectively solve the problem of wrong feature matching caused by similar interferents in a semi-supervised video target segmentation task. On this basis, a target scale self-adaptive down-sampling layer is further combined, and information of the same target under different scales is captured through the layer, so that the self-attention calculation complexity is reduced while the feature distinguishability is improved, and the balance between the model segmentation performance and the reasoning prediction efficiency is realized.

The invention discloses a thrown object detection method and system based on a traditional algorithm and deep learning. The method comprises the following steps: sequentially carrying out background filtering, category labeling, data enhancement, rasterizing operation and rasterizing partitioning, ground filtering and point cloud compensation on an original point cloud set to obtain a post-processing point cloud set; performing model training by taking the post-processing point cloud set as a training sample, and introducing a terrain clearance factor to obtain a target point cloud segmentation network model; performing background filtering, ground filtering and point cloud compensation on the to-be-detected point cloud in sequence to obtain to-be-detected post-processing point cloud; inputting the post-processing point cloud to be detected into the target point cloud segmentation network model for prediction to obtain the category of each point; and extracting the points of which the category is the throwing object, and calculating an external frame of the points to obtain the center position and the length, width and height size information of the throwing object. The method provided by the invention can accurately detect the small target thrown object, and improves the detection precision while ensuring the detection efficiency of the thrown object.

The invention relates to a skin lesion segmentation method based on error positioning and adaptive optimization, and the method comprises the steps: dividing a skin lesion image shot by a dermatoscope and a corresponding segmentation label into a training set, a verification set and a test set; then constructing a segmentation network, training the constructed segmentation network by adopting a training set, continuously monitoring the generalization ability of the segmentation network through a verification set in the training process, and finally obtaining a trained segmentation network; and finally, inputting the test set into the trained segmentation network, and outputting a segmentation result of the skin lesion. The skin lesion segmentation method based on error localization and adaptive optimization effectively focuses on a key area needing to be optimized in actual training, and helps the model to more accurately understand and distinguish the features of lesion and normal skin. Therefore, the segmentation capability of the method in processing complex lesions such as fuzzy boundaries, complex shapes and background noise is improved.

The invention belongs to the technical field of remote sensing image segmentation, and provides a remote sensing image segmentation method and system based on edge auxiliary information. First, a remote sensing image is acquired, and a plurality of local images are obtained after image preprocessing; then a remote sensing image segmentation model is used to predict the local image to obtain a prediction result that each pixel belongs to a class; wherein the remote sensing image segmentation model comprises an encoder and a decoder; the encoder adopts a residual network to obtain a shallow feature map and a plurality of deep feature maps; the decoder is based on the shallow feature map and the plurality of deep feature maps, and obtains an edge feature map and a main feature map through upsampling, splicing and difference operation, and fuses the edge feature map with the main feature map to obtain a final feature map; the segmentation effect of the edge in the remote sensing image is improved, and the accuracy of remote sensing image segmentation is improved.

The invention discloses a cigarette micropore target detection method and system, and belongs to the technical field of cigarette quality detection, and the method comprises the steps: carrying out the three-dimensional point cloud data collection of cigarettes of a plurality of brands, carrying out the filtering processing of collected cigarette punching original point cloud images, and removing useless background information; fusing feature information of different levels of cigarette punching original point cloud images, introducing a data adjustment function to fully train hole data in the feature information, and obtaining an optimized PointNet + + deep network model; and extracting point cloud data of the cigarette punching area, segmenting the whole predicted point cloud image according to the feature information of the distance between points, mapping the connection and edge contour of all points of the single hole into a two-dimensional image, and obtaining the maximum depth value of the to-be-detected hole. According to the method, the perforation segmentation and perforation depth calculation of cigarettes of various brands are realized, and a more efficient, convenient and reliable detection scheme is provided for cigarette production.

The invention provides a plug abnormity detection method and a detection system, which are used for detecting falling and surface flaws of a plug during operation. The method comprises the following steps: firstly, preprocessing original point cloud data to obtain preprocessed point cloud data, and segmenting the preprocessed point cloud data to obtain header data; and then plug falling detection is carried out, if the plug does not fall, plug surface flaw detection is carried out, and if the plug falls, the steps of data preprocessing and plug falling detection are repeated.

The invention relates to the technical field of image processing, in particular to a training method of a semantic segmentation model and a semantic segmentation method and device. Comprising the following steps: acquiring and preprocessing tree-containing images, and constructing an image set; carrying out image segmentation based on a U-Net architecture, wherein the U-Net architecture comprises an encoder, a decoder and a jump connection; based on Dice coefficient loss and weighted cross entropy loss, determining a total loss function to optimize image background and tree category imbalance; the learning rate is adjusted in each training step through an Adam optimizer in combination with gradient first-order moment estimation and gradient second-order moment estimation, and learning rate attenuation adjustment of the Adam optimizer is achieved in combination with cosine annealing scheduling; and training the semantic segmentation model based on the configured Adam optimizer and cosine annealing scheduling until the semantic segmentation model is obtained through convergence. Therefore, based on the obtained semantic segmentation model, subsequent image segmentation processing application in an education robot can be facilitated.

The invention discloses a path planning method and system based on multi-modal joint integration, and the method employs an image processing and deep learning technology to automatically recognize and eliminate the influence of a dynamic object, and guarantees that SLAM and VIO systems are only matched with a stable static background object. According to the technical scheme, firstly, feature points in an input image are preliminarily screened and matched through the image feature point matching technology; and then introducing a deep learning network to carry out refined segmentation and instance identification. The deep learning network can accurately distinguish a static object from a dynamic object, and accurately identify the existence of the dynamic object in a complex scene. After the dynamic objects are identified, the dynamic objects are eliminated from the feature matching process by adopting an intelligent elimination strategy, so that the SLAM and VIO systems are ensured to perform feature matching and positioning calculation only based on stable static background objects, the negative influence of the dynamic objects on the positioning precision is avoided, and the stability and applicability of the system in a complex dynamic environment are improved.

The embodiment of the invention provides a human body image segmentation method and device, electronic equipment and a storage medium, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a target video frame sequence containing a target object; wherein the target video frame sequence has a timestamp; classifying the target video frame sequence based on the timestamp to obtain a reference video frame and a target video frame; wherein the timestamp of the reference video frame is before the timestamp of the target video frame; generating a segmentation mask according to the reference video frame to obtain reference mask data; performing image segmentation mask prediction on the target video frame based on a preset human body segmentation mask prediction model, the reference video frame and the reference mask data to obtain target mask data; and performing human body image segmentation on the target video frame according to the target mask data. According to the embodiment of the invention, the human body image segmentation precision of the video frame sequence can be improved.

The invention discloses an innovative apple grading evaluation method, and relates to the technical field of apple grading evaluation, and the method specifically comprises the following steps: S1, providing a web crawler and manual collection mode to collect a high-quality image for an apple stem detection task; s2, proposing an FDNet lightweight fruit stem detection algorithm for fruit stem detection and fruit body auxiliary positioning; s3, for apple feature extraction, two algorithm structures of MBFilter and DPC-AKNN are provided; and S4, aiming at the apple grading model, adopting a GBDT algorithm. According to the invention, a high-precision lightweight fruit stem detection algorithm FDNet-p is provided by combining a computer vision technology with a current mainstream model compression technology, and superior performance of the algorithm is proved through a large number of experiments. In the apple grading process, the FDNet-p algorithm is preliminarily used for screening out apples without fruit stems, the idea of auxiliary positioning is put forward, camera collection operation is prevented from being triggered in advance or lagging, then the image processing technology is used for extracting the characteristics such as the apple color, the apple shape and the apple diameter, and finally the GBDT algorithm is used for completing the intelligent grading model design of the apples. And intelligent grading of apples is realized.

The invention discloses a depth camera and a sweeping robot, and the camera comprises a projector which is used for projecting speckles and floodlight; the receiver is used for receiving reflection signals of the speckles and generating a speckle pattern; receiving a reflection signal of the floodlight, and generating an IR image; the RGB camera is used for obtaining an RGB image; and the processor is used for generating a depth map according to the speckle pattern or the IR image, determining a ground area according to the RGB image and / or the depth map, fusing the RGB image, the IR image and the speckle pattern corresponding to the ground area, and performing detection through a smudginess segmentation algorithm to obtain a smudginess range. According to the invention, the degree of liquid and solid smudginess can be effectively identified, and accurate segmentation and 3D modeling are carried out on the liquid and solid smudginess.

The invention provides a medical image segmentation method and device, electronic equipment and a readable storage medium, and belongs to the technical field of image processing, and the method comprises the steps: carrying out the multi-scale local feature extraction and global feature extraction of a medical image, and obtaining a first local feature map and a first global feature map corresponding to each scale; performing feature fusion on the first local feature map and the first global feature map corresponding to each scale to obtain a first fusion feature map corresponding to each scale; fusing the first fusion feature maps corresponding to the scales to obtain a second fusion feature map; and decoding the second fusion feature map to obtain an image segmentation result. According to the medical image segmentation method and device, the electronic equipment and the readable storage medium provided by the invention, the segmentation precision of the medical image can be improved.

The invention provides a fine-grained segmentation method for extremely small defects in a welding seam of a spiral steel pipe, and relates to the technical field of nondestructive testing based on machine vision. According to the method, a fine-grained minimum defect segmentation network model is constructed under the condition that minimum defects in a spiral steel pipe welding seam are difficult to distinguish in a static scene, the model comprises a feature extraction network, a semantic discrimination encoder, a comparison conversion mechanism, a progressive iterative decoding strategy and a plurality of convolution modules, and a semantic discrimination system structure is designed. Features are self-adaptively initialized with semantics as guidance, and defect scale differences are relieved; introducing a comparison conversion mechanism to dynamically optimize defect features; and finally, performing fine-grained pixel-level segmentation on the fused feature map by adopting a progressive iterative decoding strategy. According to the method, a good segmentation effect can still be achieved in adverse scenes such as unbalanced sample category distribution, dense minimum defect distribution, pixel redundancy and background interference, and the segmentation performance of the network is effectively improved.

The application discloses a kind of modal missing medical image segmentation method, device, terminal and storage medium, belong to medical image processing technical field, including obtaining three-dimensional medical image, the three-dimensional medical image regularization obtains the three-dimensional medical image after regularization;The three-dimensional medical image after regularization is extracted and integrated by encoder to obtain high-dimensional image feature output data;The high-dimensional image feature output data is further enriched by multistage knowledge distillation method and obtains high-dimensional image feature;The further enriched high-dimensional image feature is input into segmentation decoder to obtain final segmentation result.The application extracts more potential information from single mode input, finally improves the segmentation performance in single mode situation, and obtains high-quality segmentation result.

This invention relates to a semi-supervised segmentation method for thyroid nodules based on morphological progressive pseudo-supervised refinement. Addressing issues such as blurred boundaries, background interference, and low pseudo-label quality in semi-supervised learning within ultrasound images, this invention proposes a plug-and-play semi-supervised segmentation module. This module progressively filters blurred boundary responses, recovers reliable foreground regions, and introduces morphologically-aware geometric constraints to systematically refine noisy pseudo-supervised signals. The semi-supervised segmentation module constructed by this method includes: a boundary and uncertainty curriculum module to suppress blurred boundary responses; a dynamic region expansion module to recover weak but reliable foreground regions under spatial constraints; and a gradient-driven geometric refinement module to further standardize boundary quality and structural consistency through topology-aware feedback and differentiable geometric refinement. Through the collaboration of these three modules, the semi-supervised segmentation method gradually transforms noisy pseudo-labels into more reliable and structurally sound supervised signals.

The application relates to the field of sewage pumps, in particular to a novel flow control submersible sewage pump, which comprises a pump body, a rotating shaft arranged in the pump body and an impeller fixedly installed on the rotating shaft, and the bottom end of the pump body is fixedly connected with a sealing cylinder; the bottom end of the sealing cylinder is provided with a screening and dividing assembly, the screening and dividing assembly is used for screening and preliminary dividing of garbage in sewage, and the screening and dividing assembly comprises a screening cylinder fixedly connected with the bottom end of the sealing cylinder, and a dividing cutter capable of moving up and down to cut is arranged in the screening cylinder. The screening and dividing assembly is arranged, so that when the sewage pump is used, the screening cylinder can screen garbage, garbage with a larger size cannot enter the screening cylinder, garbage that can enter the screening cylinder can stay in the screening cylinder for a short time, the garbage is preliminarily divided by the rotating dividing cutter, the size of the garbage is reduced, the pressure of subsequent crushing treatment of the garbage is reduced, and the garbage is conveniently discharged subsequently.

This application provides an open-vocabulary remote sensing image semantic segmentation method and system. The method acquires remote sensing images and learnable text, and obtains image-encoded features and text-encoded features through encoding operations. Low-frequency components are extracted from the image-encoded features, and structured noise is injected into these features to obtain low-frequency enhancement features. A similarity cost map is generated by calculating the similarity between the text-encoded features and the low-frequency enhancement features, and a semantic score is formed by aggregating these similarity cost maps. Detail-guided features are acquired, and the semantic score and detail-guided features are combined to output the segmentation result through image decoding. This method can analyze the low-frequency dominance of remote sensing images based on their frequency composition and enhance discriminative ability in the frequency domain through noise injection, thereby improving the segmentation performance of remote sensing images under open-vocabulary settings and increasing the recognition efficiency of remote sensing images.

The invention discloses a head and neck tumor image segmentation method based on combined analysis of CT and PET, and the method comprises the steps: obtaining a head and neck tumor data set, and carrying out the preprocessing of the head and neck tumor data set; constructing a parallel common attention mechanism module; an improved UNet3D network model is constructed; optimizing the improved UNet3D network model by adopting a combined loss function; and inputting the preprocessed head and neck tumor data set into the optimized and improved UNet3D network model, and outputting a segmentation task result of the bimodal image. According to the method, a parallel common attention mechanism and a multi-scale feature fusion technology are introduced, multi-modal image information is efficiently combined, features of the tumor under different scales are captured, accurate segmentation is carried out on the features, the precision and efficiency of head and neck tumor segmentation are remarkably improved, the limitation that a single-modal image is difficult to comprehensively reflect the tumor features is overcome, and the accuracy and efficiency of head and neck tumor segmentation are improved. And the problem of scale diversity of the tumor in the image is solved.