1113 results about "Contrast level" patented technology

The invention belongs to the field of computer vision and industrial defect detection, and discloses a steel coil end face defect detection method and system based on CBAM-BiFPN and multi-loss optimization, and the method comprises the steps: image preprocessing and region extraction: extracting a steel coil end face region through OpenCV and other image processing methods; dividing the high-resolution image into a plurality of small blocks and performing data enhancement; the method comprises the following steps: constructing a YOLOv11 network based on CBAM-BiFPN, introducing a CBAM attention mechanism and BiFPN feature fusion structure, and constructing an improved YOLOv11 detection network; multi-loss function joint optimization training is carried out, and model training is carried out in combination with loss functions such as Focal Loss and WIoU; and fusion of detection results and defect reconstruction output: reconstructing original image defects of all tile image detection results in a space coordinate mapping and redundant region fusion mode, and realizing high-precision overall detection output. Compared with a traditional method, the method still has the high recognition capability in a complex background and low-contrast scene, the detection precision and stability are obviously improved, and higher industrial adaptability and practical value are achieved.

The invention discloses a multi-modal fusion defect detection method and system, and belongs to the technical field of intelligent detection and machine vision, and the system comprises a visible light sensor, a thermal infrared sensor, a hyperspectral sensor, a multi-band light source trigger control system, a modal preprocessing and alignment module, a cross-modal feature fusion module, and a defect detection and output module. According to the invention, three sensors are used to construct a multi-modal visual perception system, and a multi-band light source triggers a control system to complete image acquisition; after multi-modal information is subjected to preprocessing and cross-modal alignment through the modal preprocessing and alignment module, multi-modal fusion is achieved through the cross-modal attention module and the multi-scale feature fusion pyramid structure, and finally defect recognition and output are conducted through the defect detection and output module. According to the method, the defect identification precision can be obviously improved, and the method has obvious advantages especially for low-contrast and early-stage hidden crack defects, and has good expandability and deployment suitability at the same time.

The invention relates to an improved YOLO11n underwater target identification and detection method based on local and global perception, and belongs to the field of underwater intelligent identification. According to the method, a local bottleneck module is constructed to extract local fine-grained features, a multi-scale large convolution kernel module is introduced to expand a receptive field, and a multi-branch channel attention module, a space and channel combination grouping attention module and a cross-dimension feature fusion module are combined to adaptively enhance effective features and suppress environmental noise interference. And a local-global bottleneck module is further constructed, and local detail and global semantic collaborative modeling is realized through a cascade structure. And a Bottleneck module of the original YOLO11n is replaced by the module, so that an improved model is formed. After training, the method has higher detection precision and robustness on underwater data sets such as DUO, RUOD and the like, and is suitable for real-time target identification tasks in a complex underwater environment. According to the method, the problems of color distortion, low contrast, fuzzy details and difficulty in multi-scale target feature extraction of the underwater image are solved, and high-precision and real-time balance detection is realized.

The invention discloses a liquid medicine foreign matter detection method and system based on machine vision, and the method comprises the steps: obtaining multiband spectral data of a liquid medicine sample, and carrying out the normalization processing to generate a liquid medicine background standard spectral feature template; comparing the refractive index spectral characteristics of the template and the foreign matter-containing liquid medicine, and recording the surface roughness scattering mode and polarized light reflection angle change data of a foreign matter area; calculating a spectrum peak displacement quantized value according to the polarized light data, and extracting foreign matter edge spectrum gradient information in combination with a threshold value; calculating a transparency spectral attenuation coefficient based on the edge gradient information, and obtaining spectral contrast enhancement factors of different foreign matters in combination with the spectral response characteristic data; and if the enhancement factor does not reach the standard, adjusting the spectrum correction parameter and recalculating, fusing the scattering mode and the multi-angle incident spectrum response result to carry out consistency verification, and obtaining a final identification result containing the foreign matter material type and the danger level. The method can improve the drug detection accuracy and guarantee the drug quality safety.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention provides a medical image segmentation method and system based on spatial detail enhanced vision, and relates to the technical field of image processing. The method comprises the following steps: performing initial feature mapping on an input and preprocessed medical image to obtain an embedded feature map; the embedded feature map is input into an encoder for feature extraction, and multi-scale features are obtained; the encoder comprises a plurality of encoding stages, and the number of channels is doubled and the spatial resolution is halved through down-sampling operation between the encoding stages; the multi-scale features are subjected to up-sampling and spatial enhancement reconstruction step by step through a decoder, and a high-precision segmentation result is generated; wherein the decoder comprises a plurality of decoding stages, the decoding stages correspond to the encoding stages, and feature fusion is carried out between the corresponding stages of the encoder and the decoder through jump connection. According to the method, the boundary description precision and the segmentation robustness of the low-contrast image can be improved without increasing the linear complexity, and the method is suitable for medical image segmentation scenes of skin lesions, gastrointestinal polyps and the like.

The invention discloses a visual tracking method for a target aircraft target to identify a high-dynamic target, and relates to the technical field of dynamic visual tracking. According to the method, multi-source information of a visual frame stream and an event stream is fused, and the definition and stability of an initial envelope are ensured through the steps of infrared enhanced filtering, local contrast reserved filtering, multi-scale ROI screening, unified perspective correction and the like; in the trajectory generation and paragraph division process, two-norm fusion analysis is performed according to high-dynamic target maneuvering characteristics, trajectory mutation can be caught acutely, and the rationality of trajectory paragraph division is improved; a cross-level backtracking compensation mechanism further corrects envelope deviation caused by visual delay or jitter in high-speed motion, breakage and drifting of a target trajectory are effectively reduced, the accuracy of trajectory attribution can be kept under the condition of multi-target overlapping, target confusion is avoided, and layered early warning of target aircraft off-target behaviors is achieved.

The invention relates to the technical field of optical localization, in particular to an optical localization feedback control system in a minimally invasive surgery of bile adipoma. The system comprises the following steps: an image optimization module is used for scanning a full-band optical image in an operation cavity, performing adaptive contrast optimization and constructing a spectrum optimization image; the focus positioning module is used for performing accurate focus positioning on the spectrum optimization image and extracting a focus optical contour; the three-dimensional reconstruction module is used for performing structural feature point identification and three-dimensional reconstruction on the spectrum optimization image to construct a three-dimensional structure model; and the visual error compensation module is used for performing multi-angle optical ranging and stereoscopic visual error compensation on the optical contour of the focus to obtain a three-dimensional compensation positioning coordinate. According to the invention, through real-time accurate position positioning, the instrument response speed and the tissue identification precision are improved.

The invention discloses a wafer probe mark accurate detection method based on deep learning, and belongs to the field of wafer probe mark detection, and the method comprises the steps: constructing a pin mark image denoising preprocessing network, employing a small target feature protection and enhancement strategy based on HSV color space and local contrast joint adjustment, and carrying out the recognition of a small target feature; denoising and contrast optimization are carried out on the needle mark image; a multi-scene training sample is generated through mosaic splicing and mix fusion; a dense small target enhancement module is introduced into the backbone network to enhance needle mark feature expression, and a multi-scale feature fusion module is arranged in the neck network to extract full-scale features; and establishing an anchor frame optimization system adaptive to the minimum needle mark target, and adopting an optimizer and learning rate collaborative optimization training strategy to realize model adaptive convergence. According to the method, high-precision detection and robust identification of the wafer probe mark can be realized under a complex background, and the detection accuracy and stability are remarkably improved.

The invention discloses a multi-modal dynamic compensation road disease intelligent detection and risk assessment system, and relates to the technical field of artificial intelligence and computer vision, and the system comprises an image collection module which is used for obtaining a road surface image in real time through a camera device, and transmitting the image to a preprocessing module; the preprocessing module is electrically connected with the image acquisition module and is used for carrying out graying, noise reduction, contrast enhancement and geometric correction operation on the image and outputting a standardized image; the feature extraction module is electrically connected with the preprocessing module. According to the road disease detection system provided by the invention, by integrating a plurality of modules, high efficiency and intelligence of road disease detection are realized, compared with traditional manual inspection, the system not only improves the detection efficiency, but also remarkably enhances the objectivity and accuracy of detection, and is particularly suitable for real-time monitoring requirements of a large-scale road network; the image acquisition quality is effectively improved, and the effectiveness of feature extraction can be ensured.

The invention discloses a camouflage target detection method and system based on a double-domain fusion enhanced network, and relates to the technical field of target detection. Through the nonlinear double-domain fusion module, in combination with nonlinear mapping of a spatial domain and a frequency domain, key difference characteristics of a frequency domain amplitude spectrum and a phase spectrum are captured, the problem that the detection performance is reduced in a scene of low contrast and the like depending on an RGB spatial domain is solved, and the target discrimination degree is improved; based on a lightweight scale perception modulation converter and a double-feature fusion module, multi-scale features are extracted, aligned and fused, a semantic relation is integrated by means of cross attention, and the problems of detail loss and boundary fuzziness caused by scale diversity are solved; the context feature enhancement module integrates cross attention and edge auxiliary injection, accumulates multi-layer feature integration, gives consideration to a global boundary and a local structure, effectively reduces false detection, missing detection and edge roughness, and further enhances robustness through multi-layer auxiliary supervision.

The invention provides a cultural relic crack monitoring system and method based on image processing, and relates to the technical field of image processing. The system is composed of a multispectral image acquisition module, an environment calibration module, an image processing module, a contour recognition module, a crack recognition module, a time sequence evolution analysis module and an output module. The calibration module performs reflectivity normalization according to the material spectrum and real-time illumination and inhibits specular reflection; the image processing module uses multi-scale Retinex to enhance the crack contrast; the contour recognition module is used for extracting candidate contours by using improved Canny; the crack identification module introduces a lightweight CNN to carry out texture analysis and generates crack and texture data; the time sequence evolution analysis module completes multi-moment crack registration and morphological parameter difference; and the output module summarizes the data to generate a chart and a monitoring report. The false detection rate of a traditional method is reduced.

The invention discloses a reticulocyte recognition and grading system based on a blood smear, and particularly relates to the field of medical cell morphology examination, and the system comprises an image acquisition module, a staining normalization module, an instance segmentation module, an erythrocyte classification module, a reticulocyte grading module and a statistics output module. The image acquisition module adopts an automatic microscope platform to continuously scan the blood smear under a 100-time visual field, acquires a high-quality bright field channel image through an automatic focusing technology, and monitors image definition, contrast and illumination uniformity; the statistical output module is used for calculating the proportion and bluish violet intensity statistics of reticulocytes of each level, and generating a visual quality control result and a diagnostic report; according to the invention, full-automatic processing from samples to reports is realized, interpretability and high classification precision are both considered, subjective difference and omission ratio of manual microscopic examination are significantly reduced, and efficiency and consistency of clinical detection are improved.

The invention relates to the technical field of image acquisition and testing, and discloses an image acquisition and testing method, device and equipment and a storage medium, and the method comprises the steps: collecting a standard test card image through an image acquisition card in a multi-stage standard illumination environment, and calculating sensitivity benchmark test data; executing time domain and space domain combined sampling processing to obtain super-sensitivity image data; performing brightness analysis and dynamic adjustment on the currently shot first scene image to obtain a first image acquisition parameter; acquiring a second scene image, and performing image grid division and contrast compensation on the second scene image to obtain an enhanced image after contrast compensation; according to the super-sensitivity image acquisition method and the super-sensitivity image acquisition device, the sensitivity limitation of the position depth of a traditional image acquisition card is broken through, and super-sensitivity image acquisition is realized.

The invention belongs to the technical field of image enhancement and recognition, and particularly relates to a radar image intelligent enhancement and recognition method and system based on multi-model fusion, and the method comprises the steps: carrying out the adaptive suppression of speckle noise of an original radar image; feature point detection is carried out, robust transformation matrix estimation and adaptive contrast enhancement are carried out, and a corrected and enhanced image is output; utilizing the generative adversarial network and multi-loss function collaborative constraint to obtain a texture reconstruction image; establishing an image-semantic double-flow network architecture, performing cross-modal attention fusion to obtain a fusion feature map, and outputting a target recognition result; performing time phase division on the texture reconstruction image, judging a change type, and outputting a change detection result; and outputting a processing report including the enhanced image, the target list and change analysis. According to the method, noise suppression, correction enhancement, texture reconstruction, target recognition and change detection are integrated, the defect of fragmentation processing in the traditional technology is overcome, and the overall processing performance and the actual application adaptability are improved.

The invention discloses a Mars transverse wind ridging few-sample remote sensing interpretation method and system based on an SAM model. According to the method, contrast stretching preprocessing is carried out on an original Mars image, a dual-branch feature fusion framework based on VIT and CNN is constructed to extract and fuse image features, a position coding generator is introduced to support input of any size, a fine-tuning SAM strategy of a selective freezing encoder and a trainable mask decoder is adopted, and LoRA low-rank adaptation optimization calculation is combined, so that the Mars image is obtained. And a double-branch prompt generation module is used for fusing labeled and unlabeled data to generate a high-precision prompt, and finally, an interpretation result is output through a mask decoder and connected component analysis is carried out, so that instance-level labeling is realized. The system comprises an image preprocessing module, a double-branch feature extraction and fusion module, an image embedding generation module, a model fine tuning module, a prompt embedding generation module and an interpretation module. According to the method, the recognition precision and robustness of the mars transverse wind ridge formation under the condition of few samples are effectively improved.

The invention provides a facial skin flaw enhancement method based on a Lab color space. The method comprises the following steps: firstly, acquiring an RGB face image and converting the RGB face image into a CIE Lab color space with uniform perception; then, performing differentiation treatment according to the manually selected skin flaw type: for the vascular flaw, extracting statistical characteristics of a component and driving adaptive nonlinear transformation, and generating a grey-scale map which highlights the red flaw; for pigment flaws, nonlinear transformation is carried out on the component L, and then collaborative linear weighting and feature amplification are carried out on the component L, the component a and the component b, so that a grey-scale map with highlighted pigment spots is generated. And finally, coloring the grey-scale map in the Lab color space through adjustable parameters to generate a high-contrast color enhanced image. The method overcomes the dependence on hardware and training data in the prior art, can clearly and adaptively enhance various flaws such as acnes, couperose streaks and color spots, shows robustness under different illumination, and can be widely applied to clinical beauty, later photography and real-time video processing.

The invention relates to the technical field of medical image processing, in particular to a medical image segmentation method based on a frame prompt enhanced SK-VM + + network, and the method comprises the steps: collecting a to-be-segmented image in a data set, and carrying out the three-channel copying of the to-be-segmented image; extracting focus bounding box information in the text annotation file to generate a binary mask; generating a box prompt visualization graph by using the binary box prompt mask; fusing the binary box prompt mask and the copied three-channel image to obtain a four-channel tensor; and taking the four-channel tensor as input, constructing an SK-VM + + model, and performing segmentation processing by using the SK-VM + + model. The medical image segmentation method solves the problem that the existing medical image segmentation method is difficult to accurately deduce the spatial context only by means of the pixel intensity in a low-contrast and high-noise environment, and is easy to cause false detection and missing detection.

The invention discloses a reflective steel pipe weld defect identification method based on a multimode spectrum chromatography technology, and provides a detection process of multiband reflection data acquisition, denoising and normalization, multi-region feature distribution modeling, sparse reconstruction separation abnormal response, multi-scale contrast weighted enhancement, significant layered imaging and defect classification and discrimination. According to the method, through the measures of automatic partitioning, environment and material dynamic acquisition, self-adaptive parameter optimization and the like, high-reflection interference is effectively suppressed, the distinguishing between defects and backgrounds is remarkably enhanced, and the detection sensitivity and the positioning precision of the small and micro defects are improved.

The invention relates to the technical field of image analysis, in particular to a vegetation fine classification and recognition method and system based on an unmanned aerial vehicle, and the method comprises the following steps: obtaining a multispectral image through the unmanned aerial vehicle, extracting red edge reflectivity, NDVI and gray-level co-occurrence contrast, generating a feature vector in a standardized manner, calculating neighborhood offset to obtain a dynamic weight, and combining the dynamic weight into a weighted vector; high discrete features are screened as effective channels, multi-scale clustering is carried out, center and region growth extension recognition is optimized, and a vegetation classification atlas is generated. According to the method, a neighborhood pixel feature offset dynamic weight mechanism is introduced, multi-spectral feature dimension contribution degree is adjusted in a self-matching mode, effective channels are screened based on full-image dispersion, redundant interference is eliminated, image pyramid multi-scale clustering and consistency constraint are fused, the complex vegetation boundary recognition capability is improved, dynamic weight and multi-scale optimization are coordinated, and the method is high in robustness and high in robustness. Sample dependence is reduced, and accurate distinguishing of spectrum similar vegetation is achieved.

The invention relates to the field of endoscope image processing, and particularly discloses an automatic lesion recognition system for endoscopy of the digestive system department, which is characterized in that after a preprocessed original endoscope image is acquired, a double-branch parallel processing architecture is used to acquire characteristics with low resolution and rich semantic information through a deep context branch, and the characteristics of the original endoscope image are acquired. The potential area of the focus is accurately deduced; meanwhile, the fine texture of the mucous membrane is captured in a lossless manner through shallow detail branches which keep high resolution in the whole process. Furthermore, through a context-guided asymmetric enhancement mechanism, a global view of a deep branch is utilized to generate an uncertainty perception attention map as a reference, and weak detail features corresponding to a potential focus area in a shallow branch are accurately irradiated and adaptively enhanced. Thus, a conservative enhancement strategy is adopted in an uncertain focus area, background noise is effectively inhibited, and therefore the detection sensitivity and robustness of low-contrast and flat focuses are fundamentally improved.

The invention relates to the technical field of image segmentation, in particular to an anorectal focus automatic segmentation method based on deep learning, which comprises the following steps: acquiring an anorectal image pixel map, extracting contrast and direction offset to mark candidate focus points, screening overlapped marks to generate a focus activation mark map, and establishing a response map to generate a boundary response distribution map. And training the network to output a classification graph, and extracting a truncation path to complete image segmentation. According to the invention, through extracting the contrast value and the gradient amplitude of the local gray level co-occurrence matrix, accurate capturing of the spatial difference of the lesion area under a complex background is realized, through constructing a response map and direction consistency comparison mechanism and combining multi-dimensional features such as a direction gradient histogram and a structure tensor, the area discrimination capability and the edge classification precision are improved, and the accuracy of edge classification is improved. The texture stability is judged by means of anisotropic standard deviation, a fuzzy edge mask is set, truncation paths are screened in combination with a main direction vector included angle deviation trend, and continuity and stability of a boundary convergence position are ensured.

The invention relates to the technical field of machine vision and wind power detection, and discloses a surface defect identification method and system for a rotor core of a wind driven generator, and the method comprises the steps: collecting an original image of the surface of the rotor core, and carrying out the gray mapping, and obtaining a single-channel gray image; carrying out frequency domain periodic texture suppression and local histogram equalization processing with contrast limitation on the single-channel grayscale image to obtain an enhanced feature image; constructing a background fitting model for the enhanced feature image and performing differential operation to obtain a background differential image; performing adaptive segmentation and morphological refining based on a texture direction according to the background difference image to obtain a refined defect connected domain; local gray level distribution is extracted, sub-pixel-level geometric feature calculation is carried out, and defect geometric attribute data are obtained; and spatial clustering and grading evaluation are carried out according to the data, and a final defect distribution map is determined. According to the method, periodic texture interference can be effectively suppressed, and sub-pixel-level precise positioning and intelligent grading of the tiny defects are realized.

The invention relates to the technical field of image enhancement, and discloses an intestinal dynamic MRI image enhancement method and system based on deep learning, through the image enhancement method based on deep learning, the quality of a small intestine cine-MRI image is improved, the problems of low resolution and poor contrast of a traditional image are improved, and the identifiability of a small intestine intestinal wall structure is enhanced. On the basis, pixel-level registration processing of continuous frame images is combined, the intestinal segment movement track is accurately extracted, and movement parameters such as contraction frequency, lumen diameter variation amplitude, pixel displacement mean value and variance are calculated. Furthermore, a pre-training evaluation model is utilized to intelligently output a small intestine dynamic state judgment result based on motion parameters, and standardized auxiliary recognition of small intestine dynamic disorder diseases and severity of the small intestine dynamic disorder diseases is achieved. According to the method, the image processing quality, the motion feature extraction accuracy and the evaluation intelligence level of small intestine dynamic evaluation are integrally improved, and the method has important clinical application value and popularization significance.

The invention relates to the cross technical field of artificial intelligence and medical image processing, in particular to a medical image segmentation system and method based on a wavelet bridge diffusion model and an efficient conditional random field. A WBDM-ECRF framework is constructed and comprises a discrete wavelet transform module, a BDM-T module, a BDM-S module and an ECRF module; decomposing the image through discrete wavelet transform, extracting a low-frequency sub-band, and enhancing the contrast ratio of a focus and normal tissues; the BDM-T takes U-Net as a backbone, integrates a FlashAttention mechanism, and optimizes a variance formula to realize efficient training; the BDM-S adopts a leapfrog sampling strategy, so that the reasoning time is greatly shortened; the ECRF introduces a multivariate potential function of a structural similarity index and smooth operation through edge expansion, and accurately optimizes edge segmentation. According to the method, the dependence of marked data is reduced, the training and reasoning efficiency is improved, the problem of fuzzy edge segmentation is solved, the Dice coefficient and intersection-union ratio performance on the ISIC data set is excellent, and reliable quantitative support is provided for disease diagnosis and treatment.

The invention provides an automobile cabin end side model multi-modal perception enhancement method, which comprises the following steps: S1) deploying an RGB camera in a cabin, and collecting facial biological characteristic data, hand interaction behavior data and cabin overall environment data of a driver; s2) adjusting image illumination and contrast by adopting an algorithm, dynamically focusing a key area through a target detection frame to process a shielding problem, and eliminating static redundant information; s3) constructing a neural network, extracting facial biological features and the like in parallel, and outputting low-dimensional feature vectors; s4) time sequence association is established, key area feature weights are enhanced through a space attention mechanism, and a logic relationship among different features is explicitly modeled; s5) aggregating the weighted feature maps by using feature attention pooling, and retaining a core feature channel in combination with a channel pruning technology to realize model compression; s6) dynamically adjusting the calculation priority and weight distribution of each feature extraction branch; and S7) the end side uploads the sample to the cloud side, and the cloud side generates an update package and pushes the update package to the end side to complete model iteration.

The invention discloses a road target detection method and device based on bimodal feature fusion and weak light enhancement, and relates to the field of target detection. And the weak light enhancement target detection model performs enhancement and target detection on the road image under the weak light condition. And the bimodal target detection model performs target detection on the road image under the normal light condition. The weak light enhancement target detection model carries out feature extraction through a multi-scale feature attention module, and carries out image feature brightness enhancement through a brightness enhancement module. The image quality is improved, the image contrast is kept, and the target detection accuracy of the weak light image is greatly improved. The bimodal target detection model adopts a double-branch training strategy, the rich capture capability of visible light on target color and texture features under a normal illumination condition is fully exerted, the advantage of a target contour is highlighted in combination with the penetration characteristic of infrared light, and the target recognition capability under the conditions of complex background, shielding and long-distance blurring is remarkably improved.

The invention relates to the technical field of perovskite thin film detection, in particular to a perovskite blade coating thin film defect detection device and method based on real-time visual monitoring, and the device comprises a support, an image acquisition unit, an illumination unit and a self-adaptive adjustment system which are arranged in a glove box and located at a coating machine, a flash evaporation box and a heating stage respectively; the camera and the lens are installed on the adjusting frame, and the first driving piece achieves height adjustment. The light source is installed on the supporting frame and controlled by the second driving piece to ascend and descend. The image analysis unit evaluates the definition, contrast and illumination uniformity of the acquired image, and the control unit automatically adjusts the height and angle of the light source and the position of the camera according to the result so as to keep the image quality stable; according to the scheme, real-time monitoring is carried out in the working procedures of coating, flash evaporation, heat treatment and the like, the comprehensiveness, real-time performance and accuracy of thin film detection are effectively improved, influences caused by environmental fluctuation are avoided, and the accuracy and consistency of defect recognition are guaranteed.

The invention relates to an interventional therapy target accurate positioning method and system based on an ultrasonic image. According to the method, a binary mask is generated by extracting an acoustic shadow area in an ultrasonic image, contrast enhancement parameters of different areas are dynamically adjusted based on the binary mask, and target spot edge details are enhanced while shadow area noise is suppressed; a shadow mask attention mechanism is combined to guide a neural network to accurately segment a target point probability graph, a segmentation boundary is evolved and optimized by integrating a level set constrained by a shadow region shape, and local smooth compensation is performed on registration deviation of a three-dimensional reconstruction model and a preoperative image by using a shadow perception deformation field correction strategy; and finally generating a puncture path for avoiding the dangerous structure. The problems of fuzzy target boundary, segmentation error accumulation and deformation compensation misalignment of a traditional method under acoustic shadow interference are effectively solved, and the pertinence of image enhancement, the accuracy of target positioning and the safety of an intervention path are remarkably improved.

The invention relates to the technical field of focus labeling, in particular to a medical image focus labeling method and system based on deep learning, and the method comprises the steps: collecting a multi-modal medical image, and carrying out the noise reduction and contrast enhancement preprocessing to obtain a medical enhanced image; segmenting a focus area through a deep convolutional network and generating a focus labeling mask image; and performing post-processing optimization on the mask image, inputting an optimization result into the sub-pixel level annotation generation model, and outputting a high-precision focus annotation coordinate sequence. According to the method, image quality and focus visibility are improved through multi-modal preprocessing, accurate segmentation is realized by using a deep convolutional network, traditional pixel-level precision limitation is broken through in combination with post-processing and a sub-pixel-level model, the problems of noise interference, contrast difference and boundary blur are effectively solved, and focus labeling fineness and spatial positioning precision are remarkably improved.