1003 results about "Contrast enhancement" patented technology

The invention discloses a quartzite surface defect detection method, system and device, and relates to the technical field of image processing, and the method comprises the following steps: fixing a to-be-detected quartzite on a detection platform, and carrying out preprocessing; constructing a double-path polarization imaging light path; polarization parameter optimization is carried out based on the optical anisotropy characteristic of quartz stone, so that a polarization state difference is generated between a defect area and a normal area; under the condition of polarization parameter optimization, synchronously acquiring a first polarization image and a second polarization image through a double-path polarization imaging light path; performing polarization difference calculation on the first polarization image and the second polarization image to generate a polarization difference image; and carrying out contrast enhancement processing on the polarization difference image, identifying a defect area, carrying out defect classification, and outputting a quartz stone surface defect detection result. By optimizing polarization imaging parameter configuration and combining image processing, high-precision detection and intelligent classification of quartz stone surface defects are achieved, and the automation level and reliability of detection are improved.

The invention discloses an MURA defect detection method based on background reconstruction and related equipment, and relates to the field of image processing. The method comprises the following steps: acquiring an initial image displayed by a display panel; performing discrete cosine transform processing on the initial image to extract low-frequency background components; denoising the low-frequency background components based on a singular value decomposition algorithm to obtain main background features; performing fitting reconstruction on the main background features through a polynomial surface fitting algorithm to obtain a background reconstruction image; performing contrast enhancement processing on the background reconstruction image to obtain a background enhancement image; subtracting the background enhancement image from the initial image to obtain a difference image; determining an MURA defect area to be detected according to the difference image; and performing identification processing on the MURA defect to-be-detected area through a preset MURA defect detection model to obtain an MURA defect detection result of the display panel. According to the technical scheme, the MURA defect detection precision can be improved.

The invention relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

The invention relates to the technical field of building information modeling, in particular to a building drawing geometric feature extraction and visual modeling method and system, and the method comprises the steps: carrying out the self-adaptive noise reduction, contrast enhancement and line refinement processing of an original building drawing image, and carrying out the automatic layer separation based on colors and line types; linear geometric features and specific symbol geometric features in the drawing image are extracted, and a geometric feature set is constructed; component instantiation, attribute assignment and topological relation reasoning are carried out by using a predefined building component semantic rule base, and a building component semantic network is generated; and mapping the semantic network to a parameterized three-dimensional modeling engine, calling an IFC standard three-dimensional template, performing parameter driving and automatic assembly, generating a three-dimensional building model with semantic information and a spatial structure, and performing visual output. According to the method, efficient and standardized conversion from a two-dimensional building drawing to a three-dimensional building model can be realized, and the method has the remarkable advantages of processing complex drawings and high-precision modeling.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention provides an aluminum alloy surface oxidation spot defect identification method and device based on machine vision, and relates to the field of intelligent manufacturing and industrial automation, and the method comprises the steps: obtaining an aluminum alloy surface color image, and carrying out the preprocessing of the image, so as to extract a brightness component image; self-adaptive threshold segmentation of local contrast enhancement is carried out on the brightness component image, a defect area binary mask is generated, morphological connected domains are extracted according to the mask, and three basic feature indexes of the area pixel value, the contour Fourier descriptor complexity and the area gray scale standard deviation contrast of each connected domain are calculated; and extracting and marking a connected domain boundary, verifying a boundary closed topological structure, and dynamically generating a curvature-driven self-adaptive sampling point through multi-scale B-spline curvature extreme value detection. Through optical-algorithm-process three-level collaborative innovation, the curved surface reflection false alarm rate is reduced, the pinhole detection rate is increased, and the boundary precision is + / -0.2 pixel.

The invention relates to the technical field of medical image report error correction, in particular to a medical image report automatic quality control error correction system and method. The method comprises the steps of importing medical image data, analyzing the medical image data by using an open source tool, performing standardization processing and local contrast enhancement on an image, performing automatic segmentation of a target area through a UNet model, and calculating density features of a key area to obtain feature vectors; meanwhile, the report text data is subjected to word segmentation, standardization processing and keyword extraction steps, the word importance is evaluated by utilizing a BERT model, a semantic vector of the report is generated through a text model, finally, the consistency of the image and the report is detected by calculating the similarity between a feature vector and the semantic vector, and an automatic error correction program is started when the image and the report are inconsistent. And error types are analyzed and corrected. According to the method, medical image processing, natural language processing and deep learning technologies are combined, so that human errors are reduced, and the diagnosis efficiency and accuracy are improved.

The invention discloses a medical form data identification method and system based on OCR and MLLM, and the method comprises the steps: receiving original image data, and generating an optimized image sequence through adaptive contrast enhancement, regional importance weighting and multi-resolution optimization; feature extraction is carried out based on the optimized image sequence, a text-space attention map is constructed, and text recognition is realized; performing visual coding and text coding on the optimized image sequence and the text recognition result, and obtaining uniform feature representation through feature alignment; constructing an information level diagram based on unified feature representation, performing relation reasoning, and combining professional knowledge verification to obtain structured feature information; and performing data verification on the structured feature information to obtain normalized data. According to the invention, efficient identification and accurate structuring of the medical form are realized, and the digitization efficiency of the medical data is improved.

The invention belongs to the technical field of computer vision, and discloses a wire harness crimping defect detection system and method based on machine vision, and the system comprises a multispectral light source control module which is used for employing an annular LED light source and coaxial light mixed illumination and adapting to different wire harness surface materials by dynamically adjusting the angle and intensity of the light source; the multi-dimensional perception acquisition module is used for acquiring wire harness image data and crimping geometric data in real time based on the dynamically adjusted light source angle and intensity; the visual enhancement fusion module is used for de-noising the wire harness image data by using a non-local mean de-noising algorithm and dynamically adjusting and controlling a similarity measurement range through a smooth factor; superposing a multi-scale Retinex algorithm, and enhancing the texture contrast of the wire harness image data through the local contrast; the wire harness image data and the crimping geometric data are fused, and a crimping part multi-mode characteristic graph is constructed; the accuracy of defect detection is facilitated, and potential problems can be found and solved in time.

The invention relates to an underwater image enhancement method based on wavelet Mama, which aims at the problems of color shift, low contrast ratio and fuzzy details of an underwater image, extracts shallow layer features of an underwater low-quality image, inputs the shallow layer features into a multi-scale coding network, and performs joint enhancement on low-frequency color information and high-frequency detail features by using a wavelet Mama unit. Global semantic features are obtained by combining down-sampling layer-by-layer compression, and global modeling of color offset correction and contrast enhancement is completed; the spatial resolution is recovered through up-sampling, and reconstruction and enhancement of texture details and color information are realized in combination with jump connection and a wavelet Mama unit; and finally, the features are mapped to an image domain and fused with input image residual errors, and an enhanced underwater image with natural color, clear texture and balanced contrast is generated. According to the method, frequency domain-space domain joint feature modeling is realized through the wavelet Mama unit, and the color authenticity, the structural definition and the detail perceptibility of the underwater image are remarkably improved.

The invention discloses an MRI medical image segmentation method and system based on deep learning, and the method comprises the steps: obtaining a mammary gland MRI image, carrying out the standardization processing of the mammary gland MRI image, and removing a non-region of interest to obtain a target MRI image; performing detail enhancement on the suspected lesion area to reconstruct an image to be preprocessed; performing noise reduction processing, contrast enhancement and edge enhancement preprocessing on the to-be-preprocessed image to generate three versions of input images, and inputting the three versions of input images into a pre-trained deep learning model as three channels to complete breast tumor identification; and carrying out image segmentation on the breast tumor region, and classifying segmented breast tumors. According to the MRI medical image segmentation method and system based on deep learning provided by the invention, accurate and efficient processing of the mammary gland MRI image is realized, efficient identification, accurate segmentation and accurate classification of the mammary gland tumor are realized, the accuracy and robustness of mammary gland tumor detection can be improved, and efficient and reliable auxiliary diagnosis is provided for clinic.

The invention discloses an image processing algorithm for pupil center real-time extraction and trajectory tracking in an eye closing state. According to the algorithm, from the angle of digital image processing, graying, contrast enhancement and smoothing processing are firstly carried out on an input eye video frame by frame, and then the boundary of a pupil area can be accurately extracted by adopting an adaptive threshold segmentation technology and combining morphological operation and a Canny edge detection operator. And then, accurate fitting of ellipse parameters is performed on the pupil contour by using a geometric distance-based least square method, so that center coordinates and area information of the pupil are obtained, and the accuracy and reliability of pupil monitoring in an eye closing state are remarkably improved. Through recording and analyzing pupil center position changes in a time sequence, real-time tracking of pupil movement tracks is realized. And finally, evaluating pupil light reflex (PLR) in an eye closing state before and after white light excitation by adopting baseline correction and smooth filtering processing. The method has the advantages of high robustness, adaptability to illumination variation, high real-time performance, high precision and the like.

The invention relates to the field of image enhancement, in particular to an image enhancement method and system of a vehicle-mounted image sensor. The method comprises the following steps: acquiring driving record images of a vehicle at multiple angles; dynamic scene self-adaptive exposure adjustment is carried out on the driving record images of the vehicle at the multiple angles, so that multiple self-adaptive exposure driving images are obtained; carrying out deep network noise identification on the plurality of adaptive exposure driving images, and carrying out dynamic filtering and noise reduction on sub-images one by one so as to generate a plurality of filtering and noise reduction optimized images; carrying out image texture analysis on the plurality of exposure sub-images, and carrying out texture distortion reconstruction to construct a plurality of texture reconstruction images; and road object visual identification is carried out on the plurality of texture reconstruction images, and adaptive contrast enhancement processing is carried out, so that a plurality of detail contrast enhancement images are constructed. According to the invention, a high-definition and stable panoramic vehicle-mounted image is realized.

The invention discloses a black pig image segmentation method based on multi-feature fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a black pig image, and carrying out the contrast enhancement processing; inputting the enhanced image into a residual convolutional network to generate a depth feature map, extracting a local shape feature map through curvature threshold segmentation and a double fitting strategy, and fusing the two feature maps to generate a black pig feature map; establishing a spatial position prior probability graph based on the black pig sample library, calculating regional correlation and performing adaptive weighting to obtain a fusion feature graph; boundary segmentation and iterative optimization are carried out on the fused feature map based on the dynamic behavior pattern map and the attitude constraint rule, and an initial segmentation map is generated; and adopting a group behavior model as an optimization criterion, correcting the boundary of the initial segmentation image, and outputting a final segmentation result. According to the method, the segmented enhancement function based on the double-peak characteristic and the local texture feature self-adaptive adjustment strategy are constructed, so that differential enhancement of image preprocessing is realized.

The invention provides a digital information processing method for a hospital radiology department, which comprises the following steps: S1, multi-modal image collaborative acquisition and standardization: synchronously acquiring anatomical structure images, functional images and metabolic parameter data of a patient through radiology department imaging equipment, and converting the anatomical structure images, the functional images and the metabolic parameter data into space-time aligned three-dimensional digital matrixes; s2, image quality optimization processing: performing nonlinear contrast enhancement and noise suppression on the original image to improve the signal-to-noise ratio of a target area; s3, dynamic self-adaptive registration: according to the biomechanical characteristics of the organ, fusing the rigid transformation model and the elastic deformation model, and according to the digital information processing method for the hospital radiology department, based on the dynamic registration matrix of the biomechanical model, improving the multi-modal image fusion precision; a deep learning segmentation algorithm fused with morphological constraints improves the focus boundary recognition accuracy; a texture mapping three-dimensional reconstruction technology is mixed, and an anatomical structure and metabolism information are presented at the same time; the invention discloses a structured report automatic generation system based on an attention mechanism.

The invention provides an image-based construction site violation recording method and device. The method comprises the following steps: acquiring a real-time image of a construction area through a camera, and preprocessing, including denoising, contrast enhancement and size adjustment; using an improved YOLOv5 network to identify hand and head features, positioning a constructor, and extracting position information, state information and action information; based on the extracted information, judging whether the constructors have illegal behaviors or not; if yes, the system controls the camera to capture and store violation images. According to the invention, by optimizing the image acquisition and processing technology and combining with the improved YOLOv5 network, the efficiency and accuracy of violation monitoring on the construction site are significantly improved, and the problems of poor image quality, low recognition precision and low violation judgment accuracy in violation monitoring on the construction site in the prior art are solved.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention discloses a product defect detection method and system based on multispectral imaging, and the method comprises the steps: obtaining multispectral image data of a financial payment terminal product, and generating a first image set; performing de-noising filtering and contrast enhancement processing on the first image set to generate a second image set; performing feature extraction based on the second image set to generate a feature set; and constructing a special convolutional neural network model of the plastic shell and the metal circuit board according to the feature set, generating a model set adaptive to material difference through transfer learning, and determining a final detection model to perform defect detection on the real-time multispectral image to obtain a detection result. The accuracy and efficiency of financial payment terminal product quality detection are improved, data support is provided for production process optimization, the product defect rate is effectively reduced, and the product quality and reliability are improved.

The invention provides an infrared thermal imaging image processing method for a pain area, and relates to the field of image enhancement, and the method combines multi-scale adaptive contrast enhancement and a bidirectional circulation mechanism, captures global and local temperature features of an infrared image, constructs an iterative feature enhancement module through an incremental fusion strategy, and carries out the image enhancement through the iterative feature enhancement module. Different level features are fused in stages, learnable parameters are introduced to dynamically adjust the new and old feature fusion proportion, the adaptability and flexibility of the model are improved, and a staged training strategy is adopted, so that the model can learn low-level thermal image information and also can extract high-level structures and thermal anomaly semantic features. According to the method, the adaptability of the model to different types of focus areas is enhanced, and the actual demand of efficient and automatic processing of the infrared image is met.

The invention discloses an in-tunnel cable facility defect feature extraction and identification method and system based on YOLOv8, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting data through an automatic moving platform and a high-definition camera, carrying out the preprocessing of denoising, contrast enhancement and the like, and extracting defect features through a designed convolutional neural network. And real-time defect detection is realized through a YOLOv8 algorithm, and the efficiency is improved through model lightweight processing. A detection result is optimized through a non-maximum suppression and double combination algorithm, and finally a multi-format visual report is generated. According to the method, efficient extraction and accurate identification of defect features of the cable facilities in the tunnel are achieved, the rapid detection capability and the fine feature extraction capability of the YOLOv8 are combined, the risk and cost of manual inspection are reduced, the cable facilities in the tunnel are efficiently managed, and the method is suitable for detecting the defects of the cable facilities in the tunnel and has good application prospects. And features of different defects can be learned, so that accurate identification of defect types is realized.

The invention relates to the technical field of defect detection, and discloses an industrial surface defect detection method, system and device and a storage medium. The method comprises the following steps: receiving an input surface image of a to-be-detected industrial product and carrying out image preprocessing to obtain a preprocessed surface image; performing multi-scale decomposition on the preprocessed surface image to obtain images of different scales; processing the images of different scales based on an adaptive local contrast enhancement algorithm to obtain enhanced images of different scales; performing multi-scale fusion on the enhanced images of different scales to obtain a target enhanced image; and performing defect detection on the target enhanced image based on an adaptive threshold segmentation method to obtain a defect candidate region. According to the method, industrial surface defect detection is carried out in a mode of combining multi-scale analysis and self-adaptive local contrast enhancement, so that the accuracy and robustness of defect detection are effectively improved, and the requirement of real-time detection in industrial production can be met.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that recolors a digital design according to colors of a digital image and further generates an enhanced recolored digital design. In particular, in some embodiments, the disclosed systems identify a digital image for recoloring a digital design and recolors the digital design utilizing a color affine transformation algorithm to generate a recolored digital design. Further, in some embodiments, the disclosed systems generate the enhanced recolored digital design by transforming one or more colors of the recolored digital design to be within a range of the colors of the digital image utilizing a convex hull projection method. Moreover, in some embodiments, the disclosed systems further enhance the recolored digital design utilizing a contrast enhancement algorithm to modify luminescence values.

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 field of intelligent detection, and provides an LCD system fault detection system and method based on image analysis, and the method comprises the steps: firstly obtaining an LCD panel image collected by a high-resolution industrial camera, then carrying out the homomorphic filtering and CLAHE contrast enhancement processing of the image, and obtaining an enhanced LCD panel image; the method comprises the steps that firstly, an LCD panel image is obtained, then multi-scale feature enhancement fusion is carried out on the enhanced image to obtain an LCD panel image multi-scale fusion feature map, then statistical analysis is carried out on the feature map based on a Mura segmentation area to obtain a Mura area statistical feature coding vector, and finally a Mura defect recognition result is obtained according to the coding vector. Therefore, the characteristics of the Mura defect can be captured more accurately, misjudgment and missing detection caused by interference factors such as a light source and noise are reduced, the detection precision of the Mura defect is improved, and meanwhile, the detection system can adapt to different production conditions and ensure the stability of a detection result.

Provided is an ultrasound imaging apparatus including a display, a memory storing one or more instructions, and a processor configured to execute the one or more instructions stored in the memory, wherein, by executing the one or more instructions, the processor is further configured to generate at least one contrast-enhanced image of an object based on first ultrasonic signals obtained when a probe sweeps the object in a first direction, generate at least one ultrasonic image of the object based on second ultrasonic signals obtained when the probe sweeps the object in a second direction different from the first direction, and control the display to display the at least one contrast-enhanced image and the at least one ultrasonic image.

The invention discloses an image enhancement method based on high-frequency and low-frequency adaptive fusion. The method comprises the following steps: respectively carrying out contrast enhancement of color channels on original images to obtain intermediate feature maps; calculating pixel mean values of the intermediate feature maps under the color channels; performing color compensation on the intermediate feature map according to the pixel mean value to obtain a color reconstruction image; decomposing the color reconstruction image into high-frequency image features and low-frequency image features; performing normalization processing and gamma correction on the high-frequency image features to obtain a high-frequency corrected image; sharpening the high-frequency correction image to obtain a high-frequency recovery image; carrying out defogging processing on the low-frequency image feature weight to obtain a low-frequency recovery image, and carrying out adaptive fusion on the high-frequency recovery image and the low-frequency recovery image to obtain an output image; rich detail and edge information is provided by the high-frequency recovery image, and optimized background and color information is provided by the low-frequency recovery image, so that the visualization effect of the output image is remarkably improved.

The invention relates to an underwater image enhancement method based on adaptive color equalization and multi-scale fusion, and belongs to the technical field of computer vision. The method comprises the following steps: firstly, carrying out adaptive color equalization on R, G and B channels of an original underwater image to reduce the difference among the R, G and B channels, and then carrying out color correction by using a white balance algorithm; then, constructing a gamma function for the image after color correction, performing brightness correction, and then performing Rayleigh stretching to obtain an underwater image with clear contrast; thirdly, performing sharpening based on a local variable coefficient on the image after gamma correction to obtain an underwater image with enhanced details, and stretching S and V channels of the processed underwater image to the whole interval; and finally, performing non-subsampled contourlet fusion on the underwater image after S and V stretching to obtain a final clear underwater image. According to the method, the color of the underwater image can be corrected, the contrast ratio of the underwater image is improved, and the details of the underwater image are enhanced.

The invention relates to the technical field of image analysis, in particular to a mobile phone screen uniformity detection method based on image analysis, which comprises the following steps: extracting a central region gray sequence, calculating a slope to generate a trend chart, positioning a change starting point to adjust coordinates, extracting gray difference to enhance comparison, and analyzing a bright-dark span to generate a map. And extracting a gravity center gray level judgment difference and outputting a detection scheme. According to the method, through multi-direction gray scale sequence slope extraction and fluctuation aggregation feature construction, detail capture of a brightness trend is enhanced, reverse gray scale change starting point positioning and coordinate offset are combined, abnormal region positioning precision is improved, a local transition zone and a jump section are extracted, and gray scale fluctuation partition enhancement is realized. Through high-gray pixel set gravity center and local fluctuation difference analysis, brightness abnormal points are accurately recognized, the whole process is fused with the links of gray trend extraction, change track adjustment, contrast enhancement, gravity center analysis and the like, the detection sensitivity and the judgment precision are enhanced, and the screen brightness differential detection requirement is met.

The invention discloses an automatic endoscope pipeline inner wall crack identification system based on video deep learning, and relates to the technical field of pipeline detection, and the system comprises an image acquisition module, an image preprocessing module, a pipeline inner wall model establishment module, a deep learning identification module and a result output module. The image acquisition module is used for acquiring images in the pipeline by utilizing the autonomous moving endoscope device, and a multi-wheel driving structure, a self-adaptive rubber track, a high-precision sensor and the like of the image acquisition module ensure that the acquisition process is stable; according to the invention, the automation level and accuracy of pipeline inner wall crack detection are obviously improved, the autonomous moving endoscope device is matched with three-dimensional illumination and multi-angle image acquisition to ensure that high-quality pipeline inner wall images can be obtained in a complex environment, and through calibration registration, noise suppression and contrast enhancement of the image preprocessing module, the pipeline inner wall crack detection accuracy is improved. And high-quality input data is provided for a deep learning model, so that the accuracy and reliability of crack identification are improved.

The invention relates to a small-sample contrast enhancement fine tuning method and system based on a large language model, which are used for identifying named entities in recruitment texts. The method comprises the steps of performing cleaning and format conversion on an original recruitment text, and generating an input sample conforming to a natural language instruction format; under the condition that the labeled samples are insufficient, positive and negative sample pairs are constructed to enhance the recognition capability of the model on entity categories and boundaries; carrying out low-rank parameter updating on the pre-trained large language model by adopting a LoRA fine tuning technology, and reducing computing resource consumption in combination with 4-bit quantitative training; in a pre-training large language model reasoning process, through a multi-dimensional joint confidence evaluation mechanism, confidence of four dimensions of entity levels, lengths, types and contexts is synthesized, and low-confidence identification results are filtered after dynamic weighted normalization processing. The method is suitable for recruitment recommendation, talent matching and other downstream tasks, and has the advantages of high recognition accuracy, low training cost, high system robustness and the like.