210 results about "Image evaluation" patented technology

The invention provides a self-adaptive accurate rust removal method and system based on unmanned aerial vehicle visual feedback, and relates to the technical field of large metal structure surface maintenance and anticorrosion treatment.The rust removal method comprises the steps that a global path is planned, and a global flight waypoint sequence is generated; a local image of the current to-be-derusted area is collected; the corrosion characteristics of the current to-be-derusted area are analyzed, and initial derusting parameters and initial derusting time are set; after each derusting operation is finished, a frame of intermediate result image is collected; based on the difference between the intermediate result image and the historical image, performing quantitative evaluation on the effect of the previous rust removal operation, and performing feature analysis on the residual rust region; self-adaptively adjusting and generating rust removal parameters of the next stage; and the steps of derusting operation, image collection, evaluation and parameter adjustment are repeatedly executed, and after the termination condition is met, the unmanned aerial vehicle is switched to the next to-be-derusted area.

The invention provides an image processing method, a training method of an image evaluation model and electronic equipment, and relates to the field of computer technology and artificial intelligence. The method comprises the steps that image data and inquiry text data are acquired, the image data are generated by an image generation model, and the inquiry text data are used for describing the requirement for quality evaluation of the image data by adopting a natural language; respectively inputting the image data and the inquiry text data into a plurality of image evaluation models, and respectively carrying out quality evaluation on the image data from corresponding evaluation dimensions by utilizing the plurality of image evaluation models to obtain a plurality of evaluation results; and integrating the plurality of evaluation results to obtain a quality evaluation result of the image data. According to the method and the device, the technical problems of relatively low accuracy and meticulousness of image quality evaluation in related technologies are solved.

The invention discloses a photovoltaic panel surface pollution degree image evaluation system. The system comprises an image acquisition module, an image processing module, a multi-task evaluation module and a pollution degree determination module. Multi-spectral image data of the surface of a photovoltaic panel are collected, after radiation calibration and atmospheric correction processing, spectral reflectivity features and spatial texture features are extracted and fused, a multi-task deep learning model is input, a pollution type classification result and a power generation efficiency loss weight of each pixel region are output, and a pollution type classification result of each pixel region is obtained. And finally, generating a pollution type spatial distribution diagram, calculating the overall power generation efficiency loss percentage, and determining the pollution degree grade. According to the method, the problem that the mixed pollution type cannot be distinguished and the differentiation influence cannot be evaluated in the prior art is solved, accurate quantitative evaluation of the pollution degree is realized, and a reliable basis is provided for fine operation and maintenance of a photovoltaic power station.

The invention discloses a cutting control method and system for femtosecond laser machining equipment, and relates to the technical field of laser precision machining and intelligent control. The method comprises the steps of collecting a cut image sequence in real time, extracting and normalizing feature parameters representing cutting quality, and forming a standardized feature vector; performing quality evaluation based on the feature vector and generating a score; identifying anomalies and predicting a quality trend by threshold comparison, generating a parameter optimization request when deviation or predicted degradation is detected; inputting the feature vectors, the process parameters and the quality scores into a pre-trained AI model, outputting process parameter correction and adjusting equipment parameters; and after the equipment is updated, acquiring the image again to evaluate the quality so as to decide whether to maintain the adjustment result. Self-adaptive control and dynamic optimization of femtosecond laser cutting are achieved, parameter matching and path adjustment can be automatically completed under the conditions of multiple materials and multiple procedures, and manual intervention and debugging are reduced.

The invention discloses an image transmission method, device and equipment of a remote operation system and a medium, the method is applied to a local operation end, the remote operation system comprises the local operation end and a remote operation end, and the method comprises the following steps: acquiring an initial ultrasonic image corresponding to a target object at the local operation end, performing feature extraction on the initial ultrasonic image to obtain a target ultrasonic image; determining an ultrasonic sub-image corresponding to the target ultrasonic image based on the knowledge graph, and determining an image value of the ultrasonic sub-image according to the ultrasonic image evaluation dimension; and transmitting the ultrasonic sub-image to a remote operation end according to the image value. Based on the technical scheme, the image is divided into the sub-images of different areas, the image values of the sub-images are determined, and the transmission methods corresponding to the sub-images are determined according to the image values, so that the preferential transmission of the high-value image is ensured, the overall bandwidth occupation and the system load are reduced, and the transmission efficiency is improved. And a key technical support is provided for the accuracy and safety of a remote operation.

The invention discloses a model generation image evaluation method and device and a storage medium, and relates to the technical field of image processing. According to the method, the image generated based on the text cue word is obtained, wherein the image comprises the image element corresponding to the text cue word; filtering the image to obtain a filtering response value of each image pixel in the image, and generating a visual attention map of the image based on the filtering response values, the visual attention map representing frequency domain energy distribution of the image; and determining a visual saliency value of the image element according to the visual attention map, and determining a layout score of the image according to the visual saliency value of the image element. According to the method, spatial relations such as distances, alignment modes and hierarchical structures among elements are quantitatively evaluated through visual saliency values, and the defect that a previous model cannot accurately evaluate and adjust image layout is overcome.

The embodiment of the invention provides a multi-view consistent image generation method and device based on geometric information guidance, a storage medium and electronic equipment, and relates to the technical field of image generation, and the method comprises the steps: obtaining a multi-view geometric image; extracting a cross-modal condition feature, an image condition feature and a geometric condition feature based on the multi-view geometric image to serve as condition input of a diffusion model; constructing and initializing a multi-view diffusion generation model containing a decoupling geometric attention enhancement mechanism based on the condition input; optimizing parameters of the multi-view diffusion generation model by adopting a geometric information intensity adjustment mechanism; and generating a multi-view image based on the optimized multi-view diffusion generation model, and performing image evaluation based on perception similarity, distribution difference and diversity indexes. According to the method, the problems of structure mismatching, shielding disorder, detail drift and the like in the existing single-view-to-multi-view-angle generation are solved, and the multi-view-angle consistency and the detail fidelity are improved.

The invention belongs to the technical field of optical module assembly, and particularly relates to an AR equipment intelligent active alignment method based on artificial intelligence, and the method comprises the steps: enabling a system to obtain the real-time motion posture data and real-time image definition data of a six-axis robot in an alignment period in real time, the two sequences are marked as a motion posture sequence and an image definition sequence after being aligned and denoised; extracting an initial evaluation value from the image definition sequence; acquiring an image evaluation value of the detection position; constructing a weighted instantaneous gradient index; obtaining an out-of-focus curve correction factor; outputting a nonlinear correction distance factor representing the residual physical movement amount; obtaining a self-adaptive confidence weakening factor; calculating coordinates of a dynamic fine adjustment starting point; and the six-axis robot is driven to move to the dynamic fine adjustment starting point coordinates, and final optimization is executed. The technical problem that in the prior art, the fixed fine adjustment starting point is not matched with the batch tolerance of optical components, and consequently the optimal imaging point dynamically shifts is effectively solved.

The present invention relates to a self-propelled forage harvester (1) comprising a height-adjustable header (4) for receiving crop (2), working units (20) for processing the received crop (2), an unloading device (15) for discharging the processed crop (2), a camera system (16) for capturing images (41) of a crop flow (21) passing through the forage harvester (1), an image evaluation device (27) for evaluating the images (41), and a driver assistance system (17) for controlling the header (4), the working units (20), and the unloading device (15), wherein the driver assistance system (17) has a memory (40) for storing data and a computing device (39) for processing the data stored in the memory (40), wherein the image evaluation device (27) together with the driver assistance system (17),The front attachment (4) and the working units (20) form an automatic setting system, in that the image evaluation device (27) is configured to continuously analyze the images (41) of the crop flow (21) for the proportion of, in particular inorganic, impurities contained in the crop flow (21) by means of a machine learning algorithm and to transmit a derived degree of contamination (DC) to the driver assistance system (17), which autonomously and continuously adapts a setting of the front attachment (4) and / or at least one of the working units (20) depending on the degree of contamination (DC).

The invention relates to the technical field of ship maintenance, and provides a method and system for analyzing the cleaning effect of a ship decontamination robot, and the method comprises the steps: employing a plurality of independently controlled illumination units, and activating all illumination units according to a preset time sequence within a preset time; when each lighting unit is activated, synchronously acquiring a frame of image corresponding to the surface of the ship body to obtain a plurality of frames of images, and packaging into an activated acquired image corresponding to the surface of the ship body; performing optical interference area identification on the activated acquisition image to obtain an optical interference area identification result; based on an optical interference area identification result, performing information fusion on a plurality of frames of images in the activated acquisition image to reconstruct an optical interference-free image corresponding to the ship body indication, and obtaining an interference-free surface image; based on the non-interference surface image, the cleaning quality of the ship surface is evaluated, and a ship decontamination robot cleaning effect analysis result is obtained. The automatic cleaning device has the effect of improving the efficiency and thoroughness of automatic cleaning operation.

The present application relates to the target ISAR image evaluation method based on signal processing and image change, belongs to ISAR image processing field. In view of the ISAR image imaging fuzzy under the condition of target posture change on ISAR image, feature extraction is difficult, leading to the problem of target threat assessment difficulty, a kind of target ISAR image evaluation method based on signal processing and image change is presented. Including: based on the rearrangement and elimination method of micro-motion to realize the de-micro-motion imaging of space micro-motion target;The ISAR image is carried out morphological processing open operation, using Canny operator carries out edge detection processing, carries out Hough transformation and carries out straight line detection, obtains the size and boundary of target;The echo model of compound micro-motion target of combined micro-motion is established, and the micro-motion period is estimated using the method based on the phase difference signal aggregation degree, realizes the de-micro-motion imaging of space target and micro-motion target feature extraction and solves the problem of target threat assessment difficulty.

The embodiment of the invention provides an image processing method and device.The image processing method comprises the steps that in response to an editing instruction for an original image, the original image is updated into an edited image through an image editing model; inputting the original image and the edited image into an image evaluation model for processing to obtain image evaluation information corresponding to a plurality of image evaluation dimensions; dividing the editing image into an editing area and a non-editing area, and determining editing evaluation information corresponding to the editing area and non-editing evaluation information corresponding to the non-editing area; and according to the region information of the editing region, fusing the image evaluation information, the editing evaluation information and the non-editing evaluation information to obtain target evaluation information corresponding to the editing image.

This application discloses an unsupervised adaptive method and system for evaluating insurance claims images. The method includes: normalizing multimodal claims images; extracting feature vectors from a Siamese network encoder, bringing features of different modalities under the same case ID closer together and pushing features of images under different case IDs further apart, achieving case-level cross-modal semantic alignment; constructing a structured evaluation database storing historical image feature vectors and business dimension labels; generating evaluation results for new images through a similarity retrieval database; fusing contrast loss and reconstruction loss, and automatically adjusting the dynamic balancing parameter γ based on the retrieval matching degree or reconstruction error to achieve adaptive evolution of the encoder incremental updates. This application eliminates the need for manual annotation, solves the problems of semantic fragmentation in multimodal data and model update lag, and improves evaluation accuracy and efficiency.

The invention belongs to the technical field of power cable detection, and particularly relates to an image evaluation method for the twisting tightness of an overhead insulated cable, and the method comprises the steps: obtaining a cable detection image, carrying out the semantic segmentation and skeleton extraction, and unfolding a cable main body into a standardized ROI region through the polar coordinate transformation, so as to eliminate the geometric distortion; performing frequency domain transformation on the ROI region to obtain a power spectrum, and extracting multi-dimensional feature vectors including a twisting pitch frequency, an energy concentration ratio, a frequency spectrum symmetry unbalance degree and a harmonic broadening index; determining a defect type and calculating a confidence coefficient by calculating a weighted square distance between the feature vector and a prototype feature library; triggering an alarm or sending an adjusting instruction to the production line according to the detection result. According to the invention, cable jitter interference can be effectively overcome, and accurate on-line monitoring and closed-loop control of defects such as strand breakage and too loose twisting can be realized.

The present application provides a kind of face image evaluation method, device, equipment and readable storage medium, face image evaluation method includes: using face detection model to detect the image to be detected, obtain the face frame and attitude angle of all faces to be detected in the image to be detected;For each face to be detected, determine the face image of the face to be detected according to the face frame of the face to be detected;Using face quality evaluation model to evaluate face image, obtain the score of face image;According to the score of face image and the corresponding attitude angle, the comprehensive score of face image is calculated.The present application detects the face frame and attitude angle of all faces in face detection model, and the face quality evaluation model no longer detects the attitude angle, so that the efficiency of the face quality evaluation model can be greatly improved in the case of multiple faces in the snapshot image needing to be evaluated separately, thereby meeting the application requirements of multiple face real-time snapshot even on embedded devices.

The application relates to a character recognition method, a device and a storage medium. The character recognition method is applied to a terminal device, and the method comprises the following steps: displaying first content, wherein the first content is an image containing characters; receiving a character recognition operation performed by a user on the first content; determining an image evaluation level corresponding to the first content in response to the character recognition operation; and recognizing the first content by using a character recognition algorithm corresponding to the image evaluation level to obtain a character recognition result. According to the embodiment of the application, the character recognition algorithm corresponding to the image evaluation level is used to recognize the characters in the first content, so that the characters in the first content can be more accurately recognized, and unnecessary calculation amount is reduced.

To provide an image evaluation method and an image evaluation program capable of appropriately evaluating distortion of an under-vehicle image generated on the basis of a camera image attached to a vehicle.SOLUTION: Generating an integrated overhead image in which an overhead image that is an image obtained by overlooking the vicinity of a vehicle and a shielded image that is an image of an area shielded by the vehicle when the vehicle is overlooked are integrated, and evaluating an image deviation between the overhead image and the shielded image, wherein the overhead image and the shielded image include an image obtained by imaging a same first graphic that extends across the overhead image and the shielded image in the integrated overhead image when there is no image deviation.SELECTED DRAWING: Figure 17

The application discloses an AWB automatic parameter adjusting method and system, the method comprises the following steps: obtaining a test image and a reference image of a device to be adjusted; extracting a color cast optimization region of interest from the test image and the reference image, calculating the index difference of the test image and the reference image in multiple quantization index dimensions to determine the image color cast difference; determining the color cast optimization direction based on the image color cast difference and generating AWB debugging parameters; generating a debugging image according to the AWB debugging parameters, evaluating the error improvement effect of the debugging image; if the error improvement effect does not meet the preset condition, iterative optimization is performed; if the error improvement effect meets the preset condition, the final AWB debugging parameters are output. By determining the color cast difference, using a decision model, and forming a full-process automatic closed loop from color cast analysis, parameter generation to effect evaluation through iterative optimization, the time cost and labor cost of parameter debugging are reduced, and the parameter adjusting efficiency is improved.

This application discloses a method, model training method, and device for evaluating the brightness of portrait images, relating to the field of image evaluation. The training data for a first portrait image brightness evaluation model includes a large number of images where the face is the primary brightness element, enabling the model to primarily evaluate brightness scores based on the brightness of the face portion within the portrait image. The training data for a second portrait image brightness evaluation model includes a large number of images where the entire image is the primary brightness element, allowing the model to use the overall brightness of the entire image as an evaluation factor for its brightness score. By weighting the first brightness score output by the first model with the second brightness score output by the second model, the brightness score of the portrait image is obtained. This allows for the assessment of the portrait image's brightness based on the face's brightness and the brightness of the surrounding environment, improving the accuracy and reliability of the brightness evaluation.

The application provides a kind of self-adapting precision rust removal method and system based on unmanned aerial vehicle vision feedback, and the application relates to the technical field of large metal structure surface maintenance and anticorrosion treatment, the rust removal method of the application includes: global path planning, generate a global flight waypoint sequence;Collect the local image of current rust removal area to be removed;The rust characteristics of current rust removal area to be removed are analyzed, and initial rust removal parameters and initial rust removal time are set;After each rust removal operation ends, collect an intermediate result image;Based on the difference between intermediate result image and historical image, the effect of last rust removal operation is quantitatively evaluated, and the feature of remaining rust area is analyzed;Self-adaptively adjust the rust removal parameters of next stage;And repeat the steps of rust removal operation, image collection, evaluation and parameter adjustment, after meeting the termination condition, the unmanned aerial vehicle switches to the next rust removal area to be removed.

The present invention relates to a device (1) for recording the use of one or more implants (8) inserted during surgery, the implants (8) having identifiers (10) on their implant surfaces (9). One or more implants are provided for surgery and can be used during said surgery. The device comprises a housing (2) having a housing interior (3) in which at least one light source (4) and at least one camera (5) are arranged for recording images of multiple implants (8) of a surgical set (6) before and after surgery, and an image evaluation device for evaluating images recorded before and after surgery by at least one camera (5) to identify one or more implants (8) inserted during surgery. According to the present invention, at least one light source (4) and at least one camera (5) are configured to reduce reflections on the implant surface (9) in order to adequately recognize the identifiers. The present invention further relates to a method using this type of device (1) and to the use of surface-coated implants (8).

The invention relates to an image evaluation method and an ultrasonic system. The image evaluation method comprises the steps that a preoperative ultrasonic image of a detection object and preoperative pose information of a marker are acquired, and a postoperative ultrasonic image of the detection object and postoperative pose information of the marker are acquired; the marker is arranged on a detection object; determining a target corresponding relation between the preoperative ultrasonic image and the postoperative ultrasonic image according to the preoperative pose information, the postoperative pose information, the first pose information and the second pose information; the first pose information is the pose information of the ultrasonic probe corresponding to the preoperative ultrasonic image, and the second pose information is the pose information of the ultrasonic probe corresponding to the postoperative ultrasonic image; and according to the target corresponding relation, the preoperative ultrasonic image and the postoperative ultrasonic image, evaluating the region of interest in the postoperative ultrasonic image. According to the image evaluation method provided by the invention, the accuracy of the obtained evaluation result can be improved.

A tool evaluation device and a tool evaluation method are provided that can automatically determine the phase of the cutting edge in the rotation direction of a tool regardless of the state of attachment of the tool to a tool holder. [Solution] The tool evaluation method includes the steps of: a tool holding unit 24 holding a tool 26; a tool holding unit 24 transporting the tool 26 to an imaging position; an imaging step of the tool 26 at a predetermined tool phase and storing the image as a reference image; a rotating unit 22 connected to the tool holding unit 24 rotating to image the tool 26 at predetermined phase intervals or time intervals within a phase range and obtaining multiple post-machining tool images; a step of comparing the reference image with the post-machining tool images; a step of determining the one of the multiple post-machining tool images that has the highest degree of agreement with the reference image as an observation image; and a step of evaluating wear on the cutting edge 50 of the tool 26 using the observation image.

The invention discloses an urban comprehensive traffic digital platform based on a GIS technology, and the platform comprises a business layer, an application layer, a data layer, a support layer, and an infrastructure layer. The application layer comprises an urban traffic monitoring and evaluation module, an expressway traffic monitoring and evaluation module, a traffic image evaluation module, a comprehensive consultation decision support module and a parking facility management module; according to the invention, the urban comprehensive traffic digital platform is constructed by using urban traffic data resources and industry management data, based on the GIS technology and by using information technologies such as the Internet of Things, cloud computing and big data; the functions of monitoring the urban road traffic state in real time, evaluating the traffic condition, analyzing the traffic jam cause, predicting the traffic travel demand, evaluating the traffic measure implementation effect and the like are achieved, scientific decision support is provided for urban traffic planning, infrastructure construction and traffic management, the wide application prospect is achieved, and application and popularization are facilitated.

A cervix uteri cell image enhancement method based on a generative adversarial network comprises the following steps: carrying out statistical analysis on a cervix uteri cell image data set, and confirming that a high category imbalance problem exists; according to morphological characteristics and clinical importance of cells of different lesion categories, designing differentiated enhancement strategies and target quantities; for extremely scarce high-risk categories, a StyleGAN2-ADA model constrained by medical knowledge is adopted for generation, and for other categories, a traditional enhancement method combining CLAHE, elastic deformation and the like is adopted; and performing quality control through Masked-SSIM and Med-FID medical image evaluation indexes and pathology expert auditing to obtain a high-quality enhanced image. According to the method, the problem of class imbalance in cervical cell image data is effectively solved, the recognition performance of a deep learning model on high-risk cervical lesions is remarkably improved, and the method has important clinical auxiliary diagnosis value.

A computer-implemented method and apparatus for generating and validating digital markups on customizable products. The method includes receiving a textual prompt describing a product and a desired markup, generating a synthetic image of the product with the markup using a generative AI model, and validating the synthetic image by comparing the synthetic image with known product metrics. The method further involves isolating and recognizing specific markup regions, refining the synthetic image based on validation results, generating manufacturing instructions, and providing the instructions to a manufacturing system. The apparatus comprises an AI-assisted markup generator, a validation and correction system, an image segmentation and recognition module, a generative image evaluation and correction module, a manufacturing instruction generator, and a descriptive phrase processor. The apparatus utilizes various generative AI models, machine learning algorithms, and advanced segmentation techniques to ensure the accurate and efficient production of customizable products.

The invention relates to the technical field of medical image processing, and discloses a liver cancer rupture bleeding embolism treatment curative effect image evaluation method and system, and the method comprises the steps: S1, obtaining enhanced CT image data of a patient after liver cancer rupture bleeding embolism treatment, carrying out the preliminary region division of a focus region of the enhanced CT image data, and carrying out the image segmentation of the focus region; obtaining a preliminary region division result; s2, when an interaction correction mark made by a user on at least one sub-region of the preliminary region division result is received, the preliminary region division result is adjusted in real time, the boundary of the adjusted sub-region is obtained, and the interaction correction mark indicates the real organization type of the sub-region; s3, circularly executing the step S2 to perform interactive iteration correction on the boundary of the sub-region until the user confirms a final division result; and S4, performing volume quantitative evaluation on different types of tissues of the final division result, and generating an evaluation report. According to the invention, misjudgment caused by fuzzy and overlapped image features is effectively avoided.

The invention provides a multi-energy-spectrum CT (Computed Tomography) metal artifact removal method for oral medicine, which is used for solving the problem that the existing method cannot realize low-dose and high-precision oral metal artifact removal. The method comprises the following steps: acquiring a plurality of groups of continuous energy channel images of the multi-energy-spectrum CT system in an energy interval; data preprocessing is carried out, and CT images of the same part of adjacent energy channels are spliced in the channel dimension; constructing a metal artifact removal model (ELM) based on edge enhancement; training the model by using the data after the multi-energy-spectrum CT data channel dimension splicing; and removing the metal artifacts of the multi-energy-spectrum CT image by using the trained model. According to the method, the energy spectrum advantages of multi-energy-spectrum CT are utilized, the high-precision metal artifact removal effect and generalization performance are achieved on clinical data and a general data set, and reliable technical support is provided for CT image evaluation after oral cavity metal implantation.

To provide an image evaluation system capable of quickly performing design development of a decorative sheet at the time of manufacturing a decorative molded article, an image evaluation method, and a method for manufacturing the decorative sheet.SOLUTION: An image evaluation system 20 includes a first terminal 30 for providing a storage unit 40 with image data of a decorative sheet 3 and a second terminal 50 that can display a plurality of image data provided from the first terminal 30. The storage unit 40 selects predetermined image data from the plurality of image data based on a signal from the second terminal 50 and transmits the selected image data to the second terminal 50.SELECTED DRAWING: Figure 5A