20results about How to "Enhanced edge" patented technology

The invention relates to a monocular vision tower span measurement method based on RTK correction, and belongs to the technical field of space distance measurement. Comprising the following steps: acquiring ambient light intensity and quality in real time based on an environment where a power transmission and distribution tower is located, and obtaining ambient light adaptation parameters by analyzing ambient light data and adjusting ISO sensitivity and shutter speed of a camera. According to the method, real-time environment light data analysis and camera parameter automatic adjustment are introduced in the monocular vision tower span measurement process, the image quality is effectively improved, tower recognition is more accurate, background interference is reduced, the geographic position and the shooting angle of a shooting point are corrected through the RTK technology, and the measurement accuracy is improved. And perspective transformation correction is carried out on the image in cooperation with a perspective projection theory, and the accuracy of distance measurement data is improved to centimeter level.

The invention provides a lace texture image diffusion generation method based on an ADM and a dynamic denoising filtering model, the model combines a learnable Gaussian blur layer and a high-pass filtering technology, and the Gaussian blur layer aims at adaptively adjusting the blur degree of the image according to different input images and reducing high-frequency noise in the lace texture image. According to the high-pass filtering technology, key areas such as the contour and the edge of a main pattern in the image are enhanced, so that main texture features of the image are highlighted. The module is added into a down-sampling layer of an ADM network, the phenomenon that lace texture images generated by an original model are too smooth is improved, and the texture layering sense of the images is enhanced.

The invention discloses an image enhancement method and device based on double-teacher knowledge distillation and a storage medium, and belongs to the technical field of digital image processing. According to the method, an underwater image enhancement network for double-teacher knowledge distillation is provided, and an image processing problem is divided into a plurality of small problems by the framework; a Freq-UIE network and a vision-based Fourier processing module are designed, and frequency domain information is added to different layers of the network, so that the effect of edge and detail enhancement is achieved; in order to better match and distill features of multiple teachers and students, a knowledge collection and distillation module is provided, and the features of the students and the multiple teachers are processed on each scale. Experimental results prove that compared with the prior art, the method has the advantages that the detail and texture information of the image can be well enhanced, the color recovery is more accurate, and the edge is clearer.

The utility model discloses a fruit collecting device, and belongs to the field of fruit picking. A fruit collecting device comprises a collecting net; the fruit collecting device further comprises a plurality of supporting rods which surround tree roots and are horizontally placed. The main rod is horizontally placed and located on one side of the supporting rods; the auxiliary rod is positioned on one side of the main rod; the first pull rope sequentially penetrates through the ends, close to the tree roots, of the branch rods through the main rod and then penetrates out of the auxiliary rod; the blocking net group is arranged around the outer edge of the collecting net and is connected with the supporting rod; wherein the collecting net wraps the lower part of the supporting rod and is fixed with the supporting rod; one end of the collecting net is connected with the main rod; the other end of the collecting net is connected with the auxiliary rod. The fruit collecting device has the beneficial effect that the fruit collecting device capable of clamping and adapting tree roots with different thicknesses is provided.

The invention relates to the technical field of visual remote sensing image processing, in particular to a remote sensing image cloud removal method based on robustness Swin Transform. According to the method, the Swin Transform model and the self-attention mechanism thereof are introduced, the information of the pixel level and the semantic level is comprehensively considered, and the detailed post-processing step is combined, so that remarkable innovation and improvement are achieved in the aspects of the cloud removal effect, the generalization ability, the information comprehensive processing, the post-processing optimization and the like. Compared with the prior art, the remote sensing image cloud removal method can more effectively solve the problems encountered in the remote sensing image cloud removal process, provides a high-quality cloud removal result, and has wide application value. The method can significantly improve the cloud removal effect, improves the remote sensing image quality, and has a wide application prospect.

The present invention discloses a method and system for automatic recognition of pathological images based on deep learning, and the present invention relates to the technical field of automatic recognition of tissue pathological images. The method comprises the following steps: collecting a number of digital pathological slice images as sample data, segmenting the collected sample image data, the segmented cell images include positive cell images and negative cell images, and storing the segmented positive cell images and negative cell images into positive cell image sets and negative cell image sets respectively; preprocessing the images in the two image sets to facilitate subsequent recognition and extraction of single cell images in the images; obtaining the extracted single cell images, extracting features of the single cell images to be identified, and training the initial neural network with the corresponding cell features as labels; generating a comprehensive scoring coefficient according to the cell features corresponding to the cell images in each region, and judging the detailed cell type according to the comprehensive scoring coefficient.

The invention discloses a CIS (Contact Image Sensor) image data noise reduction method, which comprises the following steps of: collecting night video image data by a CIS sensor, and carrying out median filtering processing on the night video image data; dividing the processed night video image data into a training set, a verification set and a test set; constructing a deep convolutional neural network, normalizing the processed data in the training set, and training the deep convolutional neural network; evaluating the training model by using the obtained test set and adopting a peak signal-to-noise ratio (PSNR); inputting the obtained verification set into a training model for processing and evaluation, and adjusting the number of network layers and the number of filters according to an evaluation result to obtain an optimal deep convolutional neural network model; and carrying out noise reduction processing on the CIS image data by using the obtained optimal deep convolutional neural network model to obtain image data after noise reduction. The method has the advantages of improving the noise reduction effect, increasing the video processing speed and the like.

This invention relates to the field of target detection technology and discloses a method for obstacle detection in adverse weather conditions for AGV cargo vehicles used in copper electrode plate transfer. The method includes acquiring real-time images of the AGV vehicle to be detected under adverse weather conditions; inputting the real-time images to be detected into an improved YOLOX model for processing; and outputting obstacle detection results. The improved YOLOX model includes a backbone network, a feature pyramid network, a dynamic self-supervised network, and a detection head. The dynamic self-supervised network includes an environment-aware encoding layer and a dynamic feature reconstructing layer. Based on the improved YOLOX model, this invention solves the key technical problem of feature coupling caused by environmental interference in visual detection systems under adverse weather conditions by introducing a dynamic self-supervised network, and also addresses the core issue of a surge in false alarm rates in the AGV vehicle visual detection system during copper electrode plate transfer under adverse weather conditions.

The invention discloses a target semantic segmentation method based on radar data and image data, and the method comprises the following steps: 1, carrying out the preprocessing of radar point cloud data: obtaining the radar point cloud data through a radar sensor, and carrying out the preprocessing of the radar point cloud data; 2, projecting the preprocessed point cloud data, and converting the point cloud data into a pseudo-depth map form; the method specifically comprises the following sub-steps: step 2-1, determining a projection plane; step 2-2, carrying out coordinate conversion; step 2-3, carrying out projection calculation; step 3, performing feature interaction: performing feature interaction on the radar data in the pseudo-depth map form and the input image data; and step 4, inputting the fused multi-modal features into a decoder network to generate a semantic segmentation result. By combining the advantages of the radar point cloud data and the image data, the invention provides a semantic segmentation method which can still keep high precision in severe environments such as low light, strong light, rain and fog and the like.

The present invention discloses a method and device for edge enhancement of RAW domain images based on a lightweight neural network, the method comprising: inputting a RAW format image to be sharpened into a trained convolutional neural network model, classifying the input RAW format image with a classification module, and outputting group information corresponding to the image; then, according to the group information, calling a corresponding set of convolutional neural network weight parameters in an external memory, combining with the sharpening module structure in the convolutional neural network model, sharpening the RAW format image, and outputting an RGB format image. The present invention replaces the traditional image sharpening algorithm with a convolutional neural network, which can combine the advantages of four traditional image sharpening algorithms and is suitable for different types of scenes. In addition, the present invention lightweights the convolutional neural network algorithm, reduces the amount of calculation, and provides the possibility of realizing edge computing.

The application discloses a fruit collecting method and device, and belongs to the field of fruit picking. The fruit collecting device comprises a plurality of supporting rods surrounding a tree root, a main rod horizontally arranged on one side of the supporting rods, a sub rod arranged on one side of the main rod, a connecting sleeve arranged at one end of the main rod and abutting against the tree root, a first pull rope sequentially penetrating through the supporting rods close to the tree root from the main rod and then penetrating out from the sub rod, and a bottom net arranged below the supporting rods and fixed with the supporting rods, one end of the bottom net being connected with the main rod and the other end of the bottom net being connected with the sub rod. The fruit collecting device can clamp and adapt to tree roots with different diameters.

The invention relates to the technical field of electronic equipment, in particular to a shell, a preparation method thereof and electronic equipment. The shell comprises a shell base body, a covering layer, a first decorative film and a second decorative film; the shell base body comprises a shell main body, a first connecting part and a functional module decoration part, and the functional module decoration part is located on the shell main body and connected with the shell main body through the first connecting part; the inner surface of the first connecting part comprises a first cambered surface; the covering layer is located on the first cambered surface; the first decorative film covers at least part of the inner surface of the shell main body and extends to cover the covering layer; and the second decorative film covers at least part of the inner surface of the decorative part of the functional module and extends to cover the covering layer. The problem that bubbles are likely to be generated during attachment of the decorative film due to evolution of shell modeling can be solved.

The invention discloses a solid waste monitoring and data management system based on artificial intelligence, and relates to the technical field of water environment monitoring, the system is composed of an image acquisition module, a data preprocessing module, an encoder feature extraction module, an up-sampling fusion module, a detection head module and a data management module, and the system is based on an RW-YOLOv11 architecture. A C3K2Sc feature extraction unit is introduced into an encoder, and floating garbage edge and texture expression is enhanced through space attention and a dynamic channel reconstruction mechanism; a SurfCAU water surface content awareness enhanced up-sampling module is adopted in the neck network, detail compensation of low-resolution features is achieved, and the multi-scale feature fusion quality is improved; a SurfMSDFHead multi-branch structure is adopted in the detection head, multi-scale feature fusion is realized through dynamic weight, and the positioning precision of overlapped garbage is improved by using a Focaler-IOU interval weighting strategy. The system can realize real-time detection, classification and positioning of the floating garbage in the river channel, and has the advantages of high detection precision, good lightweight degree, strong adaptability to complex water surface scenes and the like.

The invention relates to the technical field of biological medicine, and discloses a multi-immunohistochemical staining and field E staining combined comprehensive identification method, which comprises an image data acquisition module, a data analysis module, a data identification module and a data management module, the data analysis module calculates a data speed increase index Siy, a multi-immunohistochemical staining combined field E staining recognition accuracy index Ziy and a comprehensive efficiency increase index Wiy, the data recognition module judges the data transmission speed of the system according to the speed increase index Siy, and the data transmission speed of the system is judged according to the multi-immunohistochemical staining combined field E staining recognition accuracy index Ziy. The data accuracy after the multi-immunohistochemical staining unit and the field E staining unit are combined to work is judged, the comprehensive efficiency of the system is evaluated according to a comprehensive efficiency improvement index Wiy, and the data management module automatically issues a repair instruction to a unit area needing to be repaired according to the judgment data of the data identification module.

The invention discloses an RAW domain image edge strengthening method and device based on a lightweight neural network, and the method comprises the steps: inputting an RAW format image which needs to be sharpened into a trained convolutional neural network model, classifying the inputted RAW format image through a classification module, and outputting the group information corresponding to the image; and then, according to the group information, calling a corresponding group of convolutional neural network weight parameters in an external memory, after combining with a sharpening module structure in a convolutional neural network model, sharpening the RAW format image, and outputting an RGB format image. The convolutional neural network is used for replacing a traditional image sharpening algorithm, the advantages of four traditional image sharpening algorithms can be integrated, and the method is suitable for different types of scenes. In addition, according to the method, the convolutional neural network algorithm is lightened, the calculation amount is reduced, and possibility is provided for edge end calculation.

This invention discloses an image enhancement method and system based on improved MSR and CLAHE, belonging to the field of image enhancement technology. The method involves extracting the reflection components from each channel using the improved MSR algorithm, quantizing them back to 0-255 to obtain R1, G1, and B1, and then performing CLAHE on the green channel G1 and the blue channel B1 to obtain G2 and B2, resulting in a color image img2. Guided filtering is then applied to img2, and a multi-scale method is used to extract and enlarge its detail information, achieving detail preservation. Finally, the image is converted to the HSV color space, and adaptive gamma correction is performed on the V channel to improve image brightness. The image enhancement method provided by this invention solves the problems of insufficient detail highlighting, low contrast, uneven brightness, and color distortion in existing medical image enhancement methods for cervical cancer images.

This invention belongs to the field of deep learning and image processing technology, and discloses a learnable tensor low-rank enhancement method for near-infrared images under low illumination. Based on the estimation results of the local brightness mean and noise variance of the input image, the image is non-uniformly adaptively divided, and a three-dimensional local feature tensor containing spatial height, width, and brightness channels is constructed on each image block using a lightweight convolutional feature extraction network. Subsequently, based on the brightness energy of the image block, the tensor decomposition rank of each block is adaptively determined; factor generation networks and kernel tensor prediction networks are used to generate directional factor matrices and kernel tensors, respectively; threshold suppression is applied to the singular values ​​of the kernel tensor to remove noise subspaces, and scaling of high-frequency directional factors is combined to enhance texture details, effectively restoring degraded edge structures under low illumination conditions. Finally, a weighted fusion method based on minimizing the brightness difference in overlapping regions is used to reconstruct the entire image from the restored image blocks.

The invention provides a circuit breaker appearance defect rapid detection method based on image recognition. The method relates to the technical field of image recognition, and comprises the steps that image data of the appearance of the circuit breaker are collected in real time through a camera, and the image data comprise overall shape data, detail feature data and defect areas of the appearance of the circuit breaker. According to the circuit breaker appearance defect rapid detection method based on image recognition, rapid and accurate detection of circuit breaker appearance defects is achieved through the image adaptive filtering technology, the physical modeling technology, the multi-scale feature extraction technology and other technologies. Specifically, the image quality is improved by using an image adaptive enhancement technology, then macroscopic and microscopic features are effectively extracted through a multi-scale feature extraction method, and finally a comprehensive feature image is formed in combination with a weighted fusion method. According to the method, the recognition capability of complex and irregular defects can be effectively improved, and a high-precision defect detection report is generated through a machine learning model.

This invention provides a method for generating lace texture images based on an ADM (Adaptive Demographic Model) and dynamic denoising filtering model. This model combines a learnable Gaussian blur layer with a high-pass filtering technique. The Gaussian blur layer adaptively adjusts the blur intensity based on the input image, reducing high-frequency noise in the lace texture image. The high-pass filtering technique enhances key areas such as the outline and edges of the main pattern in the image, thereby highlighting the main texture features. Adding this module to the downsampling layer of the ADM network improves the overly smooth appearance of the lace texture images generated by the original model and enhances the image's texture depth.