2536 results about "Spatial domain" patented technology

The invention provides an unmanned aerial vehicle identification method and system for low-altitude security and protection. An optical compensation parameter set for unmanned aerial vehicle imaging optimization is generated in real time through a joint optimization model of an ambient light intensity change rate and a background motion vector field, and an original optical sequence in a target capture window is processed by using the parameter set. And constructing a time domain deconvolution kernel in combination with the motion characteristics of the unmanned aerial vehicle to generate an enhanced optical image resistant to motion blur. Non-linear weighted fusion is carried out through a disturbance intensity evaluation function, and a confrontation disturbance feature mask is formed. The enhanced optical image and the confrontation disturbance feature mask are subjected to airspace superposition operation, a multi-scale residual network is adopted to carry out target confidence estimation on the superposed image, an unmanned aerial vehicle recognition result is generated, and the unmanned aerial vehicle recognition accuracy and the anti-interference capacity in the low-altitude complex environment are remarkably improved through the technical scheme provided by the invention.

The invention discloses an inspection method, device, equipment and medium based on a digital airspace system, and the method comprises the steps: carrying out the feature extraction and calibration of multi-source sensing monitoring data of a to-be-inspected region through a pre-trained neural network model; obtaining a structured semantic description and a time sequence measurement value to be combined with external meteorological spatial data to be input into a digital airspace expert model based on an MoE framework for multi-modal fusion; carrying out enhancement processing on the generated multi-modal fusion representation through a variational auto-encoder so as to input the multi-modal fusion representation into a multi-modal transformer decoder for decoding, and obtaining an entity triple set to construct a digital spatial domain knowledge graph; node characterization in the knowledge graph is enhanced by adopting an attention enhancement mechanism based on a graph neural network, the node characterization is input into a conditional generative adversarial network model for multi-task prediction, and an inspection decision suggestion is generated to be executed by the unmanned aerial vehicle, so that the utilization rate of multi-source sensing data is effectively improved, and the inspection efficiency of the unmanned aerial vehicle is improved.

The invention relates to the technical field of wireless signal detection, and discloses a wireless anti-interference signal detection method, device and equipment and a storage medium, and the method comprises the steps: collecting a standardized interference feature vector through a multi-scene wireless signal receiving device; performing interference feature enhancement processing on the standardized interference feature vector to obtain an interference feature sample set; constructing a dual-array cooperative detection system comprising a passive detection array and an active detection array, and performing real-time monitoring to obtain dual-path interference signal monitoring data; performing spatial domain and time-frequency domain dual feature extraction and fusion processing on the dual-path interference signal monitoring data to obtain a comprehensive interference feature; the receiver filtering parameter matrix and the detection threshold vector of the double-array cooperative detection system are dynamically adjusted according to the comprehensive interference characteristics, the method can adapt to changes of different interference environments in a self-adaptive mode, comprehensive coverage of communication spectrums is achieved, and the wireless anti-interference monitoring range and the spectrum sensing capacity are greatly expanded.

The invention discloses a PCB three-proofing coating quality detection method and system. The method comprises the steps that a visible light reflection image of the surface of a PCB and scattering spectrum data of a near-infrared band are acquired; performing spatial filtering processing on the visible light reflection image to extract interface area pixels, calculating coating layer thickness gradient distribution based on Mie scattering characteristics in scattering spectrum data, and fusing to generate an edge gradient distribution map; and according to the gradient amplitude change rate in the edge gradient distribution map, segmenting the effective coverage area of the coating layer by adopting a self-adaptive dynamic threshold algorithm, and extracting curvature extreme point density and boundary fractal dimension parameters at the segmentation boundary. According to the invention, three technical barriers of difficult microdefect capture, unmeasurable interface performance and delayed failure risk in traditional detection are solved, and two-dimensional accurate diagnosis of the structural compactness and interface reliability of the coating layer is realized.

Disclosed in the present invention are a hyperspectral calculation imaging method and system based on multi-domain fusion of spatial, spectral and frequency domains, and a medium. The method of the present invention comprises: using a two-dimensional offline discrete cosine transform (DCT) to convert an RGB image Y into a frequency domain to obtain a frequency domain feature map Yfreq; extracting a frequency information image (I) from the frequency domain feature map Yfreq; using a two-dimensional offline inverse discrete cosine transform (IDCT) to transform the frequency information image (I) into a spatial domain to obtain a frequency information image Xfreq of the spatial domain; and fusing the frequency information image Xfreq of the spatial domain into the spatial-spectral domain features of the RGB image Y to generate a hyperspectral image (II). The present invention aims to solve the problems of poor detail information and low reconstruction accuracy of hyperspectral images in existing hyperspectral calculation imaging, and realizes high-fidelity reconstruction of a target spectrum.

The invention relates to the technical field of computer vision detection, in particular to an unmanned aerial vehicle target detection method based on frequency-space joint attention and dynamic fusion, and the method comprises the steps: obtaining an unmanned aerial vehicle image data set, carrying out the preprocessing, and dividing a training set and a test set; constructing a target detection model, inputting the training set into the target detection model to extract image features, sequentially performing frequency domain detail enhancement, spatial domain salient region extraction and multi-scale feature adaptive fusion based on the image features, and establishing a feature sequence; screening the feature sequence to obtain an initial target query, and finishing target classification and positioning on the initial target query through a decoder; training a target detection model by using the training set, and inputting the test set into the trained target detection model to generate a detection result; on the premise that the real-time reasoning advantage of RT-DETR is kept as much as possible, the problems that in an unmanned aerial vehicle scene, a target is prone to missing detection, the scale change is large, the background is complex, and the target is fuzzy are effectively solved, and the detection precision is improved.

The invention relates to the technical field of medical image processing, and provides a medical image segmentation method based on wavelet enhancement and multi-scale feature fusion. According to the method, a CNN-Transform double-branch coding structure is combined, a multi-scale wavelet fusion module is provided, from the perspective of a frequency domain, Haar wavelet transform is adopted to extract an image high-frequency sub-band so as to enhance edge and texture detail expression, dynamic weighting is performed on different frequency band features through grouping convolution and a sub-band attention mechanism, and the discrimination capability is improved; meanwhile, a multi-scale cavity pyramid structure is fused in a spatial domain, and after cross attention dynamic fusion is introduced, a feature alignment mechanism of a wavelet domain and the spatial domain is established; and collaborative fusion of frequency domain and space domain features is realized. The method effectively improves the segmentation precision of the fuzzy boundary and the fine-grained structure under the complex background, has good universality and adaptability, and is suitable for various medical image segmentation tasks.

The invention relates to a three-dimensional reconstruction method and model, in particular to a dynamic self-adaptive multi-view three-dimensional reconstruction method and model based on double-domain information fusion, and aims to solve the problems that in the prior art, the space relation between pixels is explored by utilizing attention along a channel dimension, the self-adaptive long-range information association capability is lacked, and the space relation between pixels is not explored. According to the dynamic adaptive multi-view three-dimensional reconstruction method based on double-domain information fusion, spatial domain detail enhancement and frequency domain dynamic aggregation and enhancement are realized through a multi-scale spatial domain and frequency domain information fusion network, so that multi-scale enhancement characteristics of an input image are obtained; a multi-scale depth prediction network is constructed by adopting a coarse-to-fine framework, depth hypotheses of all scales are obtained, a cost body is generated through adaptive aggregation, finally regularization is carried out to obtain prediction depth, and the prediction depth is converted to obtain a three-dimensional point cloud file of a corresponding scene. Meanwhile, the invention provides a model capable of realizing the method.

The invention discloses a low-light image enhancement method based on multi-scale frequency domain guidance and a double-branch attention mechanism. According to the method, a low-light image and a normal image corresponding to the low-light image serve as input, and illumination mapping and illumination features are extracted through a layer decomposition network; a reflection image mapping relation is learned in combination with a damage recovery network, and a preliminary enhanced image is generated; a learnable Fourier transform module is provided to realize frequency domain feature extraction, and low-frequency illumination information and high-frequency details are effectively separated; a dynamic double-branch attention mechanism is put forward, spatial domain and frequency domain features are fused, and collaborative modeling of structure and illumination perception is achieved; a multi-scale feature module is proposed to combine channel rearrangement and residual fusion to optimize feature expression; a U-Net framework is combined with a double-branch large kernel activation attention mechanism to complete image reconstruction, cross-scale feature fusion and detail recovery are achieved, and finally an enhanced image is output. According to the method, the problems of uneven illumination, detail loss, noise interference and the like in the low-light image can be effectively solved.

The invention discloses a space-time consistent multi-modal feature fusion-based aerial target detection method. The method comprises the steps of 1, obtaining a visible light and infrared image pair sequence of an aerial target; 2, constructing a space-time consistent air target detection network, wherein the space-time consistent air target detection network comprises a time domain enhanced backbone network, a space domain fusion module, a bidirectional feature pyramid and a Head module; 3, constructing a total loss function; and 4, training to obtain an optimal target detection network model, carrying out multi-scale detection on the space-time consistency characteristics, finally outputting the position and the category of the target, and returning a result with a detection frame. According to the invention, through space-time consistency modeling and cross-modal attention fusion, the detection precision and robustness of the aerial target in a complex scene are effectively improved, and meanwhile, a lightweight design is adopted to meet the real-time requirement, and the method is suitable for the fields of unmanned aerial vehicle supervision, low-altitude security and protection and the like.

The invention relates to the technical field of image denoising, and particularly discloses a dual-domain heterogeneous image denoising method, which comprises the following steps: extracting a preliminary feature map based on depth separable convolution; an encoder of a double-domain heterogeneous cooperative architecture is adopted in a shallow layer of a hierarchical double-drive encoding and decoding architecture to perform double-domain heterogeneous cooperative processing, through hierarchical feature adaptation, the shallow layer gives consideration to details and local structures, a deep layer focuses on global semantics, and dynamic allocation of computing resources is performed, so that the redundant computing burden is remarkably reduced; and splicing the processed image frequency domain information and the image space domain information, fusing features of each layer after hierarchical processing based on a vertical stripe perception fusion attention mechanism module connected between an encoder and a decoder in a jumping manner, and outputting the fused features to the decoder to obtain the sensitivity of denoising image enhancement to vertical stripe noise. And a noise area is suppressed in a targeted manner.

The invention relates to a PET (Positron Emission Tomography) and MRI (Magnetic Resonance Imaging) multi-modal medical image fusion method and system based on a Kolmogorov-Arnold network. The method comprises the steps of collecting PET and MRI images for normalization processing, and extracting an initial feature map, a first scale feature map, a second scale feature map, a third scale feature map and a fourth scale feature map through a hierarchical feature extraction network of a multi-scale dynamic convolution kernel architecture; inputting the first-scale feature map, the second-scale feature map and the third-scale feature map into a KAN for pyramid coding, decomposition and reconstruction, multi-scale decomposition and other operations to obtain a decoded feature map; and inputting the fourth scale feature map into a frequency domain-spatial domain collaborative fusion framework based on dynamic sparse attention guidance for feature integration, performing frequency domain expansion based on reversible frequency domain up-sampling, and performing channel weighted splicing, spatial-channel decoupling processing and interactive fusion processing to obtain a final fused medical image. The image details can be obtained, noise can be effectively suppressed, and the accuracy and reliability of the fused image are improved.

The invention discloses a semantic segmentation method for a low-resolution road scene, and aims to solve the problems of difficulty in small target recognition, fuzzy details, texture information loss and the like existing in a low-resolution image in the conventional semantic segmentation technology. The method comprises the following steps: (1) collecting a low-resolution road scene image and a corresponding semantic tag; (2) constructing a semantic segmentation model consisting of an edge guidance module (BGM), a double-domain feature decomposer (DDFD), a domain alignment attention fusion module (DAAFM) and a double-layer attention context aggregation module (HACAM); (3) designing a joint loss function to carry out multi-scale supervision on semantic regions, edges and middle features; (4) carrying out model training by utilizing the road scene image; and (5) outputting a semantic segmentation result map and an edge prediction map. The boundary perception capability is enhanced by introducing learnable pixel difference convolution, the extraction precision of a small target and a global structure is improved by combining frequency domain and spatial domain feature alignment, and context semantic relationship expression is optimized by fusing a channel and a spatial attention mechanism. The method effectively improves the semantic segmentation precision and boundary restoration capability of the model in a low-resolution complex road environment, and is suitable for intelligent analysis tasks of road images in scenes of automatic driving, intelligent traffic, severe weather and the like.

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 provides a modified metal surface defect detection method and device and a medium, and the method comprises the steps: obtaining a multi-angle reflection image sequence of a modified metal surface under the irradiation of a multi-spectral light source, and generating a defect sensitive parameter set based on a preset modified metal material characteristic database and in combination with the spectral reflectivity distribution information of the multi-angle reflection image sequence; performing cooperative feature enhancement on the multi-angle reflection image sequence and the defect sensitive parameter set, and enhancing the feature contrast of a defect area and a normal area through weight distribution to obtain an enhanced defect feature set; joint anomaly detection of a spatial domain and a spectral domain is carried out on the enhanced defect feature set, a potential defect region of the modified metal surface is obtained through identification, and a defect region feature descriptor is generated; and performing defect morphological quantitative analysis according to the defect region feature descriptors, and determining the type, position and severity level of the modified metal surface defect. According to the invention, the comprehensiveness and reliability of a defect detection result can be improved.

The invention discloses a space-frequency domain graph learning-based adaptive deep fake face detection method and system, and the method comprises the steps: randomly extracting an image frame from a video, intercepting a face image, adjusting the feature dimension of the face image, and transmitting the face image to a depth adaptive wavelet module and a normalized residual homomorphic composition neural network module; a depth adaptive wavelet module extracts frequency features of the face image; a normalized residual homograph neural network module extracts spatial domain features of the face image; performing weighted fusion on the frequency domain features and the spatial domain features by using a self-adaptive feature fusion module based on gated convolution, realizing class attention guidance by using the gated convolution, dynamically adjusting the channel of a feature map and the weight of a spatial dimension, and finally obtaining fusion features; and performing classification according to the fusion features by using a classifier. According to the method, the extraction capability of forged detail clues is enhanced, and the detection precision and stability of the model are remarkably improved.

The invention discloses an unmanned aerial vehicle target detection method based on a cross-spatial frequency domain and electronic equipment, and belongs to the technical field of image detection. According to the method, an unmanned aerial vehicle target detection network based on a cross-space frequency domain is constructed to carry out unmanned aerial vehicle target real-time detection; the unmanned aerial vehicle target detection network is composed of a cross-space frequency domain feature extraction network, a detail information hybrid encoder and a position relation decoder; inputting the unmanned aerial vehicle target image into a cross-space frequency domain feature extraction network, and extracting multi-scale frequency domain features of the unmanned aerial vehicle target image; performing multi-scale feature fusion and feature enhancement to obtain a multi-scale feature map; and the position relation decoder generates a target category and bounding box coordinates according to the multi-scale feature map in combination with the relative position relation features between the targets. According to the method, complementary information of a spatial domain and a frequency domain and potential spatial position relation characteristics between targets can be fully utilized, and the target detection precision and robustness of the unmanned aerial vehicle under a complex background are remarkably improved.

The invention provides a method for multi-domain division and conquering of super-resolution of an infrared image of an unmanned aerial vehicle based on Mamba. The progressive optimization of the super-resolution result is realized by fusing the feature interpretation of the spatial domain and the frequency domain. The method comprises the following steps: firstly, capturing a long-range spatial dependency relationship through a vision-oriented state space module; then capturing the local feature and texture information of the image through the synergistic effect of a wavelet transform branch, a global branch and a local branch of a feature mapping module based on wavelet transform; finally, for challenges of modal difference and semantic alignment, complementary interaction and fusion of the features are achieved through a cross attention mechanism of a multi-domain attention fusion module, the characterization capacity of global and local features is enhanced, and therefore the robustness of the model is improved.

The invention discloses a multi-modal electroencephalogram classification method under multi-source interference based on a multi-head attention mechanism, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: synchronously acquiring three types of electroencephalogram modal signals of motor imagery, steady-state visual evoked and event-related potentials, and synchronously acquiring multi-source interference data; performing data preprocessing on the multi-mode signal data; time domain, frequency domain and spatial domain three-dimensional features of each electroencephalogram mode are extracted, and meanwhile, physical features of interference data are quantified; the multi-domain features of the three types of electroencephalogram modes serve as parallel query vectors, the multi-source interference features serve as key value vectors, interference suppression weights are dynamically distributed through a layered attention mechanism, and anti-interference enhanced multi-mode fusion features are generated; and the multi-modal fusion features are processed through a gating circulation unit and a time domain attention module, a multi-modal classification result is output, and online learning and weight updating are carried out. According to the method, a robust decoding scheme can be provided for a brain control interaction system in a complex environment.

The invention provides a multispectral fusion-based valve optical image enhancement identification method and system in severe weather, and the method comprises the steps: obtaining multispectral optical image data and mechanical dynamic response characteristics of an industrial pipeline valve in severe weather; based on the mechanical dynamic response characteristics, separating the multispectral optical image data to extract multiband texture characteristics associated with the reflection characteristics of the valve material; double amplitude correction of a spatial domain and a frequency domain is carried out on the multiband texture features; distributing a spectrum fusion weight for the multispectral optical image data according to the corrected multiband texture features; and generating an enhanced optical image matched with the actual mechanical state of the valve based on the spectrum fusion weight and the corrected multiband texture features. According to the method, the recognition accuracy of defects such as valve surface cracks and deformation in severe weather is improved.

The invention provides a deep counterfeit multi-label sorting and positioning method based on multi-domain feature fusion. The deep counterfeit multi-label sorting and positioning method comprises the following steps: preprocessing a sequence deep counterfeit data set; inputting the human face RGB image into a DINOv2-based spatial feature extractor to carry out spatial domain feature extraction; a face RGB image is decomposed through discrete cosine transform to obtain a plurality of frequency domain coefficients and frequency spectrums, the obtained frequency spectrums are flattened and remodeled, components with the same frequency are grouped into a channel to form new input, and then the original color input of the face RGB image is finally converted into frequency domain feature representation through an encoder. The spatial domain features and the frequency domain features are utilized, splicing fusion operation is carried out through a cross attention module, new multi-domain representation of the face deep counterfeit image with the spatial domain and the frequency domain is formed, and then the multi-domain representation is input into a graph attention network to capture the complex relation between different features. According to the method, a more accurate deep counterfeiting analysis result can be realized.

The invention discloses a shock wave overpressure field global measurement method based on multi-view image fusion, belongs to the technical field of explosive shock wave measurement, and is suitable for weapon equipment power evaluation and blasting safety analysis. The method comprises the following steps of: synchronously acquiring a time sequence image of the whole explosion process through a distributed multi-view high-speed imaging system, preprocessing the image by adopting pixel-by-pixel comparison, square operation enhancement and normalization processing, and positioning a shock wave edge contour; the blasting center coordinate is positioned through binocular parallax, the shock wave initial radius is determined by combining spatial domain analysis, and the key feature points of the front and rear edges of the wavefront are detected by using a dynamic search window and a radial gradient field. Based on multi-view constraints, a three-dimensional point cloud is generated through direct linear triangulation, and a three-dimensional wave front form is reconstructed in combination with least square spherical fitting. And finally, constructing a wavefront radius-time evolution model, and deducing a quantitative relationship between the instantaneous propagation velocity and the peak overpressure in combination with a Ranki ne-Huton iot relationship, thereby realizing the global high-precision calculation of the overpressure field.

The invention discloses a transcriptomics spatial domain identification method, and belongs to the technical field of transcriptomics. The objective of the invention is to solve the problems of low data noise reduction precision and poor recognition effect of an existing spatial transcriptional spatial domain recognition method. The method comprises the following steps: firstly, obtaining an undirected neighborhood graph according to a gene expression matrix, obtaining embedded representation of the gene expression matrix by utilizing an encoder, obtaining a corresponding reconstruction matrix by utilizing a decoder, and further determining reconstruction loss; meanwhile, a ZINB model is used for fitting a reconstruction matrix, and a ZINB loss function is obtained; then, an augmented graph is constructed based on the undirected neighborhood graph, respective embedded matrixes are obtained through an encoder, the comparison loss of the undirected neighborhood graph and the comparison loss of the augmented graph are obtained through a comparison representation learning mechanism, and then the neighbor comparison loss is obtained; total target loss is obtained based on all losses, a joint optimization strategy is adopted for training, and after training of the whole model is completed, dimensionality reduction and spatial domain recognition are carried out on a generated reconstruction matrix.

The invention discloses a road traffic sign target detection method based on YOLOv8, and relates to the field of road target detection, in the method, a CDFF-YOLO model is constructed, and an MPA module is embedded in a feature fusion network of the model to enhance the feature fusion capability of C2f; an MSF module is constructed, GSConv and CARAFE up-sampling operators are embedded to realize a multi-dimensional sequence information fusion effect, and the detection precision of the model is improved while the parameter quantity and the calculation quantity of the model are reduced as much as possible. A DFF module is constructed, two-dimensional Fourier transform and inverse transform methods are adopted to enable feature information to be enhanced in a frequency domain and a space domain at the same time, and the detection performance of a network model for shielded small targets and small targets under uneven illumination is effectively improved. The three modules not only enhance the detection effect of the small target model, but also enable the network to stably cope with the detection scene of the small target. According to the method, the detection performance of the small road traffic sign target is excellent, the processing speed is high, and deployment of mobile terminal equipment is facilitated.

The invention discloses a cross-modal target detection method based on learnable Fourier transform, and mainly solves the problem of insufficient fusion of a visible light image and an infrared image in a complex scene due to inter-domain difference in the prior art. According to the implementation scheme, the method comprises the steps that bimodal features are extracted through a double-flow CSPDarknet53 network; a target position guiding module is utilized to enhance target area representation and suppress background interference; the features are converted to a frequency domain, and amplitude texture information of the visible light image and phase contour information of the infrared image are adaptively enhanced through a learnable frequency domain feature enhancement module; suppressing noise through global filtering and then inversely transforming back to a spatial domain; and finally, outputting a target detection result of the multi-modal image by the detection head. According to the method, frequency domain physical characteristics are fully utilized, full complementation and adaptive fusion of cross-modal features are realized, the detection precision and robustness of vehicles, pedestrians and other targets under low-illumination and complex backgrounds are remarkably improved, meanwhile, high calculation efficiency is kept, and the method can be applied to the fields of automatic driving, intelligent monitoring and the like.

The invention provides an end-to-end tiny target detection method, and aims to solve the problems of missing detection and false detection of tiny targets caused by interference of sparse features, halo, noise and the like. According to the method, a TINYDETR model is constructed, and the TINYDETR model is composed of an HGNetv2 backbone network, an LGFSI module, an SO-CSFF module and a decoder with an auxiliary prediction head. Wherein the LGFSI module realizes global-local information interaction through joint modeling of a frequency domain and a spatial domain, and background interference is effectively suppressed; the SO-CSFF module enhances the fusion of shallow details and deep semantics through a bidirectional feature flow mechanism, and enhances the feature expression of a tiny target. After the model is trained and optimized, high-precision detection of a tiny target can be realized.

The invention discloses an overlapped cervical cytoplasm region segmentation method based on deep learning and a conditional diffusion model, and relates to the technical field of artificial intelligence analysis of medical images. According to the method, accurate segmentation of the overlapped cytoplasm region in the cervical cell image is realized through a morphological prior guided conditional diffusion process. The method comprises the following steps: constructing a multi-scale cervical cytoplasm mask pair image; designing a cytoplasm specific data enhancement and preprocessing process; building a multi-branch cervical cell morphology perception condition diffusion network; using a self-adaptive multi-scale combination loss function to optimize training; and a hierarchical classifier is adopted to freely guide sampling for reasoning. According to the method, the frequency domain and space domain features are fused, a cellular morphology and statistics priori knowledge base is established, and a strategy of generating complete cytoplasm by adopting non-overlapped parts is adopted, so that the problem that the traditional method is difficult to segment in complex backgrounds and overlapped regions is successfully solved, and reliable technical support is provided for early screening of cervical cancer.

The invention relates to the technical field of remote sensing landform enhancement, and discloses a remote sensing landform enhancement algorithm, which comprises the following steps of: S101, preprocessing an existing high-resolution DEM (Digital Elevation Model) image and an existing low-resolution DEM image to construct a training data set; step S102, constructing a double-domain multi-scale attention fusion super-resolution network; step S103, training the double-domain multi-scale attention fusion super-resolution network through the training data set; according to the method, the edge information and spatial continuity characteristics of the DEM image are comprehensively considered, the high-frequency and low-frequency information of the DEM image is extracted through Haar wavelet transform by utilizing the characteristic that a wavelet domain is highly sensitive to the high-frequency and low-frequency information, and spatial domain feature information obtained by a parallel branch convolutional layer is combined, so that the high-frequency and low-frequency information of the DEM image is obtained. Through fusion of the two, the network has a feature enhancement function in a shallow layer, and meanwhile, the importance relationship between different depth feature maps is obtained through the multi-scale attention fusion module group, so that the precision of DEM super-resolution reconstruction is improved.

The invention discloses a flexible stone texture defect identification method based on a multi-scale convolutional neural network, and the method comprises the following steps: collecting images of the surface of a flexible stone, and carrying out the batch classification; selecting a first image of each production batch as a batch first sample, and generating batch configuration parameters; performing texture feature extraction by using the batch configuration parameters and the to-be-detected image to generate a texture map; respectively inputting the to-be-detected image into a spatial domain convolution branch and a frequency domain convolution branch of the space-frequency neural network model, and extracting spatial domain features and frequency domain features according to the scale control information; the spatial domain features and the frequency domain features are fused; and generating candidate areas based on the fused features, performing positioning and confidence evaluation, removing the candidate areas with confidence smaller than a preset threshold, and generating a flexible stone texture defect detection result. According to the method, the surface defects of the flexible stone can be accurately detected, the detection efficiency and robustness are improved, and the manual detection cost is reduced.

The invention relates to the technical field of dynamic weighing, and discloses an intelligent load prediction and adjustment method in a dynamic weighing system, which comprises the following steps of: separating a weighing signal into a space domain static load characteristic (pressure point distribution sparsity and gravity center geometric vector) and a time domain dynamic inertial interference component (acceleration coupling noise) through space-time double-domain decoupling; the frequency spectrum aliasing problem of a traditional filtering algorithm is eliminated from a signal source; the future interference trend is predicted based on the real-time motion state of the carrier, a feed-forward compensation signal is generated through a pre-training dynamic basis function library, and dynamic errors are actively counteracted; in combination with sensor array space consistency detection and lightweight online learning, decoupling model parameters are dynamically corrected. According to the method, space geometric analysis and kinematics feedforward prediction are combined, a traditional signal processing framework is strided out, error source separation and real-time compensation are achieved, and the dynamic weighing precision of transient working conditions such as sudden stop and speed change is remarkably improved.