301 results about "Pyramid (image processing)" patented technology

The present invention pertains to the technical field of digital image processing. Disclosed are an unsupervised super-pixel segmentation method and system assisted by collaboration between an atrous pyramid and an attention mechanism. The method comprises: combining image RGB channel information with position information of a pixel point; constructing a channel attention module by using an attention mechanism; processing the result of the attention mechanism by using atrous spatial pyramid pooling; constructing a loss function, constructing a clustering loss term, and, by using a spatial smoothing loss term, constructing a reconstruction loss term; updating model parameters by using an Adam optimizer, and using for super-pixel generation an effective depth feature obtained from the last separation; and obtaining the maximum value of a channel dimension by using an argmax function, converting a processing result of the argmax function into a two-dimensional array, and, on the basis of a defining condition, completing adaptive super-pixel segmentation in a CPU. According to the present invention, the complexity is low, the adaptive and generalization capabilities are high, and effective support is provided for improving image processing efficiency and accuracy.

The invention discloses a coal mining subsidence area surface crack intelligent extraction method based on an improved Transform model, and belongs to the technical field of remote sensing image processing. Firstly, an unmanned aerial vehicle carrying a high-resolution optical camera is used for collecting images, the image overlapping rate of 70%-80% is guaranteed, and a training data set is constructed through professional labeling, cutting screening and data enhancement. The encoder of the innovative model is very distinctive, and the adaptive multi-scale patch mapping layer can dynamically adjust the patch size according to the local complexity of the image and efficiently extract features; double-attention fusion is combined with optimization position coding, and long-distance dependency capture is enhanced; and the calculation amount and the overfitting are reduced by the dynamic sparse connection full-connection layer. Residual attention enhancement pyramid pooling and a space-channel attention bottleneck mechanism are adopted, key features are highlighted, and noise is suppressed; and a breakpoint detection and connection rule determination module is utilized to realize complete restoration of the ground fracture. After the data set is used for training a model, deployment is carried out, and through preprocessing, encoding and decoding and post-processing, ground fracture information can be accurately obtained, and a data foundation is built for mining area safety management and geological disaster prevention and control.

The invention relates to the field of medical image processing, in particular to a lung cancer PET-CT fusion segmentation method and system based on multi-modal feature comparative learning, and the method comprises the steps: firstly extracting PET and CT image features, projecting the features to a shared semantic space through a semantic guide type symmetric comparative learning architecture, obtaining key region features through a focus adaptive attention sampling mechanism, and carrying out the segmentation of a target region; optimizing feature representation through a cross-modal feature difference self-calibration mechanism, constructing a multi-scale feature pyramid, fusing features of different scales by using a multi-scale hierarchical contrast learning mechanism, and performing self-supervised learning by combining an anatomical guidance self-supervised contrast learning enhancement module and using a CT anatomical structure, so as to further reinforce the features; a high-precision lung cancer lesion segmentation result is generated through a decoder network, the Dice coefficient is increased from 0.78 to 0.91, and the detection rate of lesions below 10 mm is increased from 65% to 87%. A novel efficient and accurate image processing method is provided for lung cancer diagnosis.

The invention relates to an underwater image processing technology in the field of underwater autonomous perception, in particular to an underwater low-quality image enhancement method based on a Laplacian pyramid and contrast learning. Comprising the following steps: S1, image input and multi-scale decomposition: decomposing an input image into a low-frequency residual layer and a plurality of high-frequency detail layers through Laplacian pyramid decomposition; s2, inputting the low-frequency residual error layer into a global illumination and color correction sub-module to obtain an enhanced low-frequency residual error layer; s3, inputting the enhanced low-frequency residual error layer and the multi-scale high-frequency detail layer into a frequency domain enhancement feature module to obtain multi-scale enhanced high-frequency detail layer output; s4, performing progressive reconstruction on the enhanced low-frequency layer output and the high-frequency enhancement layer output of each scale according to the inverse process of the Laplacian pyramid; and S5, outputting a result and applying. The method can be used for sharpening optical image data in the operation task of the autonomous underwater vehicle, and the image quality is improved.

The invention relates to the technical field of computer image processing, and particularly discloses a double-branch defogging method based on a Laplacian pyramid, and the method comprises the following steps: S1, building a training data set A containing an original clear image and a corresponding foggy image; s2, performing cutting and data enhancement processing on the input image; s3, a defogging network LPSDNet based on the Laplacian pyramid is constructed, the defogging network LPSDNet comprises a multi-feature fusion focusing branch and a Laplacian pyramid supplementary branch, and after feature extraction of the two branches is completed, features learned by the two branches are fused through a learnable fusion module; s4, training the defogging network LPSDNet by adopting the training data set A until a preset loss function is converged; obtaining a trained defogging network LPSDNet in combination with an objective function; and S5, inputting an image to be defogged into the trained defogging network LPSDNet for testing and verification to obtain a defogging result. The global structure information of the image can be fully captured, and the distortion phenomenon is prevented.

The invention belongs to the technical field of image processing, and discloses a frame image and event stream feature fusion-based target detection method, which comprises the following steps of: acquiring RGB (Red, Green, Blue) images and event stream data, and constructing a time-space synchronous multi-modal data pair; constructing a double-flow feature extraction backbone network to extract events and RGB features; based on a cross attention mechanism, multi-head attention is utilized to realize semantic alignment; the fusion weight is adaptively adjusted according to the statistical distribution; a multi-level fusion network is constructed, cross-scale fusion is performed by using an FPN pyramid, and target positioning and classification feature expression are cooperatively enhanced through a spatial semantic aggregation module. According to the target detection method based on frame image and event stream feature fusion, through a multi-modal data collaborative perception and self-adaptive feature optimization mechanism, the detection robustness in a complex scene is remarkably improved, the dynamic target omission ratio and the error recognition rate are effectively reduced, and the target detection efficiency is improved. And an all-weather high-precision environment perception capability is provided for an automatic driving system.

The invention discloses a visible light and infrared fusion target detection method and system for an all-time unmanned aerial vehicle scene, and belongs to the technical field of unmanned aerial vehicle aviation and intelligent image processing. The method comprises the following steps: analyzing an original visible light image to calculate average brightness and root-mean-square contrast, and quantifying scene illumination conditions; distortion correction and registration are carried out on the original image; a parallel backbone network is adopted to extract visible light and infrared twinborn feature maps, the number of channels allocated to two modal features is dynamically adjusted through 1 * 1 convolution according to illumination condition parameters, and then a dual-light fusion network containing a gated convolution block is utilized to perform weighted fusion; and target prediction is carried out through the progressive feature pyramid network. Through an illumination adaptive fusion strategy and targeted data enhancement, the precision and robustness of target detection in a complex all-day scene are significantly improved, and the method is suitable for real-time target detection tasks of the unmanned aerial vehicle.

The invention discloses a marine litter intelligent identification and classification method based on a multispectral unmanned aerial vehicle remote sensing image, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing a remote sensing image of a preset sea area, constructing a color correction network, and carrying out the color correction of the preprocessed remote sensing image; multiband features and spectral indexes of enhanced garbage detection are obtained, and multispectral features are generated through multi-scale feature pyramid structure fusion; training a conditional generative adversarial network, inputting a remote sensing image and multispectral features, and performing pixel-by-pixel fusion on the enhanced remote sensing image and the remote sensing image after color correction to obtain a multiband fusion feature map; and constructing a lightweight detection network based on the OfficientDet-Lite, taking the multi-band fusion feature map as input, and outputting a bounding box and a garbage category of garbage by adopting a joint detection-classification architecture. According to the method, the problems of color distortion, contour fuzziness, small target missing detection and the like in marine litter identification are solved, and meanwhile, the real-time performance of marine litter identification and classification is ensured.

The invention belongs to the technical field of computer vision and image processing, and discloses a remote sensing small sample target detection method based on double-attention guided transfer learning, and the method comprises the steps: obtaining a remote sensing image data set, and carrying out the preprocessing; taking the preprocessed remote sensing image training set as input, constructing a basic detection model by using a ResNet-101 backbone network, a feature pyramid network and a content awareness upsampling and regional proposal network, and obtaining basic model parameters; basic model parameters are used as input, a DA-FSDET network is trained based on a content awareness strip pyramid and a deformable attention area proposal network, and the trained DA-FSDET network is used to acquire a category detection frame containing small sample categories and confidence. Through cascading and cooperative work of the content awareness stripe pyramid and the deformable attention area proposal network, the detection precision and robustness of the multi-scale target in the remote sensing image are effectively improved.

The invention relates to the technical field of photovoltaic module detection, in particular to a TOPCon battery piece defect detection device and a TOPCon battery piece defect detection method. The optical imaging module comprises a 25,000,000-pixel camera, a three-color integral light source array and a multispectral optical filter group, and the light source array is composed of red, green and blue LEDs and supports pulse synchronous triggering; the traditional algorithm processing module is integrated with a Halcon image processing library and is configured with a VarThreshold operator dynamic threshold segmentation unit, an adjustable kernel size corrosion filtering unit and a light and shade separation unit, and an Erosion Rectangle1 operator is used for corrosion filtering; the AI defect detection module is internally provided with an improved YOLOv8 neural network model, and the model structure comprises a Backbone network embedded with a CBAM attention mechanism, a BiFPN network bidirectional feature pyramid and a dynamic loss function optimizer; according to the technical scheme, the limitation problems of low positioning precision, poor detection efficiency, single defect identification dimension and the like of an artificial vision or traditional probe contact detection means can be solved.

The invention provides an urban landscape semantic segmentation system adopting an EGLiteSeg model, and belongs to the field of image processing. Comprising three main parts: an encoder; an polymerizer; and a decoder. The EGLiteSeg framework adopts a lightweight encoder-decoder pipeline to carry out image processing, and an encoder of the EGLiteSeg framework is provided with a five-level DeSTDCNet framework. In the first four stages, depth separable convolution with the stride being 2 is utilized, and the AECA module automatically adjusts the size of a kernel according to the number of input channels so as to enhance the multi-scale feature adaptability. For context aggregation, the framework uses GDSPPM. The module processes features through deep convolution to enhance distinguishability, and then performs multi-scale pyramid pooling to capture hierarchical contextual information. The multi-scale features are connected together and fed into a decoder. The decoder is composed of two key components: a universal attention fusion module (UAFM) and a partition head. The method not only has high semantic segmentation accuracy, but also has superiority in real-time performance, and meets actual requirements.

The invention relates to the field of remote sensing image processing, in particular to a karst landform remote sensing dynamic evolution analysis method and a karst landform remote sensing dynamic evolution analysis system, which can be applied to the fields of karst region resource management, environmental protection, geological disaster early warning and the like. According to the method, karst landform change information is obtained through multi-temporal remote sensing image data, a time-space analysis model composed of self-attention enhancement, a feature pyramid and a recurrent neural network is constructed, a constraint module is designed in combination with karst landform priori knowledge, and accurate prediction is achieved; multi-source data fusion improves the feature capture capability, and the observation advantage is obvious especially under complex meteorological conditions; the multi-module cascade design is used for accurately modeling space-time dynamic change, and the prediction accuracy is improved; priori knowledge constraint is introduced to enhance feature recognition, and misclassification is reduced; on the whole, the method effectively integrates multidisciplinary technologies, the precision and reliability of karst landform dynamic evolution analysis are improved, and a powerful tool is provided for research in related fields.

The invention discloses a heart motion feature extraction method based on optical flow estimation, and relates to medical image processing. Preprocessing the input four-dimensional space-time cardiac magnetic resonance imaging data, namely, scaling pixel values; inputting two frames of images which are continuous in time into a feature encoder and a context encoder for feature extraction, wherein the two frames of images are divided into a reference frame and a moving frame; calculating the correlation between the feature maps through a correlation volume calculation module, constructing a correlation pyramid, and extracting the feature maps to provide matching information for subsequent optical flow estimation; the motion feature iteration enhancement module iteratively and continuously refines an optical flow estimation result through a deformable convolution and global motion aggregation (GMA) module; model parameters are optimized based on a weighted sum of luminosity consistency loss, smoothness loss, and gradient consistency loss. Edge features are adaptively captured through deformable convolution, global and local features are fused by using a GMA module, optical flow prediction errors are effectively reduced, motion estimation quality is improved, and key features of a heart edge region are maintained.

The invention relates to the technical field of remote sensing image processing, in particular to an end-to-end neural network InSAR (Interferometric Synthetic Aperture Radar) phase unwrapping method based on mixed attention, which takes U-Net as a basic framework, extracts key features and improves resolution through down-sampling and up-sampling operations, thereby effectively recovering detail information. Specifically, a convolutional block attention module (CBAM) and two receptive field modules, namely cavity spatial pyramid pooling (ASPP) and a receptive field module (RFB) are combined to construct an RFAUNet network model, and then simulation data sets generated by two methods of digital elevation inversion and random matrix generation are used for training a neural network until a good unwrapping effect is obtained. And finally, carrying out a phase unwrapping experiment on simulation data and real data to verify the effectiveness and robustness of the RFAUNet network model.

The invention relates to the technical field of image processing, in particular to a mining truck edge detection method based on a multi-scale edge membership map. The method comprises the following steps: 1, constructing a multi-scale image pyramid: acquiring a grayscale image as input, performing multi-scale pixel sampling, and performing fuzzy processing by using a Gaussian function to construct a Gaussian image pyramid; 2, gradient calculation: respectively calculating the gradient of the image in the x direction and the y direction by using a Scharr operator; 3, the standard deviation of the local gradient difference is calculated, edge pixels and noise points are further effectively distinguished by calculating the standard deviation of the local gradient difference through the statistical characteristics of the local gradient difference, and therefore the edge detection precision is improved; 4, calculating an edge membership degree; 5, fusing a multi-scale edge membership map; 6, adaptive lagging connection: adopting lagging connection to further process the multi-scale edge membership fusion image so as to improve edge continuity and positioning accuracy; according to the invention, the reliability and safety of automatic loading operation are improved.

The invention provides a multi-scale pyramid weighted fusion underwater image enhancement method based on double prior, and the method comprises the steps: obtaining a degraded underwater image, and carrying out the global and local cooperation body color calibration of the degraded underwater image; decomposing the color correction image into a base layer, a detail layer and a noise layer by adopting a variational decomposition algorithm; performing spectral prior and transmissivity loss constraint on the base layer image to obtain a defogged image; fusing the detail layer image and the noise layer image to obtain a filtered image; performing enhancement processing on the filtered image by adopting a nonlinear mapping and contrast enhancement strategy to obtain an enhanced image; performing multi-level feature integration and reconstruction on the defogged image and the enhanced image by adopting a multi-scale pyramid adaptive weighted fusion method to obtain an underwater image with natural color and high visual definition; according to the method, the traditional image processing and variational optimization thought is combined to effectively correct the color deviation of the degraded underwater image, the image contrast and the detail definition are improved, and the visualization effect of the underwater image is improved.

The invention discloses a multi-modal target detection method, a multi-modal target detection device, a medium and electronic equipment, and relates to the field of image processing. The multi-modal target detection method comprises the following steps: acquiring an infrared image and a visible light image of the same scene; the infrared image and the visible light image are input into a multi-modal target detection model, the multi-modal target detection model comprises a double-flow feature extraction module, a context-guided pyramid network and a prediction module, the double-flow feature extraction module comprises two processing flows, and feature maps of different scales are extracted from the infrared image and the visible light image respectively; the context-guided pyramid network carries out feature fusion on the feature maps from a large scale to a small scale to obtain a fused feature map; the prediction module is used for determining a prediction frame where a target is located in the fusion feature map.

The invention discloses a power line icing grade classification method, electronic equipment and a storage medium, belongs to the technical field of image processing, and solves the problem of how to improve the accuracy of power line icing detection.The method comprises the steps that firstly, an original icing image is obtained, and a standard icing image is preprocessed; a DConvLSTM spatio-temporal feature extraction model is used to extract meteorological spatio-temporal features, Swin Transform and ResNet are combined to extract global and local features of a standard icing image, and fusion of the global and local features of the image is realized through spatial pyramid pooling to obtain multi-scale features. The method comprises the following steps: performing weighted summation on meteorological spatial-temporal features, topographic features and multi-scale features to obtain a spatial-temporal multi-modal feature graph, performing block embedding by using a patch partition module, introducing a related mechanism and a CBAM module for weighted enhancement, and performing lightweight deconvolution, residual connection optimization and bilinear interpolation to obtain a high-quality graph. And the safety of a power line is ensured.

The present invention provides a back segmentation method based on improved Mask RCNN. The method comprises the following steps: collecting adolescent forward-bending bare back human body images to obtain a color image dataset; manually annotating human body bare back regions to obtain annotation files corresponding to original color images; constructing an improved Mask RCNN framework; training an improved Mask RCNN network to obtain a human body bare back segmentation model; and using the back segmentation model to obtain background-free human body bare back. The present invention effectively improves a feature pyramid in a Mask RCNN framework in light of an artificial intelligence technology and an image processing technology, and improves the accuracy of human body back region segmentation on the basis of instance segmentation characteristics of Mask RCNN.

The invention relates to the technical field of image processing and information retrieval, and discloses an image feature extraction and appearance trademark retrieval method based on deep learning, which comprises a feature extraction unit, a cross-modal fusion module, a retrieval strategy module and a dynamic weight distribution unit. The feature extraction unit extracts multi-level semantic information through a multi-branch convolutional network and multi-scale pyramid pooling; the cross-modal fusion module integrates vision, text and color information by using space attention, channel attention and cross attention mechanisms; the retrieval strategy module adopts Hash coding and global self-attention to realize rapid screening and fine matching; the dynamic weight allocation unit suppresses background interference through learnable parameters. The trademark retrieval precision and efficiency can be effectively improved, and the method is suitable for a multi-modal data processing scene.

A method, apparatus, non-transitory computer readable medium, apparatus, and system for image processing include obtaining a text prompt; generating, using a generator of an image generation model, a feature embedding based on the text prompt, wherein the feature embedding includes a first set of channels that encodes a first value of an image characteristic and a second set of channels that encodes a residual between the first value of the image characteristic and a second value of the image characteristic; and generating, using a decoder of the image generation model, a synthetic image corresponding to the second value of the image characteristic based on the feature embedding.

The invention discloses an intelligent storage environment lightweight efficient target detection method based on YOLO11 improvement, and belongs to the technical field of image processing. Aiming at the problems of target shielding, illumination change and limitation of computing resources and storage space on mobile equipment deployment in carton detection in a storage environment, target detection image data is loaded by using a database and is converted into a YOLO format, and a training set and a test set are divided; performance optimization is realized through the following improvements: a multi-dimensional cooperative attention module is introduced before a spatial pyramid pooling module of a backbone network; the neck network is fused with a weighted bidirectional feature pyramid network and a multi-dimensional cooperative attention module, and feature interaction between levels is enhanced; an original large detection head is removed from the detection head, medium and small targets are focused, parameter quantity and background interference are reduced, feature representation is optimized, detection precision and calculation efficiency in a storage scene are balanced, and the method is suitable for real-time detection and efficient operation of a mobile device on cartons in a storage environment.

The invention provides a hyperspectral image classification method fusing a hybrid multi-hop graph convolutional network, and belongs to the technical field of hyperspectral image processing, and the method comprises the steps: carrying out the dimension reduction processing of original HSI image data, and obtaining the HSI image data after the dimension reduction; segmenting the HSI image data after dimension reduction to obtain a superpixel segmentation result; inputting the original HSI image data into a convolutional feature pyramid network to obtain a first feature representation, and inputting the superpixel segmentation result into a hybrid multi-hop graph convolutional neural network to obtain a second feature representation; splicing the first feature representation and the second feature representation, and inputting the spliced feature representation into a mixed feature fusion module to obtain a fused feature; and inputting the features into a softmax classifier to obtain a final classification result. Local spatial-spectral features in the superpixel can be learned, long-distance associated information of the captured image can be captured, and rich multi-scale features and semantic information in the image can be effectively extracted; and higher classification precision is realized with less parameter quantity.

The invention provides a rail transit foreign matter intrusion detection method and device, electronic equipment and a storage medium, and belongs to the technical field of image processing, and the method comprises the steps: obtaining an original monitoring image of a target rail, and carrying out the preprocessing of the original monitoring image, and obtaining a monitoring image of the target rail; inputting the monitoring image of the target track into a pre-constructed foreign matter detection model to obtain a foreign matter detection result of the target track output by the foreign matter detection model; wherein the foreign matter detection model is obtained by training based on a sample monitoring image of a sample track and a foreign matter detection result label of the sample track; wherein the foreign matter detection model is obtained by improving based on a YOLOv8 network, and the foreign matter detection model comprises a lightweight backbone network, a feature focusing diffusion pyramid network and a dynamic detection head network. The method can improve the precision of foreign matter detection, achieves the accurate detection of small-scale objects, and is suitable for complex application scenes.

The invention discloses an ultrahigh-definition panoramic image adaptive HDR fusion method based on data driving, and relates to the technical field of digital image processing, and the method comprises the steps: carrying out the coarse alignment and high dynamic range fusion of a low-resolution line graph sequence, and generating a low-resolution HDR panoramic image; carrying out sharpening processing on the low-resolution HDR panoramic image to obtain a low-resolution sharpened HDR image, and meanwhile, obtaining a multi-scale residual spectrum generated by sharpening; performing up-sampling on the multi-scale residual spectrum, and performing small-sample element learning fine tuning on the weight prediction network in combination with the low-resolution sharpened HDR graph to obtain a fine-tuned weight prediction network; performing weight prediction and layer-by-layer fusion on the registered high-resolution tile sequence on a multi-scale pyramid based on a fine tuning weight prediction network model; according to the method, the sharpening processing is implemented on the low-resolution HDR panorama, and the multi-scale residual spectrum is extracted, so that the detail sensitivity and adaptability in a complex scene are improved.

The invention discloses a ship bollard identification method and system based on adaptive multi-scale multi-grid division, and belongs to the technical field of computer vision and image processing. The system divides an image foreground region and a background region through a visual saliency calculation module, carries out dense sampling in the foreground region and sparse sampling in the background region by adopting a non-uniform grid generation module, screens candidate grids in combination with gradient direction consistency and texture features, fuses candidate frames of different scales through a multi-scale image pyramid, and finally obtains a multi-scale image. And accurate positioning of the bollard is realized through the fine grid accurate positioning module. The method solves the problem that the prior art is insufficient in adaptability to ship size, shooting distance and resolution change, improves the precision and generalization ability of bollard positioning in a complex scene, and is suitable for bollard detection scenes of various ship images.

The invention belongs to the technical field of medical image processing, and particularly relates to a linear Transform general focus recognition method based on multiple perception and context guidance, and the method comprises the steps: extracting the multi-scale features of a medical CT image through a backbone network, obtaining the edge gradient features in parallel, making up the missing of focus boundary information through an edge perception feature enhancement module, and carrying out the recognition of the focus. And then global feature modeling under linear complexity is realized through a polarity perception feature interaction module, a high-discrimination-force multi-scale feature map is generated by using a context-guided feature pyramid network, and finally the model is optimized by combining a Hungary algorithm and a joint loss function based on an end-to-end detection architecture of set prediction. The problems that the focus boundary is fuzzy, feature interaction and calculation efficiency are balanced, and the focus and background separation degree is weak are effectively solved, double improvement of calculation efficiency and detection precision on massive medical image data is achieved, and reliable support is provided for clinical precise auxiliary diagnosis.

The invention discloses a weld defect detection method and a computer program product, and relates to the technical field of image processing.The method comprises the steps that a weld image to be detected is input into a target detection model, the target detection model comprises a feature extraction layer and a feature fusion layer, the feature extraction layer is provided with a multi-scale edge feature optimization sub-model, and the feature fusion layer is provided with a multi-scale edge feature optimization sub-model; an advanced screening feature pyramid network is arranged in the feature fusion layer; performing feature enhancement on the to-be-detected weld seam image through the multi-scale edge feature optimization sub-model to obtain an enhanced feature map; performing feature fusion on the enhanced feature map through an advanced screening feature pyramid network to obtain target features; and performing weld defect detection on the to-be-detected weld image based on the target feature to obtain a weld defect category. Through layered optimization, local calculation replaces global calculation, redundant features are inhibited through dynamic screening, and on the premise that defect detection precision is guaranteed, model calculation resources are remarkably reduced.

The invention relates to the technical field of computer image processing and medical image analysis, in particular to a cervical cancer MRI (Magnetic Resonance Imaging) image segmentation method for improving a U-Net structure based on a feed-forward channel double-attention mechanism. In order to solve the problems that a traditional U-Net structure is weak in multi-scale information extraction capability, inaccurate in boundary fuzzy region recognition and the like in processing female abdominal cervical cancer MRI images, the method proposes that a feedforward connection mechanism and a double-attention mechanism are embedded into an encoder and a bottleneck module to form a novel U-Net segmentation model; the recognition and segmentation precision of the cervical cancer focus area is improved, and a high-quality image basis is provided for subsequent clinical diagnosis and treatment. A double-attention mechanism is integrated into a plurality of key nodes of the model, a joint channel-space attention module is added to the tail of each convolution module in an encoder, a cavity space pyramid pooling structure is introduced to a bottleneck position, a channel attention mechanism is embedded, and joint modeling of cross-scale, multi-channel and space context information is achieved.

The invention relates to the technical field of image processing, in particular to an aerial image small target detection method and system based on deep learning, and the method comprises the steps: obtaining an aerial original image, carrying out the preprocessing of the aerial original image, inputting the preprocessed image into a multi-scale feature enhancement input model, and obtaining a multi-scale feature enhancement model; the method comprises the steps of obtaining an enhanced image containing multi-scale features, performing feature extraction by using a channel-space attention backbone network, generating a multi-scale feature map of a space attention mask, performing fusion by using a bidirectional recursive feature pyramid network to generate fusion features, performing target detection by using an adaptive receptive field detection head, and outputting a result. According to the technical scheme, through multi-stage enhancement and recursive fusion, the problem of small target information loss is effectively relieved, and the detection precision and robustness are improved.