9676 results about "Backbone network" patented technology

The invention relates to the technical field of multi-axis linkage numerical control machining, and discloses a multi-axis linkage numerical control machining state monitoring method and system.The method comprises the steps that vibration, acoustic emission and cutting force signals in the multi-axis linkage numerical control machining process are collected and preprocessed, and first multi-source sensor data are obtained; performing feature extraction and compressed sensing to obtain a low-dimensional feature subset; performing fusion judgment to obtain an abnormal state detection result; training a deep learning model comprising a backbone network and a plurality of branch networks to obtain a state evaluation model; and inputting second multi-source sensor data collected in real time into the state evaluation model for state evaluation to obtain a processing state classification result and a parameter estimation value. According to the method, cooperative state evaluation and prediction simulation of multiple machine tools are realized.

A small-size vehicle detection deep learning model based on feature fusion of multi-scale modules is provided, which solves the problem of small-size vehicle image detection. The model includes a Backbone network, a Neck layer and a Head network, wherein a C2f_DCNv3 module based on the combination of deformable convolution v3 (DCNv3) and a cross stage feature fusion (C2f) module and an SPPF_LSKA module based on the combination of a spatial pyramid pooling fast (SPPF) layer and a large separable kernel attention (LSKA) module are introduced into the Backbone network; a C2f_SCConv module based on the combination of spatial and channel reconstruction convolution (SCConv) and a C2f module is introduced into the Neck layer; and a multi-scale kernel detection (MSK_Detect) module is introduced into the Head network.

The invention discloses a remote sensing image building extraction method fusing double-space attention features, and belongs to the technical field of image processing. The method comprises the following steps: firstly, inputting a training set of an Inria aviation data set into a ConvNeXt backbone network to extract shallow feature information; then, carrying out feature information enhancement processing on the feature information through a double-space attention feature fusion module (DAFF); and finally, a multi-modal dynamic enhancement module (FAM) is used in the last time of feature information up-sampling operation to carry out channel alignment operation of different levels of features, and feature information fusion errors are reduced. According to the method, the accuracy of building extraction in the remote sensing image is remarkably improved.

The invention relates to a remote sensing target detection method and device and a medium, and the method comprises the steps: inputting a feature map to a backbone network for feature extraction, inputting an extracted feature tensor into a multi-branch expansion convolution structure, and extracting multi-scale features through convolution kernels with different expansion rates. Then, multi-scale features are fused through a space and channel double-path attention mechanism, and enhanced features are generated; and the enhanced features are further input into a cascade pooling module to generate multi-level reconstruction features, and weight coefficients are calculated through a gating fusion network to carry out weighted fusion, so that multi-scale fusion features are obtained. Next, these features are input into an asymmetric decomposition convolutional layer for downsampling, and dynamic channel attention calibration is performed to generate channel enhanced features. And finally, inputting the feature map processed by the backbone network and the neck network into a detection head network, and outputting a target bounding box and category prediction. According to the method, high-precision detection of multi-scale rotating targets and high-density small targets is realized in a complex remote sensing scene.

The invention discloses a drainage pipeline defect detection method based on improved YOLOv8s, and the method comprises the steps: obtaining a drainage pipeline defect data set, carrying out the data enhancement of the drainage pipeline defect data set, and dividing the drainage pipeline defect data set into a training set, a verification set and a test set; according to the method, YOLOv8s is used as a network basic model, a CBAM attention module is introduced into a backbone network, and feature extraction of pipeline defects is enhanced; an FPN-PAN structure of a neck network is replaced by a BiFPN structure, so that the network feature fusion capability is improved; a new Wise-IoU loss function is adopted to improve the detection precision of the model; and finally, a Conv module in a part of the network is replaced by a GhostConv module, and a C2f module is improved into a C2f-RepGhost module, so that the weight of the network is reduced. Conveying the training set to a drainage pipeline defect detection model for training; and conveying a to-be-detected drainage pipeline defect picture to the trained drainage pipeline defect detection model to obtain a final detection result. Compared with an original model, the detection precision is improved, and support is provided for defect detection of the drainage pipeline.

The invention relates to the technical field of image target detection, and discloses a complex scene small target detection system and method based on mask attention and context feature optimization, and the system comprises a backbone network integrating a mask attention mechanism, a backbone network in which an original image is input into the integrated mask attention mechanism, and a background network in which the original image is input into the integrated mask attention mechanism. Redundant background information is filtered through a mask mechanism, and a multi-scale feature map is output; the context-aware enhanced feature refining encoder is used for carrying out multi-branch processing on the multi-scale feature map, and finally weighted fusion is carried out to generate a multi-scale fusion feature sequence; and the deformable Transform decoder inputs the multi-scale fusion feature sequence, processes the multi-scale fusion feature sequence through a cross attention and self-attention module, constructs a target query, and generates a detection result containing bounding box coordinates, category labels and confidence scores. According to the method, the performance of small target detection is improved by introducing efficient feature extraction, a fusion mechanism and a sampling and attention strategy.

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention discloses an unmanned aerial vehicle aerial small target detection method based on improved YOLOv8, and relates to the field of target detection, and the method comprises the steps: obtaining an unmanned aerial vehicle aerial image data set, and carrying out the preprocessing of the obtained data set; constructing an unmanned aerial vehicle aerial photography small target detection model based on improved YOLOv8, wherein the improved method comprises the steps of designing an SR-Conv module to replace a standard convolution module of an original backbone network, adding a small target detection layer, introducing a BiFPN to improve a neck network, and introducing a normalized weighted distance (NWD) loss function to optimize an original loss function; training the improved YOLOv8 unmanned aerial vehicle aerial photography small target detection model by using the training set; inputting a to-be-detected image into the trained target detection model to obtain a target detection result; the system, the equipment and the medium are used for implementing the method. The method can better aim at the unmanned aerial vehicle aerial photography small target detection task, improves the unmanned aerial vehicle aerial photography small target detection precision, and has wide applicability.

The invention discloses a steel surface defect detection method and device based on YOLO11n improvement, and relates to the technical field of defect detection.The method comprises the steps that a to-be-detected steel surface image collected by an image collection device is obtained; inputting the to-be-detected steel surface image into a defect detection model obtained by training the improved YOLO11n model architecture to obtain surface defect detection information of the to-be-detected steel surface image; wherein a backbone network of the improved YOLO11n model architecture comprises a C3k2NAM module formed by a C3k2 module integrated with an NAM dynamic attention mechanism, and an Neck network introduces an NCB convolution block, an MSDA attention mechanism and a CoT Attention module. The problem that accurate identification of a multi-scale target cannot be realized in steel surface defect detection in the prior art is solved.

The invention discloses an underwater small target detection method based on YOLOv8 improvement, and aims to solve the problem of small target detection caused by illumination variation, background complexity and target scale difference in an underwater environment. According to the method, optimization is carried out based on a YOLOv8 architecture, firstly, a star convolution StarsBlock in a StarNet backbone network is combined with a C2F module of the YOLOv8, an improved C2FStarsBlock module is obtained, and the diversity and robustness of feature extraction are remarkably improved. Secondly, a CARAFEEnhanced module is adopted to replace up-sampling operation of a YOLOv8 neck, and dynamic convolution, grouping convolution and a nonlinear feature enhancement strategy are introduced, so that the calculation efficiency and adaptability of the model are optimized; and finally, integrating a gradient-guided multi-scale convolution attention mechanism GMSAA, introducing the gradient-guided attention mechanism in a back propagation stage, accurately focusing on a target area, and reducing background interference. The method effectively improves the detection precision of the underwater small target, and is especially suitable for efficient automatic detection of small underwater targets such as marine organisms.

The invention discloses a lightweight semantic segmentation network and method for power grid inspection, and the method comprises the steps: employing an encoder-decoder architecture, inputting original data into a lightweight MobileNet v2 backbone network for feature extraction at an encoder part, inputting an obtained feature map into a lightweight large-scale ASPP module, and carrying out the feature extraction; further performing multi-scale feature extraction on the high-dimensional feature map; and in the decoder part, high-dimensional features and low-dimensional features obtained by the backbone network are respectively input into a cross attention wavelet fusion module, after an obtained feature map is fused with the output of a decoder, the feature map is recovered to the size of original data through down-sampling operation by a full-dimensional dynamic convolution post-processing module, and a final prediction mask is obtained. According to the invention, by optimizing a coding and decoding structure, the receptive field of the model and the modeling capability of a narrow and long target are improved, the balance between the precision and the complexity can be effectively realized, and the method is suitable for an unmanned aerial vehicle inspection system with limited resources.

The invention discloses an infrared ship detection method based on an improved RT-DETR algorithm, and aims to improve the detection precision and efficiency of a ship target in an infrared image. By introducing a multi-layer dynamic feature conversion module (MDST), the diversity of feature extraction is enhanced; effective fusion of multi-scale features is realized by using a high-level screening feature fusion pyramid network (HS-FPN); an original RT-DETR backbone network is replaced by a PPA and C2f structure, so that the problem of information loss in a down-sampling process is solved; and the CA module is used for enhancing the feature selection and fusion effect. Experimental results show that the provided method significantly improves the detection performance on an infrared marine ship data set, is suitable for resource-constrained equipment such as unmanned aerial vehicles and smart phones, and has a wide application prospect.

The invention provides an AI-based automatic identity card image cutting and OCR recognition method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting a multi-angle depth image and a visible light image, reconstructing a three-dimensional model of a certificate through a three-dimensional point cloud registration network, and achieving the perspective correction of an image through the combination of attention posture estimation and projection matrix optimization; a double-flow memory neural network is adopted to carry out information area positioning, a feature extraction backbone network constructs a multi-scale feature pyramid, and a dynamic memory updating network carries out feature matching and dictionary updating based on a time sequence attention module and comparative learning; and performing character recognition on the segmented information sub-images through a multi-branch feature enhancement network, improving the recognition precision in combination with a self-attention enhancement unit and a context reasoning unit, and dynamically optimizing network parameters by using a reinforcement learning controller until a preset recognition credibility threshold value is reached.

The invention discloses a lightweight industrial product surface defect detection method, and belongs to the field of computer vision and artificial intelligence application, and the method comprises the steps: obtaining an industrial product surface defect image data set, and carrying out the preprocessing of the data; the method comprises the following steps: constructing a YOLO-IDL industrial product surface defect detection model, designing a lightweight hierarchical multi-scale feature extraction network as a backbone network, designing a scale rhinoceros neck as a neck network, and adopting a YOLOv8 head network as a head network; and training a YOLO-IDL industrial product surface defect detection model by using the preprocessed industrial product surface defect image data set to obtain a well-trained YOLO-IDL industrial product surface defect detection model, and deploying the well-trained YOLO-IDL industrial product surface defect detection model so as to detect industrial product surface defects and output defect types and positions.

The invention relates to the technical field of industrial automatic quality inspection, and provides a PCBA surface defect detection method and system based on deep learning and a medium, and the method is used for carrying out defect detection on a preset PCBA board. Comprising the following steps: acquiring a surface image of a PCBA board according to a preset multi-angle light source and a high-resolution camera, and performing adaptive illumination compensation and noise removal processing on the surface image to generate a standardized image; performing multi-scale segmentation on the standardized image to obtain image blocks including local details and a global structure; constructing a double-branch deep learning model, wherein the double-branch deep learning model comprises a backbone network, a multi-scale feature fusion module and a defect detection branch; inputting the image blocks into a double-branch deep learning model, and outputting a thermodynamic diagram and probability distribution by the double-branch deep learning model; performing binarization processing on the thermodynamic diagram by using a dynamic threshold segmentation algorithm to generate a defect mask; and outputting a defect detection result of the PCBA board according to the defect mask and the probability distribution, and completing the defect detection of the PCBA board.

The invention relates to the technical field of material detection, in particular to an optimization method for industrial pure iron metallographic specimen defect detection based on YOLOv8obb. The method comprises the following steps: acquiring an industrial pure iron metallographic sample defect image; preprocessing the metallographic image; constructing a YOLOv8obb defect detection network, and optimizing the defect detection network to obtain an optimized defect detection model; according to the model, a DynamicConv is used as a convolutional layer of Bottleneck in a C2f module, a SimAM attention mechanism module is introduced into a backbone network, a tiny target detection head is added, and an NEFPN feature pyramid network is used as a neck multi-scale feature fusion network; and the preprocessed sample data image pair is used for performing defect detection model training, and the trained optimized defect detection model is used for performing industrial pure iron metallographic image defect detection, so that the detection precision and the detection efficiency are improved.

The invention relates to the technical field of computer vision and target detection, in particular to an improved Yolov10-oriented target detection fusion method, which specifically comprises the following steps of: selecting data to construct a data set, and then preprocessing the data in the data set to obtain a preprocessed data set; the method comprises the following steps: preprocessing a data set, enhancing the preprocessed data set, constructing a target identification network model based on a lightweight Yov10n network, improving the Yov10n network, the improved Yov10n network comprising an input end, a backbone network, a neck network and an output end, and introducing four context fusion CFM modules into the neck network, the CFM module comprises a channel adjustment convolution module, a feature splicing module, an attention module, a weighted feature recombination module and an output feature generation module; and inputting the image data in the preprocessed data set into the improved constructed target recognition network model to obtain a final detection result. According to the method, diversified targets with different scales can be better processed, so that the robustness of the model to a complex background is improved.

The embodiment of the invention provides an unmanned aerial vehicle aerial photography target detection method and device based on deep learning. The method is applied to the technical field of target detection, and comprises the following steps: acquiring aerial image data of an unmanned aerial vehicle, and preprocessing the image data; inputting the preprocessed image data into a deep learning-based feature extraction network, wherein the deep learning-based feature extraction network comprises a backbone network, a small target frequency domain enhancement module, a lightweight multi-scale modeling module, a hierarchical context sensing module and a convolution gating linear module; and the preprocessed image data is analyzed and processed through the feature extraction network based on deep learning, and a target detection result is output, so that the real-time performance and precision of target detection are improved.

The present disclosure provides a multi-task panoptic driving perception method and system based on improved You Only Look Once version 5 (YOLOv5). The method in the present disclosure includes: performing image preprocessing on an image in a dataset to obtain an input image; extracting a feature of the input image by using a backbone network of improved YOLOv5, to obtain a feature map, where the backbone network is obtained by replacing a C3 module in a backbone network of YOLOv5 with an inverted residual bottleneck module; inputting the feature map into a neck network to obtain a feature map, and fusing the obtained feature map and the feature map obtained by the backbone network; inputting the fused feature map into a detection head to perform traffic target detection; and inputting the feature map of the neck network into a branch network to perform lane line detection and drivable area segmentation.

The invention discloses a saliency target detection method for an intelligent auxiliary driving system, and the method comprises the following steps: 1, obtaining a driving scene image based on a driver visual region in real time through a vehicle-mounted image collection device, and carrying out the preprocessing of the image; step 2, performing bottom-layer multi-scale feature extraction, interaction and global enhancement of feature information on the backbone network, and extracting rich multi-scale feature information; step 3, the neck network comprises an AESwinTransform attention module, and a cross-layer feature guide fusion module and a multi-level feature interaction fusion module in the TFG network, and the multi-level features extracted from the backbone network are fused together; step 4, detecting and classifying the feature map; and 5, inputting the adversarial sample and the normal sample into the model in a mixed manner, and performing adversarial training by using the trained model. According to the method, the detection precision of the small saliency target can be improved, the generalization ability and robustness of the model in a complex environment are improved, and the interference of irrelevant scene information is reduced.

The invention belongs to the field of image data processing, and relates to an improved YOLOv10-based PCB surface defect detection system and method, the system is obtained by improving a YOLOv10 network model, a C2f module in a backbone network of the model is replaced by a multi-scale cross-stage fusion module, and the multi-scale cross-stage fusion module is used for extracting deep features of an image and capturing multi-scale information in input data; a dynamic perception feature information enhancement module is introduced into the neck network and applied to processing of P4 and P5 feature maps; a detection head of a YOLOv10 network model is replaced by a Lag module, the Lag module carries out channel adjustment through a grouping normalization convolution module and then passes through a shared convolution group, and data output by the shared convolution group is distributed to a positioning convolution module and a classification convolution module. The system can realize the detection of small defects under the interference of complex background noise, and has the advantages of high detection precision and efficiency and the like.

The invention discloses an insulator defect detection method based on multi-scale features and channel perception, and belongs to the technical field of computer-aided algorithms. The method comprises the following steps: firstly, constructing a multi-scale feature fusion module, and fully extracting context information by combining multi-scale feature information and through a series of parallel deep convolution; secondly, a self-adaptive channel sensing down-sampling module is embedded in the backbone network, so that small-scale feature information is prevented from being lost during down-sampling of a feature map, and the sensing capability of the model to important channel information is improved; and finally, on the basis of adding a small-scale target detection layer, designing a lightweight shared phantom convolution detection head so as to improve the multi-scale target detection capability of the model under the condition of reducing the model parameter quantity. According to the method, a good effect is achieved on insulator defects of different types and multiple scales, the average detection precision is 91.4% and is improved by 5.2% compared with a reference model, and the overall performance is superior to that of other insulator defect detection algorithms.

The invention discloses an improved YOLOv11-based lightweight aerial image target detection method, which comprises the following steps of: firstly, acquiring an aerial image data set and converting the aerial image data set into a YOLO format, and then constructing an aerial image detection model which takes a YOLOv11 model as a reference network and comprises a backbone network, a neck network and a head network; in the backbone network of the aerial image detection model, a Conv module in the backbone network of the YOLOv11 model is replaced by a DBR Down module, and an MSFLB module is added in the neck network; and finally, performing iterative training on the constructed aerial image detection model to obtain a trained aerial image detection model, and detecting a to-be-detected target in the aerial image by using the trained aerial image detection model to obtain a detection result. The aerial image detection model constructed by the invention can effectively improve the precision of aerial image target detection under the condition that edge equipment resources are limited.

The invention belongs to the field of cross-modal pedestrian re-identification, and discloses a cross-modal pedestrian re-identification method based on a coal mine scene, and the method comprises the steps: 1, obtaining an identity representing a pedestrian image and an identity tag corresponding to the pedestrian image, and carrying out the preprocessing; step 2, constructing a cross-modal pedestrian re-identification backbone network; step 3, constructing a multi-scale feature enhancement module and adding the multi-scale feature enhancement module into the cross-modal pedestrian re-identification backbone network; 4, determining a loss function, and completing the final training of the cross-modal pedestrian re-identification backbone network; and 5, taking one modal pedestrian image as a query set, taking the other modal pedestrian image as a to-be-retrieved set, and matching the pedestrian image in the query set with the pedestrian image in the to-be-retrieved set to obtain a cross-modal pedestrian re-identification result. According to the method, cross-modal image features can be effectively fused, feature information of pedestrian images can be effectively mined, and the prediction ability and generalization ability of the model in a cross-modal pedestrian image retrieval scene are effectively improved.

The invention discloses an unmanned aerial vehicle image small target detection method based on dynamic filtering and an adaptive sparse Transform. According to the method, an end-to-end target detection framework is adopted, a dynamic filtering module is introduced into a backbone network, global feature interaction is achieved through data-dependent frequency domain operation, and linear calculation complexity is maintained. For feature interaction in a scale, an adaptive sparse Transform module is introduced to enhance the capability of focusing key information on high semantic hierarchy features of a model, and noise interference and feature redundancy are effectively suppressed at the same time. Through the combination of dynamic filtering and adaptive sparse Transform, the model can extract image foreground information more effectively on the premise of not significantly increasing the calculation burden, and the problem that a traditional target detection model is susceptible to complex background interference is significantly relieved.

The invention discloses a real-time single-stage remote sensing image correction target detection method based on YOLOV8, and relates to the technical field of remote sensing image processing. According to the method, a deformable convolution dynamic prediction local geometric distortion parameter is embedded based on a YOLOv8 backbone network, an adaptive deformation field is generated, pixel-level real-time correction is realized, shallow details and high-level semantic features are fused through a bidirectional path aggregation network, and channel attention and a space gating mechanism are combined, so that the real-time correction of the image is realized. The small target detection capability is enhanced, background noise is suppressed, angle prediction is divided into discrete classification and continuous residual error regression tasks through a decoupling type rotation detection head, angle periodic errors are eliminated in combination with a direction sensitive loss function, and the rotation frame positioning precision is improved. And constructing a dynamic multi-task collaborative loss function, introducing gradient distribution consistency constraint to jointly optimize correction and detection tasks, and realizing feature semantic alignment and model self-enhancement through end-to-end closed-loop training. And the rotating target detection precision and the complex scene robustness are obviously improved.

The invention discloses a capacitor appearance intelligent defect detection method based on YOLO-NAS and Transform fusion, and belongs to the field of artificial intelligence and industrial automation, a lightweight backbone network is combined with MobileViT, a feature extraction architecture is optimized through NAS, and the global feature expression ability is improved; a Transform-FPN (Fabry-Perot Networking) is introduced to realize multi-scale feature fusion; instructing student model learning by utilizing high-order features of the teacher model through feature distillation; optimizing small target and irregular defect detection precision by adopting an improved positioning loss function; and the Soft-NMS and the DIOU-NMS are combined, and the calculation complexity is reduced through model pruning and hybrid quantization, so that the real-time detection deployment of the embedded equipment is realized. According to the capacitor appearance intelligent defect detection method based on YOLO-NAS and Transform fusion, the problems of missing detection of small targets, difficulty in distinguishing multiple defects and high model calculation complexity are solved by combining a lightweight backbone network, a Transform module, multi-scale feature fusion and an improved loss function.

The invention belongs to the technical field of PCB defect detection, and discloses a PCB small target defect detection method based on improved YOLOv8x. Performing data enhancement on the original PCB defect picture data set; a Backbone network is improved, a SAConv convolution kernel is introduced, and a fusion MLCA mixed local channel attention module is added; a Neck network is improved, a small target detection head for processing a 160 * 160 pixel feature map is added, in addition, a WTconv convolution kernel is introduced into the Neck network, and an SCSA fusion space and channel collaborative attention module is added; and performing PCB small target defect detection by using the trained improved YOLOv8x model. The method is suitable for defect detection of small targets in pictures under the complex background of the pictures, and compared with an original YOLOv8x model, the method has the advantage that the detection precision is improved on the premise that the detection speed is not affected.

The invention provides an improved underwater target detection system and method based on RT-DETR, and belongs to the technical field of underwater target detection based on computer vision. The system comprises a backbone network module, a hybrid encoder module, an InerMPDIOU sensing module and a decoder module, the backbone network module performs large separable kernel attention calculation after extracting features based on the large separable kernel attention, and outputs a multi-layer feature map; the hybrid encoder module design comprises a deformable large kernel attention-based in-scale feature interaction unit and an improved cross-scale feature fusion unit; the sensing module introduces a scale factor and an auxiliary bounding box into IoU calculation of an original IoU sensing module, and target query initialization is improved by using query selection sensed by InerMPDIOU. According to the model improvement, adaptation is carried out aiming at the particularity of the underwater environment, the underwater operation efficiency is improved, and the accuracy is improved while the parameter lightweight is realized.

The invention discloses a wood water-based paint surface defect detection method based on computer vision, and belongs to the technical field of wood paint surface detection, and the method comprises the following steps: S1, constructing a wood paint surface defect data set including four typical defects of bubbles, cracks, holes and scratches; s2, introducing a wavelet convolution module to improve the Backbone network, enhancing the image feature capture capability, and avoiding the excessive parameterization problem of a traditional method; s2.1, a wavelet convolution module (WTConv) is added into a Basic Block of the ResNet network, and a feature extraction unit is improved, so that the capability of the network for capturing paint surface defect features is enhanced, and meanwhile, the calculation complexity is reduced; and S2.2, the wavelet convolution module enhances the multi-scale feature representation of the image through wavelet transform. And S3, designing an AIFI module, and optimizing a feature fusion effect through a multi-scale capture mechanism based on K and V feature information. According to the method, the multi-scale feature capturing capability of the model is enhanced, and the detection precision of defects of different types and sizes is improved.