109 results about "Feature compensation" patented technology

The invention relates to the technical field of 3D printing models, in particular to a 3D printing model slice feature compensation method and system and a medium. The method comprises the following steps that a 3D printing model is collected, a model feature skeleton is split, transverse slice features are generated, then a 3D printing environment is collected, virtual simulation is carried out, environment stress analysis is carried out on the skeleton slice features to obtain environment stress data, then deformation scale deduction and thermal deformation prediction are carried out based on the environment stress data, and a thermal deformation model is obtained. On the basis, reverse deformation simulation is carried out, deformation repair requirements are evaluated, a deformation compensation path is planned, finally, compensation simulation of a model feature skeleton is achieved, non-target deformation is analyzed, a compensation path is adjusted, and therefore the feature compensation method is optimized. According to the 3D printing model slice feature compensation method, the accuracy and quality of the 3D printing model are integrally improved.

The invention provides a remote sensing image semantic segmentation method and system fusing a convolutional neural network and a visual state space model, and the method comprises the steps: firstly extracting the multi-scale semantic features of a remote sensing image based on a lightweight ResNet18 encoder; secondly, a decoder based on a visual state space module is used for modeling a long-distance dependency relationship in an image and recovering spatial resolution; the local feature compensation module is used for enhancing the perception capability of fine-grained semantic information and improving the segmentation precision of a small target area; and finally, the multi-scale attention enhancement module is used for fusing deep and shallow layer features and realizing collaborative optimization of spatial details and semantic information. According to the method, the global semantic features and the local detail information of the remote sensing image can be extracted at the same time, and the method has high segmentation precision and a good remote sensing image semantic segmentation effect.

The invention discloses a Swin-YOLOv9 target detection method based on multi-stage dynamic pruning and feature compensation, and the method is characterized in that the method comprises the following steps: 1, carrying out the multi-stage dynamic pruning and feature compensation; the method comprises the following steps: 1, preprocessing an input target image; 2, designing a target detection network, wherein the target detection network is used for carrying out feature extraction on the preprocessed target image; 3, training the extracted features based on a multi-stage dynamic Token screening mechanism and a feature compensation mechanism of an attention mechanism, dynamically screening meaningful tokens, and performing feature compensation; and step 4, performing token structured pruning based on an attention mechanism on the token after feature compensation to realize target detection. On the premise of keeping or even improving the target detection performance, the pruning strategy is optimized, the redundant parameters and the calculation amount of the model are reduced, and therefore the training efficiency is improved, and the requirement for calculation resources is reduced.

The invention discloses a multi-modal imaging method and system based on acoustic-thermal coupling, and relates to the field of artificial intelligence, and the method comprises the steps: firstly obtaining thermal radiation data and ultrasonic reflection data of a target equipment region, and obtaining thermal radiation embedded features and ultrasonic embedded features through feature representation learning; performing multi-stage compensation on the thermal radiation embedding characteristic to generate a thermal field compensation reference characteristic of each stage; synchronously carrying out equal-stage multi-stage compensation on the ultrasonic embedded features, coupling the ultrasonic compensation result of the previous stage with the thermal field reference features of the corresponding stage between every two continuous stages of compensation, and combining the ultrasonic embedded features and coupling output in the later stage of compensation; and finally, performing imaging by using the acoustic-thermal coupling characteristics after multi-stage compensation to obtain an acoustic-thermal coupling imaging result fusing the thermal distribution characteristics of the target equipment region and the ultrasonic reflection spectrum characteristics. According to the method, deep fusion of acoustic and thermal information is realized through multi-stage feature compensation and dynamic cross-modal coupling, and the comprehensiveness and accuracy of equipment area detection are improved.

The invention relates to a distributed early warning log-based automatic learning and fault prediction method and system. The method comprises the following steps: analyzing historical log traffic characteristics, generating a log rate prediction value, dynamically adjusting a sampling frequency according to the rate prediction value, and outputting a lightweight feature vector with a timestamp; generating a real-time contribution weight of each node through a dynamic weight calculation function to obtain a region-level model parameter; inputting the regional-level model parameters into a feature distiller to generate representation vectors, and performing network superposition on the representation vectors to obtain a network topological relation matrix; tensor splicing is carried out on the feature vectors and a real-time network topology relation matrix, a graph neural network model is input, and a fault probability prediction value is generated in combination with a historical feature sequence; and when the real-time contribution weight of the node continuously decreases for N times and exceeds a preset threshold value, triggering an anomaly detection instruction, injecting a feature compensation vector into the FPGA preprocessing unit, and synchronizing the model updating frequency of the feature distiller.

The invention provides a cable bridge fault positioning method and system based on multi-layer sensing data fusion, and relates to the technical field of data processing. Fault monitoring feature compensation is carried out by analyzing fault features of a cable bridge and establishing a fault evaluation list, and a multi-layer sensor network is constructed; a digital twin space is constructed according to a physical structure and operation data of a cable bridge, and a multi-layer sensing configuration module is established based on a multi-layer sensor network and is embedded into the digital twin space; and according to sensing configuration parameters of the multi-layer sensing configuration module, fault identification and positioning are carried out through a digital twin space. The technical problems of low fault identification precision and large positioning error caused by monitoring blind areas and data loss in cable bridge fault monitoring in the prior art are solved, and the technical effects of improving the fault positioning accuracy and reliability and enhancing the fault monitoring comprehensiveness and real-time performance are achieved.

The invention provides an automatic focus identification and grading method for a medical image, and the method comprises the steps: carrying out the standardization of an obtained multi-modal original image based on anatomical constraint, and obtaining a standardized image; generating semantic enhancement features through a cross-modal feature compensation network based on the standardized image and associated radiological text description; performing dynamic feature adaptation processing on the semantic enhancement feature to generate a modal adaptive feature; performing context reasoning through a multi-scale feature interaction algorithm based on the modal adaptive features to generate context reasoning features; and lesion identification decoding processing is carried out on the context inference feature map, a lesion segmentation mask is generated, and the lesion segmentation mask is used for extracting lesion area feature parameters to carry out lesion classification. By adopting the method, the adaptability to the missing mode can be enhanced, and the focus identification and grading precision can be improved.

The invention provides a Yangtze river export deep and far sea environment data association analysis method based on multi-modal fusion, belongs to the technical field of far sea environment analysis, and achieves accurate data registration by establishing a multi-source data space-time standardization model and adopting an adaptive space-time Kriging interpolation algorithm. A sparse coding ocean signal separation algorithm is utilized to construct an over-complete dictionary separation mixed signal to extract pure features, a feature extraction network is constructed based on an attention mechanism to automatically learn deep feature representation of physicochemical biological parameters, and a matrix rank loss detection algorithm is adopted to automatically supplement feature compensation vectors to ensure feature integrity. A cross-scale attention mechanism is constructed based on wavelet transform to fuse multi-scale features, a Shapley value interpretability evaluation system is established to quantify correlation feature importance, and the technical problem that correlation feature extraction is inaccurate in the multi-source heterogeneous marine environment data fusion process is solved.

The invention provides an electric power information multi-modal data dynamic integration method and system, relates to the technical field of electric power information data integration, and realizes multi-modal data fusion through a technical chain of asynchronous receiving, feature extraction, elastic alignment, dynamic verification and intelligent compensation. The method comprises the following steps: acquiring a sensor numerical value, an infrared image and an environment audio heterogeneous data stream in real time; extracting each modal feature and calculating a historical deviation coefficient; sensor data is used as a reference axis, and a multi-modal maximum feature coincidence interval is determined through sliding window matching; performing cross-modal feature cross validation in the extended dynamic interaction interval, and marking credible fusion features; redundant feature compensation is triggered for the modals which do not pass verification, and an integrated feature vector is generated; and if continuous failure occurs, marking the data source as a low-confidence data source. Through elastic time alignment and a dynamic verification mechanism, the problem of native asynchronization of power multi-modal data is solved, the feature fusion reliability is improved, and accurate fault information can be provided.

The invention belongs to the technical field of building construction safety control, and particularly discloses a building construction site dangerous behavior recognition method and system based on machine learning, and the method comprises the steps: obtaining a single-view continuous video stream of a construction site, segmenting the single-view continuous video stream into a time sequence video frame sequence, and extracting an initial spatial feature sequence through a spatial feature encoder; a pre-trained virtual visual angle projection and feature compensation module encodes the visual angle implicit vector and maps the visual angle implicit vector to a visual angle invariant feature space, and a sequential context is combined to compensate occlusion missing features to generate an enhanced feature sequence; a time sequence memory alignment module captures long-term and short-term time sequence dependence and outputs time sequence consistency characteristics; and finally, outputting a current dangerous behavior classification result through the classification prediction head, and outputting a future dangerous intention probability curve through the time sequence prediction head. The method does not need an additional camera, can accurately cope with a shielding scene, predicts the danger in advance, and improves the construction safety monitoring efficiency and reliability.

The invention relates to the technical field of remote sensing monitoring, and discloses a farmland monitoring method and system based on a remote sensing technology. The method comprises the following steps: acquiring ISAR radar data and optical remote sensing data of a target area, and performing space-time registration on the ISAR radar data and the optical remote sensing data to obtain a remote sensing data set; performing coupling feature extraction and feature compensation on the remote sensing data set to obtain remote sensing feature data; monitoring and analyzing the target area according to the remote sensing feature data to obtain a construction progress monitoring result, a facility management and protection monitoring result and a planting utilization monitoring result; the construction progress monitoring result, the facility management and protection monitoring result and the planting utilization monitoring result are visually displayed through the preset cloud platform, automatic visual display of the monitoring results is achieved through the preset cloud platform, simultaneous online check and interactive data analysis of multiple users are supported, and the monitoring efficiency is improved. And the farmland monitoring accuracy is improved.

The invention relates to a foundation cloud picture segmentation method. The method comprises the following specific steps: synchronously acquiring foundation cloud picture data of a visible light wave band and foundation cloud picture data of an infrared wave band by adopting a double-spectrum imaging system; a deformable double-branch convolutional network is constructed, and the deformable double-branch convolutional network comprises a main branch embedded into a dynamic cavity convolution module based on a CSPDarknet53 structure and an auxiliary branch integrated with a polar coordinate attention mechanism; respectively extracting multi-scale texture features of the foundation cloud picture data of the visible light band and a thermodynamic feature map of the foundation cloud picture data of the infrared band by using a deformable double-branch convolutional network; constructing a spectral migration gating unit to perform feature compensation and feature fusion on the extracted multi-scale texture features and the thermodynamic feature map to generate fusion features; constructing a semantic segmentation network model based on deep learning, and introducing a meteorological dynamics constraint item module; the method comprises the following steps: acquiring a fusion feature and a historical segmentation condition of a foundation cloud picture based on historical foundation cloud picture data, and pre-training a semantic segmentation network model;

The invention provides a canteen catering compliance real-time monitoring system and method based on image recognition. The canteen catering compliance real-time monitoring system comprises a heterogeneous sensor array. Edge computing nodes; the environment self-adaption module is used for solving the recognition problems in complex scenes such as background color interference, day and night illumination fluctuation and steam shielding through HSV histogram similarity analysis and dynamic weight adjustment; a behavior compliance analysis engine; and a real-time feedback execution module. A multi-source sensor clock is synchronized, a unified space-time coordinate system is established, an illumination-infrared weight dynamic mapping and environment feature compensation algorithm is adopted, a visible light image flow, thermodynamic data and depth information are processed in parallel, multi-modal features are fused for composite operation behavior recognition, and a dynamic response instruction is generated according to a predefined compliance strategy library. According to the method, the recognition accuracy in a background color interference scene can be improved, and the night false alarm rate is reduced. According to the invention, the problem of poor adaptability to complex environments in traditional supervision can be effectively solved, and the food safety management level is significantly improved.

The invention provides a multi-modal data fusion diagnosis assistance method and device, a storage medium and equipment, and the method comprises the steps: obtaining multi-modal data sent by a data collection module, the multi-modal data comprising at least two of 2D fundus color photo data, 3D OCT body data, OCTA blood flow parameters and wide-area fundus spliced image parameters; inputting the multi-modal data into a trained layered feature extraction module to obtain a multi-modal data feature vector; inputting the multi-modal data feature vector into a trained feature compensation-fusion module to obtain a complete multi-modal data fusion feature; and inputting the complete multi-modal data fusion features into a trained dynamic expert hybrid system to obtain structured diagnosis and treatment data and a text diagnosis auxiliary report, and sending the structured diagnosis and treatment data and the text diagnosis auxiliary report to a data display module.

The invention belongs to the field of deep learning technology and remote sensing image segmentation, and particularly relates to a remote sensing image segmentation method based on Transsubnet edge information enhancement and multi-dimensional feature perception, and the method comprises the steps: S1, preparing a data set; s2, constructing remote sensing picture text description; s3, constructing and training a remote sensing image segmentation model; and S4, storing and testing the model. The invention designs a multi-modal feature extraction method based on parallelism of a sampling branch and a text feature extraction branch under edge feature compensation. The residual error mixing axial attention module is used for forming a transformer structure; and a text-picture multi-dimensional feature fusion enhancement module and a decoder part are embedded. According to the method, the ground feature identification capability can be improved through a text and picture multi-modal feature enhancement strategy, the segmentation boundary and small target object feature information is enhanced, more fine-grained features are reserved, the cross-regional long-distance dependency relationship is better captured, the common gradient disappearance problem in a deep network is relieved, and the method is suitable for large-scale popularization and application. And meanwhile, the small sample data set segmentation effect is improved.

The invention discloses an electromagnetic water meter error compensation method and system combining conductivity and temperature, and the method comprises the steps: analyzing the change trend of the actual flow velocity through a prediction result, and constructing a trend vector 1; subtracting a numerical value of the prediction result from a measured value, analyzing the change trend of the differential flow velocity, and constructing a trend vector 2; performing feature analysis and feature compensation on the current evolution process according to transfer learning of historical data on the evolution process, and combining a compensation vector after feature compensation with the trend vector 1 to obtain a compensated trend vector 1; and correcting the curve of the prediction result by using the compensated trend vector 1 to obtain an error compensation result of the electromagnetic water meter. The compensated flow velocity data better fits the actual physical state, has higher robustness and generalization ability, and can effectively meet the requirements of intelligent water affairs, industrial metering and the like for high-precision and full-time-domain monitoring.

The invention provides an underwater robot propeller type thruster cross-domain intelligent fault diagnosis method, and belongs to the field of underwater robot fault diagnosis. Energy characteristics and environmental parameters are introduced through a dynamic adversarial generative network, frequency domain physical constraints are constructed, pseudo fault data adaptive to a target domain are generated, and authenticity and rationality of generated signals are guaranteed by using time domain and frequency domain double discriminators. The environment residual error adaptation network fuses the residual error between the actual measurement characteristic of the target domain and the pseudo fault characteristic with the environment factor after the environment parameter coding by constructing a residual error dynamic compensation mechanism, constructs the dynamic mapping relation between the environment parameter and the characteristic residual error, adaptively generates the characteristic compensation amount, and finally performs the self-adaption on the characteristic compensation amount. Double-layer domain adaptation of global distribution alignment and local residual compensation is achieved, and the generalization ability and diagnosis accuracy of underwater robot propeller fault diagnosis in different water area environments can be effectively improved.

The invention discloses a radio frequency fingerprint hiding method based on feature compensation. The method comprises the following steps: at a transmitter end, carrying out carrier frequency offset compensation, I / Q imbalance compensation and PA nonlinear distortion compensation on a to-be-transmitted signal in sequence by using a preset joint compensation model to obtain a transmitted signal after radio frequency fingerprint hiding; the joint compensation model is constructed by the following steps: carrying out carrier frequency offset estimation and compensation, I / Q imbalance estimation and compensation and PA nonlinear distortion estimation on a received signal of a receiver in sequence, and constructing the joint compensation model according to a carrier frequency offset estimation result, an I / Q imbalance estimation result and a PA nonlinear distortion estimation result. According to the method, a scientific and reasonable parameter estimation and compensation sequence is designed, an illegal receiver is effectively prevented from extracting radio frequency fingerprints from received signals, the classification precision of the RFFI technology can be effectively reduced, and the privacy protection and recognition resistance of a transmitter are remarkably enhanced.

The invention relates to the field of unmanned aerial vehicle communication and protocol adaptation, and discloses a multi-brand and multi-type unmanned aerial vehicle equipment access configuration method, which comprises the following steps of: performing three-dimensional feature modeling for a target protocol by constructing a communication layer feature matrix, a data layer association map and a control layer space-time behavior tensor; based on a multi-dimensional protocol difference degree calculation model, the communication features, the data structure difference and the control behavior difference are fused, and the difference between a target protocol and a historical protocol is measured; the method comprises the following steps: selecting a most relevant basic configuration template through a protocol feature clustering and deviation compensation strategy, introducing a communication feature compensation item of a historical protocol, and generating a personalized access configuration parameter set adaptive to a target protocol; the method is suitable for access configuration generation of multiple types of unmanned aerial vehicle equipment, the adaptability, compatibility and stability of configuration can be improved, and the automatic configuration capability of an unmanned aerial vehicle protocol in a heterogeneous environment is enhanced.

The invention provides a multi-scene image degradation integrated recovery method based on a visual language model, which realizes high-quality image reconstruction under various weather degradation conditions, and comprises the following steps: inputting a given degraded image into an encoder for encoding to obtain encoding features; querying the pre-trained visual language model through a cross-modal prompt generator to generate a multi-scale degradation perception cross-modal prompt; the pre-trained visual language model takes a problem and a degraded image as input; the degeneration perception cross-modal prompt is input into the restoration trunk through a guide attention alignment module to be aligned and fused with the coding features, and final output of the guide attention alignment module is obtained; refining and fusing the degraded image and the final output of the attention alignment guiding module through a double feature compensation module to obtain the final total output; and carrying out decoding processing and image reconstruction on the final total output through a decoder to obtain a high-quality output image.

The invention relates to the technical field of information engineering knowledge graph construction, in particular to a supervision knowledge graph dynamic modeling method based on multi-source heterogeneous data fusion, and the method comprises the steps: designing a four-dimensional knowledge graph modeling framework, and combining the entity alignment of a graph neural network, the relation extraction of remote supervision and the attribute processing of weight fusion. A dynamic evolution and cross-source consistent knowledge network is constructed, virtual features are generated through a virtual feature compensation mechanism (historical event retrieval and multi-dimensional feature weighted fusion) for a multi-modal data missing scene and mapped to a graph, data are effectively filled, the problem of incomplete knowledge coverage in a complex engineering scene is solved, a compliance entropy evaluation model is introduced, and the knowledge coverage is evaluated. The compliance state of the knowledge graph is quantitatively supervised from the three dimensions of rule coverage rate, data consistency and tracing integrity, and the hysteresis and subjectivity limitation of traditional manual compliance check is broken through.

The invention provides a double-branch spectrum quantitative analysis method Mark-GLNet based on Pearson feature marking, and global dependency modeling and local feature extraction are collaboratively optimized. The Mark mark layer quantifies wavelength importance by using a Pearson's correlation coefficient, eliminates redundant wavelengths, and inhibits low correlation noise. Refining feature input GLNet: global branches utilize sequence continuity and cross-space correlation, dynamic weights highlight keys, and redundancy is suppressed; and local branches compensate channel correlation through multi-scale convolution, and reinforce details. Double branches are cooperated through feature compensation, redundant interference is reduced, and prediction precision is improved. Through verification of four open-source near-infrared data sets, the model is simple in structure, low in parameter quantity and calculation complexity and excellent in generalization ability, and has high practicability and expandability.

The invention discloses a three-dimensional occupancy perception method and system suitable for multiple tasks, and belongs to the field of robot perception, and the method comprises the steps: carrying out the manual labeling of point cloud data and image data, obtaining a point-by-point label and a pixel-by-pixel label, carrying out the data verification, converting the point cloud data with the labels into a 3D semantic occupancy label of a scene through 3D semantic reconstruction, and carrying out the recognition of the 3D semantic occupancy label. Forming sample data; training a three-dimensional occupancy perception model by using the sample data and performing three-dimensional occupancy perception prediction; sequentially performing feature enhancement extraction, feature dimension conversion, feature time sequence fusion and feature compensation correction on the image data based on a feature extraction module to obtain multi-scale voxel features; the feature information of the multi-scale voxel features is independently adjusted through a semantic segmentation head and a target detection head, then a 3D semantic occupancy result and an instance detection result are output, the two detection results are converted into an instance segmentation result and a target tracking result through a post-processing module, and low-cost and high-efficiency multi-task three-dimensional occupancy perception is achieved.

The invention relates to an image denoising method and device, equipment and a medium, and the method comprises the steps: obtaining a training data set which comprises a plurality of image sample pairs, and each image sample pair comprises a noise sample image and a noise-free supervision image corresponding to the noise sample image; dividing the image sample pair according to a preset size to determine image blocks corresponding to the noisy sample image and the noiseless supervision image; the method comprises the following steps of: embedding a plurality of improved Transform Blocks in each coding layer and each decoding layer of a preset U-Net trunk structure, and introducing a mixed feature compensation module at an input end and an output end in the U-Net trunk structure to construct an image denoising model; and inputting a to-be-denoised image into the image denoising model trained to the convergence state to determine a clean image corresponding to the to-be-denoised image so as to complete image denoising. According to the invention, the generalization ability and stability of image denoising in a complex real scene can be greatly improved.

The application discloses a gas extraction water ring vacuum pump fouling degree online detection system and method, relates to the technical field of vacuum pump fouling degree detection, and comprises a sound signal acquisition module, an excitation control module, a parameter sensing module, a signal processing module, a feature compensation module, a fouling inversion module and a model calibration module; the sound signal acquisition module is arranged on the outer wall of the pump body and is used for acquiring structural vibration signals generated in the operation process and outputting original vibration data; the excitation control module is connected with a piezoelectric excitation device and receives the operation state feedback of the sound signal acquisition module, is used for triggering the piezoelectric excitation device to apply high-frequency pulse signals to the pump shell when the suction negative pressure fluctuation is stable and continuously meets the steady-state condition, and synchronously starts the sound signal acquisition module to collect response signals; and the parameter sensing module is used for monitoring the cooling water inlet / outlet temperature and motor current waveform and outputting environmental and operation parameters.

The invention relates to the technical field of electromagnetic valve manufacturing and quality detection, and discloses an online detection system for the pressing force of a valve element of an electromagnetic valve, and the system comprises a composite magnetic field excitation module, a multi-physical field signal acquisition module, a multi-dimensional feature resolving module and a force value estimation output module. A composite magnetic field is applied to a valve element, and an induced voltage signal, a Hall signal and a temperature signal are synchronously collected; then, three characteristic quantities of harmonic amplitude, instantaneous magnetic field intensity and instantaneous temperature are calculated; and finally, inputting the three characteristic quantities into a preset multi-dimensional mapping model, and calculating to obtain a pressing force estimated value. The invention aims to solve the problem of low measurement accuracy caused by the fact that the existing single electromagnetic parameter detection is easily interfered by temperature and excitation field fluctuation. And through multi-physical field signal fusion and multi-dimensional feature compensation, the influence of disturbance factors is effectively eliminated, and the accuracy, stability and anti-interference capability of online detection are remarkably improved.

This invention provides a multimodal fusion-based method for correlation analysis of deep-sea environmental data outside the Yangtze River Estuary, belonging to the field of deep-sea environmental analysis technology. This invention achieves accurate data registration by establishing a spatiotemporal standardization model for multi-source data and employing an adaptive spatiotemporal kriging interpolation algorithm. It utilizes a sparse coding marine signal separation algorithm to construct an overcomplete dictionary to separate mixed signals and extract pure features. Based on an attention mechanism, a feature extraction network is constructed to automatically learn deep feature representations of physical, chemical, and biological parameters. A matrix rank deficiency detection algorithm is used to automatically supplement feature compensation vectors to ensure feature integrity. A cross-scale attention mechanism based on wavelet transform is constructed to fuse multi-scale features. A Shapley value interpretability evaluation system is established to quantify the importance of correlated features. This invention solves the technical problem of inaccurate correlated feature extraction during the fusion of multi-source heterogeneous marine environmental data.

The application discloses a hydrological remote sensing image target recognition method based on a deep semantic model, relates to the technical field of remote sensing image processing, and comprises the following steps: obtaining a primary semantic code of a hydrological remote sensing image through a deep semantic network, calculating a semantic attribution probability distribution according to the primary semantic code, generating an initial attention guide signal, dynamically adjusting the weight of a specific perception path in the network, realizing first feature re-extraction to obtain refined features, calculating a feature compensation vector based on the confidence deviation generated by matching the features with a standard feature library, correcting the refined features to obtain enhanced features, and inputting the enhanced features into the network again for analysis to obtain a final recognition result. Through the attention adjustment of the semantic guide and the feature compensation based on the confidence feedback, the method effectively improves the recognition accuracy of complex hydrological ground object targets and the model robustness.

The invention discloses a neural network-based vehicle axle intelligent identification optimization method and system, and belongs to the technical field of neural networks. Image acquisition is carried out in an ETC lane, a vehicle passing image sample set is constructed, scene identification parameters are established, region division is carried out on a vehicle image, and a shielding correction weight is calculated; multi-level features are extracted based on a convolutional neural network, feature compensation is carried out on the occlusion area through a neighborhood feature compensation module, and a complete axle candidate feature map is formed; inputting the candidate feature map into a lightweight neural network model, combining multi-scale fusion of shallow and deep features, dynamically adjusting the weight of a feature layer according to a shielding correction weight, and outputting an axle recognition result; and finally, according to a preset axle configuration rule base, matching a vehicle type identifier, and establishing a sample-parameter-classification mapping data set. According to the invention, axle features can be stably identified under complex illumination and shielding conditions, and the accuracy and robustness of ETC lane vehicle identification are improved.

The application discloses an end-to-end pest detection method with feature compensation and local information enhancement, and belongs to the field of deep learning. First, a Transform network framework based on feature representation compensation and cross-shaped window attention mechanism is established to extract multi-scale features of an image. Second, regional grid self-attention is used to replace even-numbered multi-head self-attention modules in a Transform model encoder to obtain feature information of the image. Finally, the first K encoder features in the last layer are selected to initialize the position query of a Transform decoder with contrastive denoising, and the content query remains a learnable parameter. Compared with a convolution-based method, the technical scheme of the application can achieve a higher average precision mean in a pest detection task.