318results about How to "Enhance feature expression" patented technology

The invention discloses a fault analysis method for vehicle-mounted automatic test equipment, and relates to the technical field of vehicle-mounted electronic equipment testing, and the method comprises the steps: collecting vehicle-mounted test data of the vehicle-mounted automatic test equipment, and storing the vehicle-mounted test data in a test database according to a time sequence; performing feature extraction on the test database through a deep learning model to generate a fault feature vector; inputting the fault feature vector into a fault diagnosis model, and generating a fault type judgment result and a fault probability value; and according to the fault type determination result and the fault probability values, screening corresponding fault recovery schemes from a preset fault recovery strategy library, and sorting the fault recovery schemes according to the fault probability values from high to low to form a fault processing report. According to the method, the fault processing report is generated based on the layered fault recovery strategy library and the priority grading mechanism, the technical problems that a traditional fault diagnosis method is insufficient in feature extraction and low in diagnosis precision are solved, and the accuracy and recovery efficiency of fault diagnosis are improved.

The invention provides an RSVP type unbalanced electroencephalogram signal classification method based on a self-adaptive channel mixed attention mechanism and decoupling learning, and aims to improve the feature extraction and classification performance of RSVP electroencephalogram signals. Firstly, collected RSVP electroencephalogram signals are preprocessed, including common average reference, band-pass filtering and standardization processing, and a training set and a test set are divided; then, multi-scale time and space features are extracted through a parallel convolution layer and a space convolution layer, and the features of different levels are fused through an interpolation method; the training process is decoupled into representation learning and classifier learning, the representation learning uses a triple loss function based on difficult sample sampling, and the classifier learning uses the optimized Focal Loss to perform feature learning and classifier optimization. A self-adaptive channel mixed attention mechanism is introduced, and the flexibility and robustness of the model are enhanced through dynamic weighting and multi-level feature fusion. According to the method, the problem of class imbalance in the RSVP task is solved.

The invention discloses a bearing fault diagnosis method based on a parallel multi-fusion deep learning network model. And obtaining a sample data set, dividing the sample data set into a training data set and a test data set, carrying out parallel multi-fusion deep learning network model training by using the training data set, and inputting the test data set into the trained parallel multi-fusion deep learning network model to carry out fault diagnosis. Wherein the parallel multi-fusion deep learning network model comprises three parallel sub-networks, namely a bidirectional gating circulation unit sub-network, a fast Fourier transform-convolutional neural network sub-network and a discrete wavelet transform-enhanced depth separable ion network, and a dynamic multi-head attention mechanism module. According to the method, the three parallel sub-networks and the dynamic multi-head attention mechanism module are combined to automatically extract and identify the fault features of the rolling bearing under multiple working conditions, and self-adaptive fault feature extraction under different domain signals and rolling bearing fault diagnosis under complex working conditions are realized.

The invention discloses a symptom management and prognosis evaluation method and system for a tumor radiotherapy patient, and relates to the technical field of tumor radiotherapy patient management.The method comprises the steps that basic information of the tumor radiotherapy patient is obtained, prognosis evaluation is conducted according to the basic information, a first result is generated, and the risk category is judged according to the first result; obtaining symptom information of a tumor radiotherapy patient, evaluating the symptom severity according to the symptom information, and generating a second result; and performing symptom management according to the second result and the risk category, generating prognosis data, and updating the first result through the prognosis data. According to the invention, by fusing the dual contrast learning network and the dual-path decision tree integrated model, the dynamic association analysis and adaptive feature enhancement of the multi-dimensional medical data of the tumor radiotherapy patient are realized, so that a precise prognosis evaluation and a personalized symptom management scheme are provided for clinical practice.

The invention discloses a lightweight space-time traffic flow prediction method based on GAT and Mama, and relates to the technical field of intelligent traffic systems. According to the method, the feature expression ability of the model is improved through multi-dimensional feature learning (time, date and space-time) of an embedded layer, rich features of input data can be deeply captured, and the learning ability of the model is enhanced; a graph attention module is used to adaptively pay attention to the relative importance between nodes, so that the graph structure relationship in the data can be more effectively captured and utilized, and the analysis accuracy of the model on the spatial data is improved; the GAT-Mama module optimizes the time sequence analysis and spatial data processing capability, and the model can more accurately capture the dynamic dependency relationship between time points and between spatial positions through a multi-layer graph attention mechanism and residual connection; the GAT-Mama layer structure supports flexible adjustment, a user can adjust the depth of the model according to specific task requirements so as to adapt to spatio-temporal data prediction tasks with different complexities, and good adaptability is achieved.

The invention provides a time sequence prediction method and system for a KAN enabling graph attention parallel loop network, and relates to the technical field of time sequence prediction, and the method comprises the steps: obtaining time sequence input data; inputting the time sequence input data into a feature extraction layer based on fast Fourier transform and discrete wavelet transform, and preprocessing the time sequence input data by the feature extraction layer to obtain spliced feature extraction data; inputting the spliced feature extraction data into a feature aggregation layer of graph attention based on KAN, and preprocessing the spliced feature extraction data by the feature aggregation layer to obtain feature aggregation data; and inputting the feature aggregation data into a time sequence prediction model of the parallel gating circulation unit network based on KAN and attention, and outputting time sequence prediction data by the time sequence prediction model. According to the method, the calculation complexity of time sequence prediction is reduced, and the time sequence prediction efficiency and accuracy are improved.

The invention relates to the technical field of genomics, in particular to a gene detection data cleaning method and system based on artificial intelligence. The method comprises the following steps: acquiring gene detection data, and performing preliminary filtering on the gene detection data based on removal of low-quality reads to obtain preliminary filtering data; performing linker sequence identification and removal on the preliminarily filtered data, and performing sequencing error correction to generate gene sequence data; performing GC content analysis and repetitive sequence identification on the gene sequence data to obtain sequence feature data; performing dynamic quality threshold adjustment according to the sequence feature data, and performing secondary filtering on the gene sequence data to generate high-quality sequence data; and performing sequence pattern recognition on the high-quality sequence data to obtain sequence pattern feature data. According to the method, deep analysis is carried out by utilizing sequence characteristics such as GC content and nucleotide distribution, and the recognition capability on sequence complexity and variation modes is improved.

The invention belongs to the field of network security of smart grids, and discloses a method for detecting an antagonistic network attack in a smart grid based on an iterative algorithm, and the method comprises the steps: generating an antagonistic attack sample through iteration, so as to avoid the protection of a detection system; performing feature extraction and reconstruction of input data by using the constructed automatic encoder network model, and capturing complex time sequence features through convolution, pooling and up-sampling operations; a dynamic parameter adjustment strategy is adopted to adaptively optimize the training optimization momentum coefficient and the training optimization target reconstruction error in the training process, and the system robustness is improved; and finally, verifying the concealment and stability of the resistance attack sample under different scaling conditions through multi-scale evaluation so as to optimize the automatic encoder network model. According to the invention, the security protection level of the smart grid system in a complex network attack scene is improved, and stable operation of the power system is ensured.

The invention relates to an edge fusion method and system of an XR immersion type LED display screen. The method comprises the following steps: carrying out feature extraction on image data of an XR immersive LED display screen to obtain an RGB three-channel spatial domain feature matrix and a frequency domain edge feature matrix; performing feature processing to obtain a multi-core context feature map; performing coding processing to obtain a position-brightness comprehensive coding feature matrix; performing multi-scale decomposition to obtain a multi-scale detail feature map; carrying out pixel clustering on the edge region of the XR immersive LED display screen to obtain an edge topological structure diagram and a brightness transition function; and performing dynamic matching on the display frame rate through a PID controller, and performing brightness compensation and color correction on the edge region to obtain edge fusion data. The method has the multi-scale feature extraction capability, the edge fusion problem under different scenes can be adaptively processed, and it is ensured that processing delay meets the real-time display requirement, so that the overall performance of an XR immersive display system is improved.

The invention provides a rolling bearing fault diagnosis system and method based on a multi-source fusion residual network, and relates to the technical field of bearing fault diagnos.The rolling bearing fault diagnosis system and method adopt a one-dimensional convolution kernel and a residual block feature extraction module, the one-dimensional convolution kernel ensures that data of different sensors are not mixed in the feature extraction process, and the residual block feature extraction module is used for extracting the data of different sensors; the effectiveness of unique features of each type of signals is guaranteed, the gradient disappearance problem in a deep network is relieved through residual connection, a feature fusion module of a grouping cross attention mechanism is further adopted, sensor signals are grouped according to types, each group is weighted independently, and the sensor signals are subjected to clustering processing according to types. And local and global multi-scale features are further extracted by using multi-scale convolution, so that the uniqueness of different sensor signals is ensured, the expression ability of fusion features is improved, and the accuracy and robustness of rolling bearing fault diagnosis are improved.

The invention discloses a refrigeration equipment fault prediction and diagnosis method, device and system, and the method comprises the steps: collecting a vibration signal and a working condition signal of refrigeration equipment, carrying out the preprocessing of the original data of the vibration signal and the working condition signal, and obtaining the vibration characteristic data and the working condition data; aligning the vibration characteristic data with the working condition data, dividing working conditions according to the working condition data, and classifying the vibration characteristic data under each working condition to obtain a plurality of groups of fault diagnosis data sets; on the basis of each working condition, historical healthy vibration data of the refrigeration equipment are obtained, a fault diagnosis model is constructed and trained, abnormal diagnosis is conducted on the vibration characteristic data through the trained fault diagnosis model, and abnormal fault points are obtained; and fault positioning analysis is carried out by adopting a spectrum analysis method, fault positioning information corresponding to each working condition is obtained, and a maintenance work order of the refrigeration equipment is generated and displayed. According to the method, accurate fault pre-judgment diagnosis can be carried out on the refrigeration equipment, and maintenance is convenient.

The invention discloses a pedestrian and vehicle detection method based on an improved YOLOv10n. The pedestrian and vehicle detection method comprises the following steps: on the basis of an original model YOLOv10n, replacing a first layer and a third layer in Backbone and a seventeenth layer Conv in Neck with a space-to-depth convolution SPDConv; a focused spatial pyramid pooling module ASPPF is constructed, and semantic fusion of cross-level features is optimized; designing a global grouping coordinate attention module GGCA, fusing the GGCA into a Neck layer, generating attention maps by using global information of the feature maps in the spatial dimension, and weighting the input feature maps through the attention maps to enhance the feature expression ability; a WIou loss function is introduced, the bounding box loss of the network is optimized, and the convergence speed and regression precision of the model are improved. And training a model by using a data set which is made by autonomous labeling to obtain a training result, and completing a pedestrian and vehicle detection task by using an optimal training weight. According to the method, a good detection effect is achieved with a small number of model parameters, and the pedestrian and vehicle recognition accuracy is well improved.

The invention discloses a strip steel surface defect detection method based on a multi-branch parallel attention model, and belongs to the technical field of industrial defect target detection. On the basis of a YOLOv8 network, space depth conversion convolution is used for replacing all down-sampling convolution in a backbone network CSPDarkNet, a multi-branch parallel attention module is used for replacing a C2f module, and an internal feature interaction module is added behind the backbone network, so that the strip steel surface defect detection precision of the model is improved. The strip steel surface defect detection precision is effectively improved while model parameters are slightly increased, and compared with an existing strip steel surface defect model, the problem that the image detection effect is poor due to the fact that background interference is strong, various defects are large in difference, the number of small defects is large, and defect targets are likely to be confused in an industrial scene can be better solved.

The invention discloses a wind power generation power prediction method and system, and belongs to the technical field of wind power generation processing, and the method comprises the steps: building a multivariate time series prediction data set based on the historical data of a to-be-predicted target, and training a multivariate time series prediction model based on the multivariate time series prediction data set to obtain a power prediction pre-training model; the power prediction pre-training model is adopted to predict the current parameter data of the to-be-predicted target to obtain the power generation power of the to-be-predicted target in the future set time, the method is excellent in performance in time series prediction tasks of multiple fields based on the multivariate time series prediction data set, and experimental results show that the method is high in prediction efficiency. According to the method provided by the invention, the long and short term dependency relationship in the multivariate time sequence can be effectively captured, and the accuracy and stability of wind power generation power prediction are improved by utilizing physical constraints.

The invention discloses a CAD drawing intelligent matching method and system based on multi-stage deep learning, and belongs to the field of image comparison, and the method mainly comprises the steps: S1, loading and preprocessing a CAD drawing, and constructing a triple data set through a data enhancement and sample balance technology; s2, constructing a target detection model and a target matching model, and training the target detection model and the target matching model by using the triple data set; wherein the target detection model adopts a YOLOv5 model, and the CAD drawing is detected in combination with sliding window detection and a non-maximum suppression technology; the target matching model adopts a double-end network structure; and S3, matching the CAD drawings to be identified by using the trained target detection model and target matching model. According to the invention, through a plurality of advanced technologies, intelligent matching and judgment of two or more CAD images in any different formats are realized.

The invention provides a light guide plate surface defect detection method based on a GCC-YOLOv8 network model. The method comprises the following steps: acquiring an image of a light guide plate to be detected; the image is input into a GCC-YOLOv8 network model for defect detection; outputting surface defect information of the light guide plate based on a detection result; wherein the GCC-YOLOv8 network model is constructed based on a YOLOv8 network model, and comprises a backbone network unit, a neck network unit and a detection head network unit with three parallel detection branches, which are arranged in sequence; two GKA-C2f1 modules are arranged at the tail end of the backbone network unit; the neck network unit comprises an FPN network unit and a PAN network unit; the FPN network unit comprises two up-sampling modules, two C2f2 modules, two Concat modules, an SPPF module and a CPCA module; the PAN network unit comprises two CBS modules, two Concat modules, two C2f2 modules, three CPCA modules, a CFC module and an SFC module. The light guide plate surface defect detection method has good light guide plate surface defect detection performance and robustness, and also has good detection speed.

The invention discloses a model training method and an intelligent construction site safety equipment detection method, and belongs to the technical field of target detection. The method comprises the following steps: firstly, acquiring a sample security equipment image, a sample security equipment annotation image and a YOLOv8 improved model, wherein the YOLOv8 improved model comprises a high-level screening feature fusion pyramid structure, a dynamic sampling module, a RepGhostCSPELAN module and an SEAM attention module; and then, based on the sample security equipment image and the sample security equipment annotation image, training a YOLOv8 improved model to obtain a target security equipment detection model. According to the method, the parameter quantity and the calculation quantity can be reduced, the detection effect of the safety equipment in the intelligent construction site is improved, and the detection real-time performance and high efficiency are ensured.

The invention discloses a multi-label arrhythmia identification method based on a multi-branch residual attention network, and relates to the technical field of ECG classification, and the method comprises the steps: carrying out the preprocessing of original data, including resampling and segmentation; constructing a training set, a verification set and a test set for network model training; inputting the processed training set into a multi-branch residual network for training, extracting information of different scales by different branch feature extractors, further automatically obtaining electrocardio features by using a residual attention module through fusion branches, and testing and storing an optimal target model in a verification set; and predicting on a test set, comparing with a label, and calculating the accuracy rate and recall rate of arrhythmia classification. According to the method, multi-label arrhythmia detection is carried out by using a thought of combining a multi-branch network and residual attention, so that the classification precision and the prediction accuracy are improved.

The invention relates to an image classification method and system based on a hierarchical competitive spiking neural network, belongs to the field of image classification, solves the problem of low recognition accuracy during image classification, and comprises the following steps: inputting preprocessed image data into a trained SNN network to obtain a prediction classification label of an image; wherein a competition strategy is introduced into the SNN, and the competition strategy comprises the following steps: sorting feature maps of different positions of input image data, and realizing excitation or suppression of the feature maps; and sorting different spiking neurons based on the sensitivity to realize excitation or suppression of the spiking neurons. The training of the SNN network comprises the following steps of: allocating a weight to each convolution kernel in the network, and allocating a weight to each neuron; calculating to obtain a training loss value; and carrying out back propagation, and calculating to obtain the weight of each convolution kernel, the weight of each neuron, an excitation coefficient and a suppression coefficient. The feature expression ability of the pulse neural network is enhanced, and the accuracy of image recognition is improved.

The invention relates to an image recognition method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a to-be-detected image and an image recognition model; the image recognition model comprises an extraction unit, a gating unit, a plurality of expert units and a feature fusion unit; performing feature extraction on the to-be-detected image based on the extraction unit to obtain an optimized feature map, and determining a scheduling coefficient based on the gating unit and the optimized feature map; performing linear transformation on the optimized feature map according to the plurality of expert units to obtain a mapping feature representation corresponding to each expert unit; determining prompt features corresponding to the optimized feature map according to the scheduling coefficient and the mapping feature representation, and performing feature fusion on the prompt features and the optimized feature map based on a feature fusion unit to obtain feature lexical elements; and determining an identification result of the to-be-detected image based on the feature lexical elements. By adopting the method, the accuracy of image recognition can be improved.

The invention belongs to the field of surface deformation prediction, and provides a goaf surface deformation prediction method fusing convolution attention and multiple influence factors, which comprises the following steps: S1, obtaining related data covering a goaf range, the related data comprising SAR image data, auxiliary data and external influence factor data; s2, constructing a time sequence data set based on temperature, rainfall and InSAR deformation of the goaf according to the related data; s3, preprocessing the time sequence data set, dividing the time sequence data set into a training set and a test set, and constructing and training a deformation prediction model based on a convolution attention mechanism and multivariable input; and S4, predicting future deformation of the goaf according to the deformation prediction model. According to the invention, high-precision deformation prediction in a complex deformation mode can be realized; according to the deformation prediction model based on the Transform encoder and the Bi-LSTM decoder architecture, the precision and the robustness of the deformation prediction model can be improved.

The invention discloses a lightweight rice disease prediction method based on SCL-YOLOv8n, and belongs to the technical field of rice disease prediction. The method comprises the following steps: constructing a rice disease data set; performing enhancement, labeling and division processing on the rice disease data set; in a GSConv module of a Slim-Neck network structure, before Shuffle operation, lightweight convolution is introduced to be connected with an original path in parallel to form a GSLConv module; the method comprises the following steps: based on a CSP-RFA structure, dividing data, and distributing the data to two branches to form an RFCA-CSP module; a lightweight sharing enhanced detection head LSCSBD is adopted; an SCL-YOLOv8n model is constructed according to the model; taking the processed data set as the input of an improved model SCL-YOLOv8n, and training the improved model SCL-YOLOv8n by using the training set; in the aspect of rice disease prediction under the complex background, the parameter quantity and the calculation complexity are reduced, the precision is improved, and an effective reference is provided for rice disease prediction under the complex background.

The invention discloses a property service response method and system based on a voice robot, and relates to the technical field of voice recognition, and the method comprises the steps: collecting a voice signal, generating a spectrogram feature, generating keyword category prediction based on the spectrogram feature, and carrying out the signal screening according to a prediction result; and positioning a signal sound source direction based on a screening result, extracting a target voice signal, performing voice enhancement on the target voice signal, converting the voice into a text, performing semantic analysis on the text, and generating a user instruction. Through voice signal acquisition and adaptive noise suppression of the multi-channel microphone array, the signal acquisition precision is improved, based on an improved spectrogram feature generation mechanism, the feature expression ability of voice signals is improved, and the service response speed and accuracy are remarkably improved.

The invention provides an infrared weak and small target detection method and system, electronic equipment and a storage medium, and belongs to the technical field of target detection, and the method comprises the steps: improving a YOLOv8 model for the features of an infrared weak and small target image; and performing target detection on the infrared weak and small target image by using the improved YOLOv8 model. According to the invention, a stronger infrared weak and small target detection capability is provided for the YOLOv8 model, and the problem of target detection precision bottleneck of a traditional model in a complex scene is effectively solved.

The invention belongs to the field of traffic control systems, and relates to a vehicle driving track prediction system and method based on an improved graph neural network model, and the system comprises an FPGCN model and a Social LSTM model. The FPGCN model comprises a graph convolution layer, a Dropout layer, a differential pooling layer, a feature fusion module and a full connection layer; obtaining node feature vectors from graph structure data through a graph convolution layer, a Dropout layer and a differentiable pooling layer, then dividing and conquering node features, generating global features through a dense attention mechanism and principal component analysis, and generating local features through virtual filling and matrix calculation; after global features and local features are obtained, a feature fusion module is used for fusion, then the features are input into a full connection layer, and the influence score of a historical trajectory diagram on a current state diagram is calculated; integrating the preprocessed driving data and the data of which the score is calculated by the FPGCN model, and inputting the integrated data into a Social LSTM model; according to the system, while the model expression ability is maintained, the parameter quantity is remarkably reduced, the calculation logic is optimized, and the prediction accuracy is improved.

The invention discloses a YOLOv5n-based industrial part surface defect detection method and device, and the method comprises the steps: embedding an attention mechanism into a convolution layer of a backbone network, introducing dynamic snakelike convolution to capture cross-channel information, capturing direction perception and position perception information, and carrying out the detection of the surface defect of an industrial part. Inputting the industrial part surface defect image data set into the backbone network for feature extraction to obtain three feature maps with different scales; a Gold-YOLO structure is introduced, multi-layer features are globally fused, global information is injected to a higher level, and a feature map highlighting the global information is generated; inputting the obtained feature maps into a feature pyramid network, and carrying out weighted fusion on the feature maps on different scales step by step through a down-sampling mode and an up-sampling mode; and outputting a defect detection result, and marking a detection frame, a defect type and confidence. The device comprises a processor and a memory. According to the invention, the detection precision of the surface defects of the industrial parts is higher, and the requirements of monitoring and maintaining the health conditions of the parts in industrial production are met.

The invention provides a virtual power plant distributed data anomaly diagnosis method based on a deep neural network, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the multi-modal data of each distributed energy of a virtual power plant, and obtaining the processed distributed data; modeling each node of the virtual power plant and the relationship thereof into a graph structure to obtain a multi-time-scale spatio-temporal feature vector; pre-training is carried out by constructing a self-supervised learning task, and effective features between different time scales and modal data are extracted; generating a normal data distribution model by using a generative adversarial network, and summarizing abnormal data samples into a comprehensive data set; obtaining a multi-task optimization model through sharing characteristics among tasks; a final optimized anomaly response system is obtained, and then the task of virtual power plant distributed data anomaly diagnosis is completed through the anomaly response system. According to the method, efficient anomaly diagnosis and response processing of the distributed data of the virtual power plant are realized, and the method has remarkable technical advantages and practical application value.

The invention discloses a visual positioning method and system based on multi-modal information fusion, and belongs to the field of computer vision. The method comprises the following steps: acquiring an RGB image and a depth image in the same scene, extracting semantic features from the RGB image through a multi-modal feature extraction network, and extracting structural features from the depth image; performing weight distribution and deep fusion on the semantic features through a feature balance module to obtain final multi-modal fusion features; predicting three-dimensional scene coordinates of each two-dimensional pixel point and uncertainty corresponding to the three-dimensional scene coordinates on the basis of the final multi-modal fusion features; and calculating the six-degree-of-freedom pose of the camera based on the predicted coordinates. According to the method, the problems of texture dependence and structure fuzziness in the existing single-mode visual positioning method are solved.

The invention provides a three-dimensional seismic fault identification method based on deep learning, and the method comprises the following steps: (1), carrying out the automatic modeling, generating three-dimensional seismic data and an accurate fault label, obtaining a three-dimensional seismic image data set, dividing the data set into a training set and a test set, and carrying out the recognition of the three-dimensional seismic fault; (2) training an improved 3D-Unet convolutional neural network of a two-channel structure or an AG-3D-Unet network fused with an attention mechanism and residual learning by taking the three-dimensional synthetic seismic data of the training set and a label of the three-dimensional synthetic seismic data as input, and verifying by using a test set to obtain a trained seismic fault recognition model, (3) inputting real seismic data, and obtaining a seismic fault recognition result. And outputting a fault identification result for the input to-be-predicted image through the identification network. According to the method, the definition, the fault continuity and the accuracy of fault identification are obviously improved, and the method has high practical value in actual seismic data fault interpretation.

The invention discloses a traffic video saliency prediction method and system based on space-time double-branch network feature fusion. The method comprises the following steps: acquiring traffic video data to be predicted, selecting a continuous video sequence in a time branch network, selecting a single-frame video image in a space branch network, and respectively inputting the continuous video sequence and the single-frame video image into a trained significance prediction model; the method comprises the following steps: preliminarily encoding a video sequence by adopting an encoder, guiding continuous frame time sequence modeling through a saliency attention transmission mechanism, and then performing decoding operation through a deconvolution decoder to obtain a time saliency map; capturing saliency information of different receptive fields from a single-frame video image through a multi-resolution feature aggregation module which is subjected to joint sampling and layer-by-layer association to obtain a spatial saliency map; and finally, performing fusion decoding on the spatial-temporal characteristics, and calculating a final saliency map. The method can accurately predict the salient region of the traffic video in the driving scene, and has important significance for the development of the auxiliary driving technology.