54 results about "Sigmoid activation function" patented technology

The invention discloses a GRU-based vehicle-mounted CAN intrusion detection method and system, a storage medium and a computer system. According to the method, an automatic encoder (AE) is introduced to deepen the understanding of a model on input sequence characteristics, a sliding window is used for selecting batch CAN data to be preprocessed to obtain 13-dimensional time sequence data, and a scalar value within the range of [0, 1] is obtained through processing of the encoder, a GRU, a decoder, a full connection layer and a sigmoid activation function and used for classification of abnormal data. The Conv1D is used as a hidden layer, and compared with two-dimensional convolution, the one-dimensional convolution parameter quantity is smaller, and the calculation is simpler and more convenient. An attack message and a normal message can be completely distinguished, the precision and the accuracy rate reach 100%, and the precision in Fuzz detection is 0.9983; compared with the prior art, the method has high accuracy and reliability in the aspect of intrusion behavior detection, can effectively identify most intrusion events, and can keep a relatively low overall error rate, so that good balance between safety and availability is realized.

The invention discloses a training method, a segmentation method and a device of a multi-mode weak supervision medical image segmentation model. The method comprises the following steps: unifying the size of a medical image; in the first stage of the network model, CAM graphs under different scales are extracted through a residual coding network, and single-mode CAM graphs under different scales are merged through a Sigmoid activation function; fusing CAMs of different modes; encoding a foreground image, a background image, a text label corresponding to the foreground image and a text label corresponding to the background image by using CLIP, calculating the similarity between an image encoding vector and a corresponding text encoding vector, and maximizing the similarity as a loss item; meanwhile, taking an opposite number of an absolute value of a difference value between the CAM of the foreground image and the CAM of the background image as one of loss items to train a model; performing foreground and background segmentation on the final CAM by taking 0.5 as a threshold value, and calculating a final Dice score with a real label so as to evaluate the performance of the model; according to the method, the multi-modal CAM fusion problem can be solved by utilizing the strong representation capability of the large model.

This application provides a joint emotion recognition method and system based on action unit-driven attention, relating to the fields of computer vision and affective computing. The method includes: inputting a feature map into an action unit (AU)-driven attention branch, processing it through a sigmoid activation function to obtain an activation vector, and inputting this vector into a learnable mapping layer; projecting the AU information back into the spatial geometric space to generate a single-channel spatial attention map, which is then multiplied element-wise with the feature map to obtain a weighted feature map; determining continuous values ​​for discrete emotion category probabilities, valence, and arousal; constructing a total loss function; and training the model using the acquired AffectNet and Aff-wild2 datasets to determine the target model for emotion recognition. This application achieves end-to-end joint optimization of AU detection and emotion recognition, significantly enhancing the interpretability and generalization ability of the model while improving emotion recognition accuracy, enabling it to better adapt to the emotion recognition needs in complex scenarios.

The application relates to the technical field of deep learning and recommendation system, and particularly discloses a hybrid expert model sparse inference method and system for generative recommendation, which comprises the following steps: obtaining original input data, and obtaining an input vector through an embedding layer; obtaining hybrid expert weights through normalization and maximum value selection operation on the input vector; calculating attention-enhanced features according to the hybrid expert weights based on a hierarchical attention mechanism; inputting the input vector and the attention-enhanced features into a hybrid expert model to calculate a recommendation result; wherein, according to the attention-enhanced features, expert weights are calculated based on a parallelized gating mechanism activated by a Sigmoid activation function; the activated expert layer is selected according to the expert weights, and the input vector is used to calculate the recommendation result. The application can achieve a recommendation accuracy comparable to or even higher than that of an advanced dense model under low time overhead and low calculation complexity.

The invention discloses a hierarchical adaptive multi-modal fusion method and system for a multi-modal financial large model, and the method comprises the steps: collecting financial text data, financial image data and time series data, and carrying out the preprocessing; performing modal feature extraction including text features, image features and time sequence features, and generating a text feature vector, an image feature vector and a time sequence feature vector; designing a full connection layer for each modal feature, then connecting a Sigmoid activation function, generating a gating weight vector, and obtaining a weighted feature vector; carrying out information interaction between modes by adopting a multi-head attention mechanism to form a feature vector after interaction enhancement; and constructing a financial domain knowledge graph, generating a domain knowledge vector, and carrying out vector splicing on the feature vector after interaction enhancement and the domain knowledge vector to generate a fusion feature vector. The method aims at improving the understanding and analysis ability of a large language model in application scenes such as financial analysis, risk assessment and market prediction.

The invention discloses a weak light image enhancement method based on Transform. The method comprises the following steps: extracting local detail information of an input weak light image through a convolutional layer, and converting the local detail information into a local feature map with a specified size; inputting the local feature map into three continuous feature extraction modules constructed based on 3D multi-head attention to extract deeper global features; fusing the three global features through a multi-layer fusion module to generate a final enhanced feature map; the multi-layer fusion module fuses features from different layers by adopting an adaptive weighting method; and the enhanced feature map passes through a convolutional layer and a Sigmoid activation function to obtain a final enhanced image. According to the invention, the quality of an image shot under a low-light condition is improved.

The invention discloses a vehicle re-identification method and device, equipment and a medium, relates to the technical field of computer vision, and aims to solve the problem that attribute information of a vehicle cannot be utilized due to lack of data annotation by extracting a semantic feature vector related to the color of the vehicle and a semantic feature vector related to the type of the vehicle. The vehicle color features and the vehicle type features are aligned to the spatial dimension of the vehicle global features, the two aligned features are spliced into attribute features, and the attribute features pass through a convolutional layer and a sigmoid activation function to generate a spatial adaptive color space attention map and a spatial adaptive type space attention map respectively; in the process, the weight of the vehicle color feature and the weight of the vehicle type feature are adaptively and dynamically allocated to improve the feature representation capability, and finally, the global feature capable of representing the appearance feature and the visual feature is spliced with the two weighted features after weight adjustment to perform vehicle re-identification, so that the accuracy of vehicle re-identification is improved.

This invention discloses a medical image segmentation method based on enhanced attention. The method acquires a medical image, preprocesses it, and inputs it into an encoder to output a multi-scale feature map of the medical image. The encoder includes N cascaded multi-dimensional attention modules. Each multi-dimensional attention module extracts multi-scale features through dual-channel convolution and residual connections, and performs feature recalibration using an attention enhancement module. The output multi-scale feature map is then fused by a multi-scale dilated fusion attention module to obtain the feature map output by the encoder. The feature map output by the encoder is input to the decoder, where it is combined with high-resolution feature maps of the corresponding levels passed from the encoder via skip connections. Through progressive upsampling and feature fusion, a reconstructed feature map is obtained. The reconstructed feature map output by the decoder is used to generate a medical image segmentation mask through 1×1 convolution and a sigmoid activation function to complete the segmentation of diseased tissues. This invention significantly improves the boundary segmentation accuracy for knee joints, breast tumors, and skin lesions.

The invention relates to the technical field of indoor positioning, and discloses an indoor multi-target positioning method and positioning system based on an antenna array, and the method comprises the steps: collecting the radio frequency signals of a region from a plurality of space angles, and obtaining the RSSI and phase of each tag; preprocessing the RSSI and the phase, inputting the RSSI and the phase into a pre-trained double-branch residual network architecture for feature extraction and feature fusion, and generating a fusion feature map; inputting the fused feature map into a network architecture through a data processing unit to carry out deep feature extraction, and determining output features; and mapping the output feature into an N-dimensional grid position vector, and outputting the probability that each grid in the N grids has a target by adopting a Sigmoid activation function. According to the embodiment of the invention, the RSSI data and the phase data of the reference tag array are acquired from a plurality of space angles through the plurality of virtual antennas in the RFID equipment, a large amount of multi-dimensional radio frequency information is acquired, the space sensing capability of a positioning area is enhanced, and the accuracy of indoor multi-target positioning is improved.

The present invention discloses an image fusion method and system based on a dual-discriminator generative adversarial network, belonging to the field of computer vision. The method includes constructing and training a dual-discriminator generative adversarial image fusion model; wherein the generator includes a dense feature extraction and fusion module, a feature enhancement module, and a decoding and reconstruction module; the dense feature extraction and fusion module extracts and fuses the features of the two frames of images to be fused to obtain a fused feature map; the feature enhancement module performs a global average pooling operation, two fully connected layers, and a Sigmoid activation function on the fused feature map to obtain a feature enhancement coefficient, and then multiplies the feature enhancement coefficient with the fused feature map to obtain an enhanced feature map; and the decoding and reconstruction module decodes and reconstructs the enhanced feature map to obtain a fused image. The present invention effectively retains the information in different source images, while improving the generator's ability to fit and model the fusion of different source images, thereby improving the quality of the fused image.

The invention discloses an airway segmentation network based on multi-scale directional attention and local graph convolution fusion, and relates to the field of medical image processing, a U-Net type three-dimensional encoder-decoder structure is adopted, space detail information is reserved and gradient propagation is promoted through jump connection, an encoder is used for extracting multi-level semantic features, and the decoder is used for extracting multi-level semantic features; a decoder gradually recovers spatial resolution through jump connection, a segmentation result is obtained through a convolution layer and a Sigmoid activation function, an airway segmentation network integrates a multi-scale direction attention mechanism and a local graph convolution attention mechanism, enhancement is carried out in combination with relative position coding, the identification capability of small airway branches is improved, and boundary false detection is reduced. According to the method, the recognition rate of the tail end fine bronchus is remarkably improved, breakage and leak detection are reduced, the connectivity and topological integrity of the airway tree are enhanced, the model is more sensitive to feature response of small airways, fuzzy boundaries and low-contrast areas, the continuity of the output airway model is higher, and the method can be directly used for bronchoscope robot navigation and path planning.

This invention claims protection for a CNN-LSTM algorithm neural network accelerator based on FPGA. The CNN hardware implementation includes a data input line buffer module, a convolution calculation module, a ReLU activation function module, an intermediate result buffer module, and a pooling calculation module. The LSTM hardware implementation includes an LSTM control module, a gate function calculation module, and a sigmoid activation function linear approximation module. The FC hardware implementation includes an FC control module, a fully connected layer calculation module, a ReLU activation function module, and a data output buffer. The purpose of this invention is to design a high-performance, low-power, and highly flexible CNN-LSTM neural network accelerator tailored to specific application scenarios. The innovation lies in the fact that, compared to traditional neural network accelerators, this invention uses a parallel pipelined design method to implement a CNN-LSTM algorithm neural network accelerator, which significantly improves the low power consumption and data throughput of the neural network accelerator. Furthermore, the parallel processing capabilities of the FPGA enable the algorithm to run at a faster speed.

This invention discloses a method and system for analyzing and outputting TCM acupuncture cases based on machine learning, belonging to the field of medical image processing technology. The method acquires facial image sequences from two pathological cycles of the subject, extracts spatiotemporal feature maps, and obtains local receptive field feature matrix pairs through sliding windowing. It calculates the cross-cycle joint entropy gradient and its matrix, and generates a two-dimensional deformation vector using optical flow. Using the coordinates of the first cycle as a reference, the deformation vector is set to zero when the joint entropy gradient does not exceed the rigidity threshold; otherwise, the joint entropy gradient matrix is ​​used as a weight matrix through a sigmoid activation function, and a Hadamard product is performed with the deformation vector to obtain a denoised deformation vector. The denoised vectors are superimposed to obtain corrected pixel coordinates, which are mapped to the target localization coordinate matrix, and the acupuncture case analysis results are output. This scheme achieves precise spatial decoupling of local tissue evolution, eliminates the contamination of rigid regions by global deformation, and significantly improves the robustness and accuracy of cross-cycle acupuncture target localization.

The invention discloses a micro-expression recognition method based on a position-motion double-flow fusion network. The micro-expression recognition method comprises two parts: designing a position extraction branch PEM based on Vision TransformerVIT; designing a motion feature extraction branch based on dynamic cross attention; a depth separable convolutional layer is embedded in front of the multi-head self-attention MSA module. A spatial adaptive feed-forward network is designed to replace a multi-layer perceptron in a standard VIT. A grouping processing strategy is adopted, and input features are divided into a plurality of sub-feature groups according to channel dimensions. A parallel sub-network is constructed to focus on feature extraction of different dimensions. And finally, generating a gating signal through a Sigmoid activation function by the interacted features, and performing element-by-element multiplication on the gating signal and the original input features. Through the dynamic weighting mechanism, which key features are reserved and which irrelevant noise is inhibited can be adaptively determined, so that accurate extraction of weak motion features is realized.

The invention provides a load data missing value dynamic filling method and device, equipment and a storage medium. The method comprises the steps that firstly, a real-time power grid parameter input vector of a clean heating transformer area is acquired, a space estimation result is generated through a KNN algorithm, and a time sequence prediction result is generated through an LSTM model; and the meta-model generates output through a full-connection ReLU hidden layer and transmits the output to an output layer, and a dynamic fusion weight is generated through Sigmoid activation function processing. And fusing the results to generate a final filling value, and inputting the final filling value into a load prediction model. According to the method, the KNN and LSTM results are dynamically fused, the load prediction precision is improved, optimal dispatching of the power distribution network is supported, the clean heating operation cost is reduced, and the wind and light abandoning phenomenon is reduced.

The lithium ion battery SOC prediction method based on the CEEMDAN and the optimized BiLSTM comprises the following steps: decomposing preprocessed operation data by adopting adaptive noise complete ensemble empirical mode decomposition CEEMDAN to obtain a plurality of intrinsic mode functions (IMF) and a residual component; constructing a BiLSTM model, wherein the model comprises a forward LSTM unit, a backward LSTM unit and an output layer; a dropout optimization algorithm is introduced into the BiLSTM model, and a combination of a two-parameter sigmoid activation function and a softsign activation function is adopted as a neuron activation function; and inputting the intrinsic mode function IMF and the residual component into the optimized BiLSTM model, optimizing model parameters through training, and predicting the state of charge SOC of the lithium ion battery by using the trained model. Aiming at the problems of weak data nonlinear processing capability, model overfitting, gradient dispersion and the like in the existing SOC estimation method, the invention provides a lithium ion battery SOC prediction method based on CEEMDAN and BiLSTM optimization, so as to improve the SOC prediction precision and model stability.

This invention belongs to the field of image processing and relates to a Retinex-based low-light image enhancement method. It reveals degraded content in the image through a degraded appearance restorer and performs Retinex decomposition using two branches consisting of convolution and a sigmoid activation function to estimate the reflectance and luminance components, respectively. After obtaining the estimated reflectance and luminance components, gamma correction is used to adjust the luminance component. This invention decouples the low-light image into a three-channel color map and a grayscale detail map to maintain consistency with the target image in color and detail representation. Furthermore, this application provides an unsupervised loss function to constrain the solution space of the Retinex decomposition, thereby improving adaptability in unknown and complex scenes. Extensive experiments demonstrate that this method outperforms state-of-the-art methods.

A gas overflow detection method based on contrast learning, comprising the following steps: S1, a contrast learning training stage, a model weight of picture feature extraction is trained, the model comprises three branches respectively for feature extraction, then three features are fused to obtain a final picture feature I o ; S2, the last model in step S1 is frozen, and two fully connected layers are connected thereafter, a nonlinear feature is obtained through a sigmoid activation function between the fully connected layers, finally a feature map of a specific dimension size of the feature is output, an output of positive and negative classification is obtained, representing the classification of whether there is gas overflow; S3, a preset gas leakage confidence threshold value, a picture and a thermal map acquisition device are fixed at a certain angle to fixed-point shoot the device to be acquired, an RGB three-channel picture corresponding to the device and a corresponding single-channel thermal map are acquired, the two fused pictures are input into the model obtained in step S2 for gas overflow classification prediction. Through the present application, gas leakage detection can be better recognized.

The application discloses a bridge risk identification method and system fusing integrated learning and an attention mechanism, and the method comprises the following steps: processing an i-th input feature in an input data set to obtain a reconstructed feature vector; constructing XGBoost and RF-MLP models to perform deep feature extraction, and acquiring fusion features by using an attention mechanism; inputting the fusion features into a back-end multi-task perception decoder, and using parallel decoding branches to decouple and identify the fusion features, wherein a first full connection layer and a first Softmax activation function are used as a risk type decoding branch to output a risk type probability distribution; a second full connection layer and a second Softmax activation function are used as a position decoding branch to output a position probability distribution; a third full connection layer and a Sigmoid activation function are used as a damage degree decoding branch in cooperation with linear mapping to output a damage degree quantitative index; and the application has the advantage of high identification precision.

The invention discloses a one-dimensional fixed star spectrum noise reduction method based on U-Net, and a noise reduction model used in the method is constructed based on a U-Net network and comprises an encoder and a decoder. The encoder comprises a plurality of encoding layers, the decoder comprises a plurality of decoding layers, each encoding layer is in jump connection with the corresponding decoding layer, and an attention gating mechanism is introduced into the jump connection; according to the attention gating mechanism, after 1 * 1 one-dimensional convolution is carried out on output features and gating signals of the coding layer, element-by-element addition is carried out, and fusion features are obtained; after fusion features are sequentially subjected to a ReLu activation function and 1 * 1 one-dimensional convolution, attention weights are generated through a Sigmoid activation function; and multiplying the output feature of the coding layer by the attention weight element by element to obtain the output feature of the attention gating mechanism. Aiming at the sequence characteristics of one-dimensional fixed star spectrum data, the method is more suitable for the recovery of weak spectrum characteristics in the aspects of characteristic extraction and detail maintenance, and the fidelity and the signal-to-noise ratio of noise reduction are remarkably improved.

This invention relates to the fields of digital image processing and computer vision technology, and discloses an automatic adjustment method for ultrasound image enhancement, comprising: acquiring discrete ultrasound echo data streams; calculating in real time, based on a sliding window, the local grayscale range characterizing the physical contrast limit and the local coefficient of variation characterizing signal fluctuation characteristics; performing threshold dynamic drift determination based on physical contrast constraints, and constructing a dynamic threshold adjustment factor; using this factor to modulate the response center threshold of the sigmoid activation function in real time, and generating image enhancement weights through dual-parameter collaborative nonlinear mapping; and using this weight to perform multiplicative modulation on the original pixels. This invention establishes a dynamic coupling mechanism between local physical contrast and the response interval of the enhancement function, thereby achieving adaptive differential processing of high-depth attenuation regions and strong reflection regions, effectively improving the imaging signal-to-noise ratio and boundary sharpness in complex sound field environments.

The invention discloses an implementation circuit of an S-type activation function based on an ASIC (Application Specific Integrated Circuit). The implementation circuit comprises an input analysis module, an address generation module, a lookup table storage module and a multiplication and addition calculation module, the input analysis module is used for receiving input data in a floating-point number format and separating sign bits, index bits and mantissa bits; the address generation module is used for converting input data into a fixed point number and generating a lookup table address; the lookup table storage module is used for storing values after segmentation quantization of the S-type function and outputting corresponding function values; the multiply-add calculation module completes multiply-add operation to obtain an output value of the S-type activation function; and the multiply-add calculation module is used for outputting an activation function result to a subsequent calculation unit. The invention further provides an S-type activation function implementation method using the circuit, and the S-type activation function implementation method has a wide application prospect.

The application discloses a kind of high-efficiency semantic segmentation networks, suitable for medical image analysis, automatic driving etc. The network adopts encoder-decoder architecture, combines CNN with Transformer (converter model), balances global modeling and computational efficiency. Encoder extracts multi-scale features through lightweight convolution, and introduces spatial selection module: its gate convolution splits channel into gate signal and reserved information, and key spatial features are activated and strengthened by Sigmoid activation function; Grouped pooling module extracts details using multi-scale pooling, and restores channels after upsampling and splicing. The decoder fuses multi-scale features and restores resolution through upsampling, enhances efficient channel attention, fuses global max pooling and average pooling, generates channel weight using one-dimensional convolution, and optimizes feature dependence. The design improves small target segmentation accuracy through gate mechanism and multi-scale pooling, is lightweight and easy to expand, and has high precision and practicality.

The application discloses a kind of context-aware mask feature enhancement synthetic speech detection methods, it is related to speech signal processing technical field, method includes: to original audio data pre-processing, the speech data after pre-processing is input to SincNet filter extraction speech timing feature;The speech timing feature is input to CAM-ResNet extraction context-aware local feature;Context-aware local feature is input L-Transformer network calculation global feature;Again through fully connected layer and sigmoid activation function classification;After iteration training, select the best model for synthetic speech detection.CAM-ResNet network in the application can effectively weaken irrelevant features, focus on context-related features, thereby improving the perception of context-effective features, capturing the local details of speech context-aware features;Again after having L-Transformer deep network calculation global feature, to improve the detection accuracy of synthetic speech.

The application discloses a kind of based on multi-scale convolutional neural network agricultural product pesticide residue detection method and system, first acquisition agricultural product hyperspectral image;Then the hyperspectral image is input into multi-scale convolutional neural network to carry out the rapid detection and analysis of pesticide residue;Multi-scale convolutional neural network includes spatial attention module, feature extraction module, classification module;Spatial attention module includes global maximum pooling layer, global average pooling layer, connection layer, dimensionality reduction convolution layer, sigmoid activation function layer and multiplication operation layer;Feature extraction module includes four multi-scale convolution blocks, each multi-scale convolution block includes batch normalization layer, multi-scale convolution layer and LeakyReLU activation function layer;Classifier module is composed of a fully connected layer and a Softmax activation function layer.The application can realize the rapid detection of pesticide residue in crops, and has important significance for guaranteeing food safety and promoting healthy diet.

The application discloses an FPGA accelerator for realizing high-quality new view synthesis based on a neural radiation field; the FPGA accelerator comprises an FPGA and three operation modules, namely, a position coding module, an MLP module and a ray stepping module, and the operation modules are arranged on the FPGA; the MLP module is split into two-stage pipelines; a front-stage pipeline is used to derive body density and a hidden layer, and a rear-stage pipeline is used to derive color information; the front-stage pipeline has eight fully-connected layers, and there are ReLu activation functions between the fully-connected layers; a coordinate signal is subjected to the front-stage pipeline to obtain the body density; the rear-stage pipeline has two fully-connected layers, and input hidden layer information and a view direction, and then is subjected to a sigmoid activation function to obtain the color information. Experimental results show that the hardware architecture designed in the application can accelerate the neural radiation field calculation, and compared with the same algorithm running on a GPU, the running power consumption is far less than that of the GPU, and the generated image quality is similar to that of the GPU.

The present application relates to a sigmoid activation function circuit based on linear broken line fitting, comprising: a data processing module, a compensation module, a first addition module and a second addition module; the data processing module, the first addition module and the second addition module are connected in sequence, and the compensation module is connected with the first addition module; the data processing module inputs and processes input data and outputs to the first addition module; the compensation module inputs and processes input data and outputs corresponding offset to the first addition module; the first addition module accumulates the data output by the data processing module and the offset, and outputs the accumulated data; the second addition module processes the accumulated data to obtain an output value. The present application simplifies the circuit module and introduces the rounding calculation based on the broken line fitting method, effectively reduces the operation power consumption and propagation delay under the condition of ensuring the accuracy, and at the same time realizes smaller hardware resource consumption.

The application relates to an image classification method based on a quaternion Sigmoid activation function, belonging to the technical field of deep learning, which utilizes an image classification network based on a quaternion Sigmoid activation function to perform image classification on an image to be classified. The image classification network comprises a parallel structure layer; the parallel structure layer comprises a splicing layer, a quaternion Sigmoid activation function branch and a convolution branch; wherein the quaternion Sigmoid activation function branch and the convolution branch are in parallel; the quaternion Sigmoid activation function branch performs forward propagation calculation on a quaternion feature matrix by using a quaternion activation function, and then performs convolution operation; the convolution branch performs convolution operation on the quaternion feature matrix; and the splicing layer splices two outputs. The application utilizes the quaternion Sigmoid activation function and the parallel structure matched therewith, can follow the quaternion operation rules, thereby retaining the integrity of image data information and improving the accuracy of image classification.

The invention provides a false news detection method based on progressive field adaptive multi-view learning. The method comprises the following steps: step 1, constructing a multi-modal field adaptive data set; step 2, single-mode feature extraction based on pre-training knowledge enhancement; step 3, constructing a visual and language cross-modal comparison visual angle; step 4, domain-aware dynamic routing and feature fusion; 5, carrying out progressive course learning optimization training; and step 6, inputting the final news representation subjected to field enhancement into a full-connection classifier, calculating the authenticity probability of the news sample, and outputting a final dichotomy prediction result through a Sigmoid activation function. According to the method, general knowledge of a pre-training model is reserved through efficient fine tuning of parameters, heterogeneity characteristics of a specific field are effectively captured, adaptive fusion of multi-modal information in different fields is achieved through a dynamic routing mechanism, and robustness and accuracy of false news detection in a cross-field scene are remarkably improved.

The invention discloses a real-time unknown target detection method based on feature-base interaction and double-flow reasoning. A detection model used in the method comprises a CSPDarknet backbone network, a neck network and detection heads, the detection heads comprise a pair of heads and a pair of multiple heads, and feature-substrate interaction object type branches are embedded in the multiple heads; multi-scale feature maps of an input image are extracted through a CSPDarknet backbone network, the multi-scale feature maps are processed through a neck network, and the multi-scale feature maps output by the neck network are processed through a detection head; in the feature-base interaction object property branch, calculating the cosine similarity of each point and each base vector in the input feature map to obtain the original object property response quantity of the point; and the original object property response quantity of the point passes through a linear mapping layer and a Sigmoid activation function to generate an object property score of each point. The model can be efficiently migrated from a known category to an unknown category, so that the omission ratio of targets without category finding is remarkably reduced, and the excellent generalization performance in the open world is ensured.