7624 results about "Infrared image" patented technology

The invention discloses a visible light and infrared image fusion method based on cross-modal dynamic collaboration. The method comprises the following steps: respectively extracting texture detail features of a visible light image and thermal radiation features of an infrared image through a visible light encoder and an infrared encoder; spatial alignment and channel complementarity optimization of cross-modal features are realized by using a heterogeneous attention collaboration module; and performing layered fusion on deep semantics and shallow detail features through a dynamic gating multi-scale decoder to generate a high-resolution fusion image. According to the method, the problems of feature dislocation, detail loss and unreasonable fusion weight distribution caused by modal difference in the prior art are solved, the detail fidelity, the thermal target saliency and the complex scene adaptability of the fusion image can be remarkably improved, and a high-robustness fusion result is provided for low-illumination environment perception and multi-modal target recognition.

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention relates to the technical field of computer programming languages, and particularly discloses a coal-fired power plant safety monitoring system and method. The system comprises a multi-modal data fusion platform, a federal learning agent network and a digital twinborn simulation engine. The edge computing node carries out unified acquisition and feature extraction on multi-source heterogeneous data through multi-protocol conversion, quantum noise suppression and a preprocessing chipset; the multi-modal data fusion platform realizes semantic mapping based on a knowledge graph, and fuses time-space correlation characteristics of data such as an infrared image and gas concentration by adopting a time-space encoder and a cross-modal attention mechanism. According to the method, the problems of early warning delay and high false alarm rate caused by low multi-source data decentralized processing efficiency and insufficient nonlinear correlation analysis in a traditional scheme are solved, efficient data fusion, complex risk accurate prediction and automatic safety response are realized, and the real-time performance and reliability of a coal-fired power plant monitoring system are remarkably improved.

The invention relates to a high-voltage equipment defect detection method based on deep learning and multispectral image fusion, and relates to the technical field of electric power high-voltage equipment state detection. The method comprises the following steps: acquiring an ultraviolet image, an infrared image and a visible light image of the surface of the high-voltage equipment; carrying out image pixel feature-based fusion processing on the ultraviolet image, the infrared image and the visible light image through an image fusion method; establishing a high-voltage equipment defect detection model, and training the high-voltage equipment defect detection model by using the fused image data to obtain a high-voltage equipment defect identification model based on the YOLO-STrans multispectral fusion network; and inputting the ultraviolet image, the infrared image and the visible light image of the outer surface of the power high-voltage equipment into a high-voltage equipment defect identification model to obtain a fault identification result of the to-be-detected power high-voltage equipment. The method can improve the recognition precision of the extremely early insulation degradation and temperature anomaly defects of the surface of the high-voltage power equipment.

A target detection method, system and device based on cross-modal fusion and an attention guiding mechanism and a medium, the target detection method comprises the following steps: obtaining a visible light-infrared image pairing data set, and carrying out data processing and division on the visible light-infrared image pairing data set to obtain a training set, a verification set and a test set; constructing a multi-modal target detection network; setting network training parameters; training and optimizing the multi-modal target detection network by using the training set, outputting a training weight file after the training is finished, verifying the trained weight file by using the verification set, and selecting the weight file with the highest precision as an optimal weight file; loading the test set and the optimal weight file into a multi-modal target detection network, and performing target detection on the test set to obtain a target detection result; the system, the device and the medium are used for bearing and implementing the method. The method has lower false detection and false detection conditions, and improves the robustness and accuracy of target detection.

The invention discloses an unmanned aerial vehicle electric power inspection image intelligent analysis method and system based on deep learning and multi-modal fusion and a medium thereof, and relates to the technical field of electric power equipment detection. The method comprises the following steps: planning an optimal inspection path by adopting an A * algorithm to realize multi-sensor synchronous data acquisition; adaptive histogram equalization and defogging processing are carried out on the visible light image, non-uniformity correction and temperature calibration are carried out on the infrared image, and filtering and registration are carried out on point cloud data; constructing a multi-scale feature fusion network based on improved VGGNet-16, and introducing deformable convolution and a cross-modal attention mechanism to realize multi-source data fusion; defect detection is carried out based on a three-level template library and a feature map cross-correlation algorithm, and the precision is improved in combination with non-maximum suppression and sub-pixel positioning; and finally generating a detection report containing defect types, positions and maintenance suggestions. According to the invention, the automation level and the detection precision of power inspection are obviously improved.

The invention belongs to the technical field of structure monitoring systems, and discloses a structure monitoring system and method for civil engineering, and the method comprises the steps that a data collection module collects structure data; the cruise monitoring mechanism collects a high-definition image, an infrared image and laser point cloud data; the environment data acquisition module acquires environment data; the image analysis module identifies abnormal conditions; the infrared data analysis module detects internal defects; the laser data analysis module accurately measures the deformation of the structure; the comprehensive analysis module performs comprehensive evaluation; the risk prediction module fuses multi-source data and predicts potential problems and risks; when an abnormal condition or a potential problem risk is monitored, the alarm module gives an alarm in time. According to the invention, through combination of sensor monitoring and unmanned aerial vehicle cruise monitoring, the structure of civil engineering is monitored in an omnibearing manner; the comprehensive analysis module is used for integrating the multi-source monitoring data to carry out comprehensive evaluation and abnormity judgment; and the risk prediction module fuses monitoring results of multiple modules and predicts potential problems and risks.

Vehicle computing systems may receive and fuse long-wave infrared data with radar data to detect and track objects in low-visibility driving environments. In some examples, radar data including position and velocity data may be projected over infrared image data to detect, classify, and track infrared-emitting objects. Machine-learned transformer models with attention also may be trained to output object detections based on combined infrared and radar data. The fusion of infrared and radar data may be used individual and / or may be synchronized with other sensor modalities. In some examples, the fusion and analysis of infrared and radar data may be used in specific low-visibility driving environments, using low-light, fog, and shadowed areas of the environment, to detect and track infrared-emitting and / or moving objects such as pedestrians and animals that may be obscured from detection by other sensor modalities.

The invention discloses a multi-source information collaborative power equipment three-dimensional temperature field construction method. Firstly, an infrared camera, an IMU and a laser radar are utilized to obtain an accurate external parameter relation through joint calibration, point cloud distortion is eliminated, angular points and plane points are extracted, a re-projection residual error, a distance sampling residual error and an IMU pre-integration residual error are constructed, an error state iteration Kalman filter is adopted to optimize a global pose, and positioning is achieved. Providing a self-supervised depth completion network, combining an infrared temperature image and a sparse depth map generated by a laser radar as input, adopting a depth completion strategy guided by an infrared image, estimating relative motion of adjacent frames by using pose information, introducing a feature alignment module to reduce alignment errors, and combining the depth map, the infrared image and IMU data to obtain a self-supervised depth completion algorithm; and efficient construction of the three-dimensional temperature field of the power equipment is realized. According to the invention, the three-dimensional temperature field of the power equipment is constructed more accurately, and the capability of the substation inspection robot for state monitoring and fault diagnosis of the power equipment is improved.

The invention relates to a human shape posture recognition method and system based on image analysis, and the method comprises the steps: inputting the reference human shape data of a monitored object, and collecting the multi-modal data of an RGB image, an infrared image and inertial measurement data in a target scene in real time; space-time alignment processing is carried out, and corresponding features are fused; image space features are extracted, time sequence modeling is carried out on inertial data, and dynamic weighted fusion of two paths of network outputs is realized through a gating mechanism; human body basic joint points are positioned, and refined posture vectors including joint angles and limb relative positions are generated; according to attitude data and environment information collected in real time, adaptively adjusting an attitude classification threshold value and a similarity measurement standard, and predicting abnormal behaviors in a future time period; and when the abnormal behavior is predicted, triggering to execute a preset safety measure. Multi-modal data can be efficiently fused, attitude features can be accurately extracted, an identification strategy can be adaptively adjusted, and human shape attitude identification with behavior trend prediction capability can be realized.

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

The invention discloses a multi-modal perception-based fan blade defect detection system and method, the system comprises a data fusion module and a depth feature extraction and defect detection module, the data fusion module is used for obtaining data of multiple modals of the surface of a to-be-detected fan blade in the same scene, and the depth feature extraction and defect detection module is used for extracting the data of multiple modals of the surface of the to-be-detected fan blade; preprocessing, feature extraction and feature fusion are carried out on the data of the multiple modes, a fused feature map is obtained, and the data of the multiple modes comprise a visible light image, an infrared image, laser radar data and thermal imaging data; and the depth feature extraction and defect detection module is used for introducing a dynamic multi-scale convolutional network and adaptive context convolution, performing multi-scale feature extraction on the fused feature map to obtain multi-scale feature information, and obtaining a fan blade defect detection result according to the multi-scale feature information. According to the invention, the limitation of a single-mode detection mode is solved, and the defect detection precision and adaptability of the system are improved.

The invention discloses a de-noising diffusion model driven texture enhanced infrared and visible light image fusion method and system, and relates to the technical field of image processing and computer vision. The method comprises the following steps: performing multi-scale space-time modeling on infrared and visible light images; enhancing high-frequency texture features of the infrared image through an edge detection convolutional network; based on a bidirectional multi-scale convolution module and a bidirectional attention fusion module, capturing a remote dependency relationship and encoding multi-scale information to ensure full utilization of global information and accurate capture of local details; and optimizing the structural fidelity and color intensity distribution of the fusion result based on an adaptive structural similarity loss function and multi-channel intensity and texture loss so as to improve the quality of the fused image. According to the method, the process of diffusion feature extraction is enhanced by fusing the de-noising diffusion model and the texture enhancement strategy. Meanwhile, a bidirectional multi-scale convolution technology and an attention mechanism are combined, so that comprehensive utilization of global information and accurate mastering of local details are ensured.

The invention discloses a semantic alignment method for infrared image and microwave non-image information fusion. The method comprises the steps that infrared image and microwave non-image information fragments in the same scene are collected; carrying out preprocessing operation on the acquired infrared image and microwave non-image information; extracting spatial and semantic features of the infrared image, and extracting frequency domain features of microwave non-image information; embedding the infrared image features and the microwave non-image features into the same high-dimensional semantic space; calculating the similarity in semantic representation; and carrying out global optimal matching by adopting a Hungary algorithm. Respectively performing wavelet decomposition on the infrared image and the microwave features by adopting a wavelet transform fusion algorithm, and extracting low-frequency and high-frequency coefficients under different resolutions; by constructing a unified semantic representation space and an efficient feature fusion mechanism, the difference between the infrared image and the microwave data in the feature expression form and the semantic level is effectively solved, complementary information of two modes is fully mined, and the target recognition and environment perception ability in a complex environment is improved.

The invention discloses a global and local multi-scale fused infrared guide low-light image enhancement method, which belongs to the technical field of computer vision, and specifically comprises the following steps: constructing an image enhancement network; constructing a data set, and training the image enhancement network; and acquiring a low-illumination image and a near-infrared image in the same scene, and inputting the low-illumination image and the near-infrared image into the trained image enhancement network to obtain an enhanced image. According to the global and local multi-scale fused infrared guide low-light image enhancement method, the problems that in the prior art, calculation is complex, the effect is single, illumination lifting is not considered, and denoising is difficult to achieve are solved.

The invention provides a photovoltaic module fault detection system, method and device based on infrared image hot spot detection, and a storage medium, and belongs to the field of photovoltaic module fault detection. The system comprises a temperature difference boundary extraction module, an image sequence registration module, a hot spot track identification module, an abnormal dynamic screening module and a fault hot spot confirmation module. Pixel difference screening is carried out by setting a temperature difference threshold value, a boundary communication structure is established, frame-level displacement calculation between time sequence images is introduced to realize coordinate alignment, hot spot center points in continuous frames are extracted to form a motion path, abnormal point screening and time node labeling are carried out in combination with path displacement characteristics, and the time sequence image is obtained. According to the method, the boundary area growth rate and temperature change double factors are fused to screen fault areas, dynamic tracking and accurate detection of abnormal hot spots are achieved, the logic relevance between abnormal behavior recognition and fault judgment is enhanced, and the integrity of photovoltaic module fault information extraction and the accuracy of hot spot recognition are guaranteed.

Disclosed is a joint imaging system based on unmanned aerial vehicle platform and an image enhancement fusion method, which are used for realizing thermal infrared oblique photogrammetry and real-time fusion of thermal infrared images and visible light images. The system includes a flying unit as a load platform, a shutter control system for controlling the operation of a load camera, a posture control system for recording the movement track and POS data of the flying unit, an airborne image transmission system for communicating with ground equipment, and an onboard computing unit with an image processing module for receiving the output images and performing image processing and fusion, so as to quickly collect thermal infrared images and visible light images, and improve the recognition capability of feature points in the process of thermal infrared oblique photogrammetry, and ensure the success rate and accuracy of building the three-dimensional temperature field model.

The invention provides a water area safety patrol method and system based on multi-modal feature analysis, and relates to the technical field of image recognition. The method comprises the steps of collecting a visible light image, an infrared image and laser radar data corresponding to a target water area, and performing time synchronization and space alignment to obtain visible light alignment data, infrared alignment data and laser radar alignment data; in response to the condition that the definition of the visible light image is higher than a quality reference value, carrying out edge detection and region segmentation processing on the visible light alignment data, and generating a candidate region set containing a target contour; for the infrared alignment data corresponding to the candidate region set, screening out a thermal anomaly region from the candidate region set; and carrying out three-dimensional space feature extraction and motion state feature extraction on the laser radar alignment data corresponding to the thermal anomaly region, and carrying out anomaly identification on the thermal anomaly region. According to the scheme, high-precision safety patrol of the target water area in various environments can be realized.

The invention discloses a space-time consistent multi-modal feature fusion-based aerial target detection method. The method comprises the steps of 1, obtaining a visible light and infrared image pair sequence of an aerial target; 2, constructing a space-time consistent air target detection network, wherein the space-time consistent air target detection network comprises a time domain enhanced backbone network, a space domain fusion module, a bidirectional feature pyramid and a Head module; 3, constructing a total loss function; and 4, training to obtain an optimal target detection network model, carrying out multi-scale detection on the space-time consistency characteristics, finally outputting the position and the category of the target, and returning a result with a detection frame. According to the invention, through space-time consistency modeling and cross-modal attention fusion, the detection precision and robustness of the aerial target in a complex scene are effectively improved, and meanwhile, a lightweight design is adopted to meet the real-time requirement, and the method is suitable for the fields of unmanned aerial vehicle supervision, low-altitude security and protection and the like.

The invention discloses a multi-modal sensor information fusion-based target detection method and system. The method comprises the steps of obtaining a visible light image and an infrared image of a preset area; based on a depth estimation network, respectively extracting depth information of the visible light image and the infrared image to obtain a depth estimation map after fusion of the visible light image and the infrared image; feature information of the visible light image and the infrared image is extracted based on a feature extraction network, and scene perception features of the depth estimation map are extracted; and carrying out feature fusion on the feature information of the visible light image and the infrared image and the scene perception feature of the depth estimation map by using a feature fusion network to obtain a depth estimation fusion image in a preset region, and carrying out to-be-recognized target detection on the depth estimation fusion image based on a target detection network. Through complementary characteristics of visible light and infrared images, depth information and scene perception capability of the images are enhanced based on a depth estimation network and a feature extraction network, and image target identification accuracy and robustness are improved.

The invention discloses a heterogeneous perceptual attention fusion network-based multi-modal image target detection method, which aims at the target detection problem in a complex scene, utilizes a double-backbone network to respectively perform multi-scale feature extraction on visible light and infrared images, generates a feature pyramid containing different levels of details and semantic information, and performs feature extraction on the visible light and infrared images. Through a heterogeneous perception enhancement module, a channel-space bidirectional attention mechanism is constructed, cross-modal heterogeneous enhancement is carried out on features of different levels, key region information is highlighted, by means of a refined channel attention fusion module, discriminative features are effectively amplified, modal specific noise is suppressed at the same time, and in a target detection link, an elliptical dynamic CIoU loss function is adopted, so that the target detection accuracy is improved. According to the method, the external connection ellipse or the inconnection ellipse is dynamically selected according to the proportion of the target to calculate the intersection-to-union ratio, geometric alignment of the anchor frame and the target is optimized, the detection precision and robustness of the multi-modal image target are effectively improved, and the method has wide application prospects in the fields of automatic driving, anomaly detection and the like.

The invention provides a target detection method and system based on infrared and visible light image fusion, and relates to the technical field of target detection, and the method comprises the steps: obtaining to-be-detected visible light and infrared images; inputting the obtained visible light and infrared images to be detected into the trained YOLOv8 double-flow network model, and outputting a target detection result; wherein the training of the YOLOv8 double-flow network model comprises the following steps: acquiring data of visible light and infrared images in different time periods of a specified area, and constructing a target joint data set; a YOLOv8 double-flow network model is built; and training the built YOLOv8 double-flow network model by using the built target joint data set to obtain a trained YOLOv8 double-flow network model. According to the method provided by the invention, the YOLOv8 double-flow network model is established, and the fusion module is used for carrying out feature fusion and target detection on the visible light and infrared images, so that the problem that the CNN is difficult to capture the long-distance dependence and global relationship between different modes is solved, and the accuracy and robustness of target detection are improved.

The invention relates to the technical field of knowledge graph construction, and discloses a transformer substation knowledge graph construction and optimization method based on multi-view learning, which comprises the following steps of: processing transformer substation multi-source data through a heterogeneous model; multi-source heterogeneous data of operation and maintenance texts, monitoring data, regulations and rules and infrared images of substation equipment are mapped to a unified feature space through linear projection, a multi-mode positive and negative sample pair of the same equipment is constructed, the distance of related equipment features is shortened by adopting comparative learning, projection matrixes of various data are dynamically optimized, and the multi-mode heterogeneous data of the substation equipment is obtained. And jointly detecting the power transformation equipment entity boundary in the operation and maintenance text and the equipment monitoring data, fusing the multi-modal equipment characteristics through an attention mechanism, and reasoning the relationship type between the equipment. According to the method, the multi-source heterogeneous data of the transformer substation and expert experience are deeply fused, so that the fragmentation and staticization problems of a traditional knowledge management system are effectively solved, and the accuracy of state perception and fault diagnosis of the power equipment is remarkably improved.

The invention relates to the technical field of infrared image recognition, and discloses a hot target extraction method and system based on infrared image recognition, and the system comprises a multi-sensor collection module, a data dimension reduction module, a data verification module, a segmentation optimization module, and a dynamic feedback module. Multi-modal data dimension reduction processing is achieved by collecting infrared images, visible light images and environment dynamic parameters and combining Laplacian feature mapping, and a target area is extracted through geometric verification and topological analysis. An energy optimization model and a distribution alignment method are adopted to complete accurate segmentation of a target area, and meanwhile, segmentation model parameters are adjusted in real time based on environment dynamic parameters, so that the system stability is ensured. According to the method, high-precision and robust extraction of the thermal target is realized in a complex dynamic environment, the problems of fuzzy target boundary, noise interference and insufficient dynamic environment adaptability in the prior art are effectively solved, and the method is suitable for the fields of military reconnaissance, fireproof monitoring, electric power inspection and the like.

The invention discloses a multi-modal target detection method based on feature enhancement and alignment fusion. The method mainly comprises the following steps: (1) extracting features of a visible light image and an infrared image by using a backbone network, and better guiding the backbone network to extract related features from two modes and enhance feature representation by using an FEM in multiple stages of extracting the image features by the backbone network; (2) performing global-to-local alignment on the feature maps of the two modalities after feature enhancement by using FAM (Frequency Administration and Maintenance); (3) utilizing FFM to fuse the aligned feature maps from the two modalities; (4) using SFFM to further fuse the deeper feature map after cross-modal fusion in the step (3); and (5) inputting the fused feature maps under different scales into a detection head to obtain a target detection result. The method is mainly applied to the technical field of target detection in computer vision, and has wide application prospects in the fields of video monitoring, automatic driving and the like.

The invention discloses a target detection method and device based on cross attention multi-scale fusion and a medium, and relates to the field of target detection, and the method comprises the steps: obtaining a visible light image and an infrared image of a to-be-detected target, and carrying out the target detection through employing a trained cross attention multi-scale fusion detection network; wherein the cross attention multi-scale fusion detection network comprises a backbone network, a background sensing module, a cross attention fusion module and a detection head; the backbone network is used for extracting features of different scales; the background sensing module is used for predicting illumination and contrast; the cross attention fusion module is used for performing cross fusion on the features extracted by the backbone network by adopting differential attention and homogeneous attention according to the illumination and contrast predicted by the background sensing module to obtain a multi-modal fusion feature map; and the detection head is used for determining the position and category of the to-be-detected target according to the multi-modal fusion feature map. According to the invention, the accuracy of target detection is improved.

The invention relates to an infrared and visible light image fusion method based on a cross-domain Transform, and belongs to the field of computer image processing. The method comprises the following steps: respectively carrying out preprocessing operation on an infrared image and a visible light image to obtain a training data set; an end-to-end image generator network is designed, an encoder module is used for extracting deep semantic features of an infrared image and a visible light image, a fusion module introduces an axial attention mechanism to enhance the global modeling capability of the features, and feature fusion is carried out in combination with information of a spatial domain and a frequency domain; the fused features are gradually recovered to an image space through a decoder module, and a fused image is generated; constructing a fusion loss function module, and guiding the network to focus a significant feature difference between the source image and the fusion image based on a comparative learning idea; and finally, inputting the infrared and visible light image Y channel into the network model, generating a fusion image, completing a training process, and realizing unified optimization of fusion performance and visual quality.

The invention discloses an infrared small target detection method fusing local prior and a multi-scale global background, and the method comprises the steps: firstly obtaining image data containing an infrared image and a mask label corresponding to the infrared image, and carrying out the preprocessing; secondly, a target detection model of an encoder-decoder architecture is constructed, an encoder comprises a local detail prior mining branch and a multi-scale global background perception branch which are parallel, step-by-step feature extraction is performed on the preprocessed image data, and a decoder comprises a progressive feature fusion decoding branch; and inputting the features of each level of the encoder double branches into decoder branches for decoding step by step to obtain a detection result. And finally, a weighted depth supervision mechanism is introduced in training, auxiliary prediction output is set in a plurality of decoding layers, and weighting loss is calculated. According to the method, the problems of insufficient local detail modeling, insufficient multi-scale global background perception of Mamba, difficulty in global and local feature fusion and the like in the existing method are solved, and the detection precision of the infrared small target is improved.

The invention discloses an unmanned aerial vehicle monocular vision forest fire positioning method, and aims to realize rapid detection, accurate positioning and three-dimensional reconstruction of an early fire. According to the method, a comprehensive fire monitoring system is constructed by integrating various sensors such as an unmanned aerial vehicle platform, a visible light camera, an infrared camera and an inertial measurement unit (IMU). Through monocular vision simultaneous localization and mapping (SLAM) technology and IMU-GNSS information fusion, the attitude of the camera is recovered in real time. And carrying out three-dimensional positioning on a fire point by utilizing a triangulation method and combining semantic information of visible light and infrared images. According to the method, the SfM (Structural From Motion) technology is adopted, the attitude of a camera and image feature points are combined, and the attitude of the camera and the three-dimensional point cloud are optimized through GBA (Global Bundle Administration), so that the positioning precision is improved. The method has the characteristics of high efficiency, accuracy, real-time performance and remarkable cost effectiveness.

The invention relates to a multi-modal large model-based low-slow small flight target identification and classification method and system, and aims at solving the problem of monitoring and identification of low-slow small flight targets in a complex scene. According to the method, various sensor data including visible light images, infrared images, radar signals, sound features and radio signals are fused, the multi-mode large model is used for processing and fusing data of different modes, combined features are extracted, efficient detection and classification of low-speed small targets are achieved, and the method has an automatic early warning function. The method has the advantages of real-time response, automatic processing and high recognition precision, and is suitable for low, slow and small flight target defense scenes such as airports, military bases and urban safety.