21802 results about "Visible spectrum" patented technology

The present invention relates to the technical field of image fusion. Disclosed is a method for fusing infrared light and visible light images. According to the present invention, design is carried out on the basis of a spatial channel attention mechanism and a gradient aggregation residual dense block, a strong texture and weak texture of a feature are retained by integrating Sobel and Laplacian operators while combining the advantages of ResNeXt and DenseNet, and channel information and spatial information of a feature map are refined by means of introducing a spatial and channel attention mechanism, thus the information capture capability of the feature map is increased, and refined spatial and channel feature maps are fused by using a pooled fusion block, to obtain a high-quality fusion feature.

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 discloses a high-precision image processing method and system based on illumination adaptive compensation, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting an original image, and dividing the image into a high-frequency edge layer, an intermediate-frequency texture layer and a low-frequency illumination layer through a multi-scale residual network; acquiring illumination intensity, color temperature and scene categories in real time by using an ambient light sensor and a scene semantic segmentation model, and generating dynamic compensation parameters; carrying out dynamic range expansion on a low-frequency illumination layer based on a physical illumination model, and adjusting the weight of highlight suppression and dark area enhancement through a self-adaptive S-shaped exposure curve; a double-branch generative adversarial network is adopted, noise suppression and super-resolution reconstruction are carried out on the high-frequency layer, and texture detail enhancement is carried out on the intermediate-frequency layer; aligning the data of the depth camera and the infrared sensor with the visible light image through a cross-modal fusion module; and performing tone mapping on the fused image based on human visual characteristics, and outputting an enhanced image with a high dynamic range and reserved details.

The invention provides an on-line quality monitoring method and system based on optical multispectral fusion, and relates to the technical field of multispectral fusion, and the method comprises the steps: collecting visible light and infrared multispectral images of an object to be measured, carrying out the preprocessing and registration, inputting the images into a variational auto-encoder, and carrying out the probability distribution modeling, and obtaining a spectrum basis vector and an initial abundance graph; and constructing a multi-scale morphological feature pyramid, performing graph segmentation in combination with spatial information to obtain a local consistency region, and fusing the local consistency region with the initial abundance graph to obtain an enhanced abundance feature. And fusing the spectrum basis vector and the enhanced abundance feature by adopting a multi-head cross attention mechanism to obtain a multi-spectrum fusion feature. And inputting the fusion features into a dynamic neural network, analyzing a topological connection relationship between nodes, obtaining a structured representation, performing discriminative enhancement, calculating node significance distribution, determining a quality defect area, analyzing a formation mechanism of the quality defect area, and finally generating a quality monitoring report.

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 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.

The invention provides a bridge structure crack position identification method based on deep learning and computer vision, and the method comprises the steps: obtaining a multi-view visible light image and a depth image, fusing the visible light image and the depth image according to registration parameters, and generating a fusion image containing the texture and depth information of a bridge; for the fused image, extracting a crack region by adopting an adaptive threshold segmentation algorithm, and dynamically adjusting the threshold to obtain a crack binary image; according to the crack binary image, calculating three-dimensional coordinates of crack pixel points through a three-dimensional reconstruction algorithm, and performing reconstruction to obtain three-dimensional point cloud data containing crack space position and form information; on the basis of the three-dimensional point cloud data, three-dimensional local feature descriptors are adopted to describe geometric attributes of the surface of the crack, crack fragment parameters are calculated, and a three-dimensional crack model is constructed; based on a deep learning model, a transfer learning strategy is adopted, a self-adaptive transferable bridge crack three-dimensional detection method is formed, and model parameters are updated so that the model parameters can adapt to the characteristics of a target bridge.

The invention belongs to the technical field of photovoltaic panel surface defect detection, and discloses a photovoltaic panel surface defect detection method and system based on physical property analysis. The method comprises the following steps: firstly, acquiring a surface temperature distribution image, surface deformation data, ultrasonic echo data, a spectral image, spectral characteristic data and eddy current signal characteristic data of the photovoltaic panel by respectively utilizing an infrared thermal imager, a laser speckle interferometer, an ultrasonic flaw detector, a visible light multi-band imager and eddy current detection equipment; various abnormal regions such as temperature, deformation, ultrasonic echo, spectral characteristics and eddy current signals are determined; and then determining defect positions by integrating various abnormal regions, and determining the types and sizes of the surface defects of the photovoltaic panel by combining various data corresponding to the defect positions. According to the method, accurate positioning and identification of the surface defects of the photovoltaic panel are realized through a multi-physical property detection means, and the detection accuracy and reliability are effectively improved.

The invention provides an early warning analysis method based on intelligent vision and a server, and the method comprises the steps: obtaining a multi-mode video stream containing a visible light and infrared thermal imaging image sequence through a vision collection device in a monitoring region, and carrying out the spatial-temporal feature extraction of the multi-mode video stream; the method comprises the steps of generating dynamic behavior sequence characteristics including a target object moving track, posture change and thermodynamic distribution characteristics, analyzing the dynamic behavior sequence characteristics through a preset abnormal behavior judgment model in a multi-dimensional matching mode, judging whether the target object triggers abnormal behavior characteristics or not, and if yes, generating a graded early warning signal in combination with an association weight. In addition, according to the spatial distribution and the time length of the abnormal behavior characteristics, the parameters of the acquisition equipment and the coverage range of the monitoring area are dynamically adjusted, and the subsequent video stream anomaly detection precision is optimized, so that intelligent and accurate early warning analysis is realized, and the security efficiency is improved.

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 provides an unmanned aerial vehicle multi-dimensional information fusion method and system based on acousto-optic-electric composite detection. According to the method, acoustic frequency band signals, visible light image sequences and electromagnetic field intensity change data are collected, acoustic characteristic flow, optical characteristic flow and electrical characteristic flow are generated, and based on the time deviation between a low-frequency vibration mode and transient electromagnetic pulses, multi-channel dynamic calibration is carried out on an image distortion area in the optical characteristic flow. The method comprises the following steps: acquiring an acoustic feature flow, an optical feature flow and an electrical feature flow, correcting space-time coordinate parameters of the acoustic feature flow, the optical feature flow and the electrical feature flow, generating a calibration feature flow, carrying out anti-interference fusion processing on the calibration feature flow, extracting multi-dimensional coupling features, inputting the multi-dimensional coupling features into an unmanned aerial vehicle flight path planning model, and generating a three-dimensional space situation model; according to the invention, the physical space-time consistency alignment and noise suppression of the multi-modal data are realized, and the obstacle positioning precision and the dynamic trajectory prediction real-time performance are significantly improved.

The invention relates to the technical field of agricultural intelligent detection, in particular to a fruit tree disease and pest detection method based on machine vision, which comprises the following steps: acquiring visible light, infrared light and ultraviolet light images of a fruit tree at multiple angles through a multispectral camera to obtain complete fruit tree surface information, and preprocessing the acquired images to obtain a fruit tree disease and pest detection result; comprising the steps of denoising, illumination correction and image alignment, extracting feature vectors, including the color, shape and size of scabs, pest distribution and other features, of pest and disease damage from preprocessed images through a convolutional neural network, fusing the pest and disease damage features with environmental data by using a multi-modal data fusion algorithm, and enhancing the accuracy of pest and disease damage classification. Classifying and identifying diseases and pests based on a support vector machine classification algorithm; according to the dynamically adjusted threshold value and suggestions provided by the expert system, a disease and pest diagnosis report is generated, and prevention and control measure suggestions are provided; according to the invention, efficient and accurate detection and management of fruit tree diseases and insect pests can be realized, and the intelligent level of agricultural management is improved.

The invention provides an improved YOLOv11s safety helmet wearing detection model and an optimization method thereof, and relates to the technical field of computer vision target detection. According to the improved YOLOv11s safety helmet wearing detection model and the optimization method thereof, the improved YOLOv11s safety helmet wearing detection model comprises the following modules: a multi-modal fusion module, a space-time analysis module, a domain adaptation module, a topological optimization module and a dynamic architecture module; and the multi-modal fusion module is used for realizing feature decoupling by adopting channel separation convolution based on input RGB and near-infrared images, fusing visible light and thermal radiation features through a dynamic weight distribution algorithm, implementing affine transformation alignment on multi-scale features by utilizing a spatial transformation network, and generating a multi-modal feature graph. Through fusion of visible light and near infrared spectrum features and implementation of dynamic weight distribution, complementarity of target texture and thermal radiation features under a complex illumination condition is enhanced, and the problem of feature distortion of single-mode data in a strong backlight or low-illumination scene is solved.

A system directs an imaging device to continuously capture visible light and fluorescence illumination from a surgical area. The system generates a visible light image stream based on the captured visible light and a fluorescence image stream based on the captured fluorescence illumination. The system operates in a first display mode by directing a display device to display a first video stream based on a first set of at least one of the visible light image stream and the fluorescence image stream. In response to detecting an event that occurs within the surgical area, the system switches from operating in the first display mode to operating in a second display mode by directing the display device to display a second video stream based on a second set of at least one of the visible light image stream and the fluorescence image stream, the first Instructions set being different than the second set.

The invention provides a smoke and fire detection method and system based on deep reinforcement learning and electronic equipment, and relates to the technical field of fire fighting. The method comprises the following steps: acquiring thermal imaging data of a forest area through a thermal imager, obtaining temperature distribution conditions of different positions, and determining position coordinates, the number and specific temperature values of temperature abnormal points for an area with abnormal temperature rise; if the temperature value of the temperature abnormal point exceeds a preset temperature threshold value, judging that the temperature abnormal point is a suspected fire point, and collecting a visible light video image of the area through a visible light camera according to the position of the suspected fire point; smog detection processing is carried out on the collected visible light video image, whether smog exists in the image is judged by analyzing the color, texture, motion and other characteristics of the image, and the concentration, morphological characteristics and diffusion speed of the smog are obtained; the meteorological parameters such as the wind speed and the wind direction of a fire occurrence area are obtained through the meteorological sensor, the meteorological parameters are input into the fire spreading model, and the spreading direction and the spreading range of the fire under the current wind field condition are simulated.

The invention provides a photovoltaic module temperature monitoring method and system, and relates to the technical field of temperature monitoring, and the method comprises the steps: obtaining the standardized temperature data of a photovoltaic module through infrared thermal imaging and visible light image collection in combination with a space registration algorithm and edge calculation. And a temperature prediction model is constructed by using wavelet transform and a graph convolutional neural network, and recognition of a temperature anomaly region is realized. And performing fault analysis by adopting hierarchical clustering and a graph neural network, generating early warning information including fault positioning, early warning levels, fault types and processing guidance, and sending the early warning information to an operation and maintenance management platform, thereby realizing intelligent temperature monitoring and fault early warning of the photovoltaic module.

An illustrative method may include receiving images of a site captured at a same time by a camera, the images comprising a single visible color component image, a first non-visible image associated with first excitation illumination having a first wavelength outside a visible spectrum, and a second non-visible image associated with second excitation illumination having a second wavelength outside the visible spectrum and different than the first wavelength; generating, based on the single visible color component image, a monochromatic image; generating, based on the single visible color component image and the first non-visible image, a first alternate image representative of a first alternate imaging characteristic of the site; generating, based on the single visible color component image and the second non-visible image, a second alternate image representative of a second alternate imaging characteristic of the site; and displaying the monochromatic image combined with the first and second alternate images.

The embodiment of the invention discloses an outer wall thermal insulation defect diagnosis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining an infrared thermal imaging and visible light image sequence of a target building outer wall, the former comprising continuous temperature distribution data, and the latter comprising textural feature data in time-space alignment with the latter; performing dynamic temperature gradient analysis on the infrared thermal imaging image sequence to generate a three-dimensional heat conduction abnormal map, extracting surface deformation characteristics from the visible light image sequence to generate a structure deformation distribution map, and performing multi-modal characteristic fusion on the two to obtain a joint defect characteristic matrix; performing defect type classification and region positioning on the matrix based on a pre-trained deep residual neural network model, outputting a defect type identifier and a corresponding region boundary coordinate, and finally generating a diagnosis report containing a repair priority score and a material matching suggestion according to the defect type identifier and the corresponding region boundary coordinate, and sending the diagnosis report to a user terminal for visual display. And efficient and accurate external wall thermal insulation defect diagnosis is realized.

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 relates to the technical field of industrial intelligent monitoring, in particular to a remote online monitoring method and system based on machine vision and artificial intelligence, and the method comprises the following steps: synchronously collecting a visible light video stream, infrared thermal imaging data and three-dimensional vibration spectrum data through an edge calculation node, and forming a multi-modal sensing data set; performing cross-domain feature alignment processing on the data set to generate a space-time synchronous composite feature matrix; inputting the composite feature matrix into a cascaded deep learning model, and outputting a three-dimensional diagnosis vector which comprises an equipment health state, an abnormal region and a fault probability; and matching the three-dimensional diagnosis vector with a historical reference vector through a DTW and cosine similarity combination algorithm to generate a deviation index, and outputting a self-adaptive alarm threshold and a maintenance suggestion scheme according to the deviation index. The method has the advantages of high multi-modal fusion degree, light diagnosis model and intelligent and adjustable alarm mechanism, and is suitable for real-time remote operation and maintenance in a high-risk industrial scene.

The invention belongs to the technical field of industrial measurement, and discloses a pump machine metal shell size detection method based on image analysis, which comprises the following steps: synchronously acquiring image data of a pump machine metal shell in a visible light wave band and a near-infrared wave band; the contrast ratio of image data is optimized through a dynamic exposure control technology, and a high-dynamic-range multispectral image is obtained; three-dimensional geometric information of the pump machine metal shell is obtained, registration fusion is carried out on the three-dimensional geometric information and the high-dynamic-range multispectral image, a dense three-dimensional point cloud model with multispectral textures is constructed, and a multi-frequency heterodyne algorithm is adopted to process a high-reflection area in the dense three-dimensional point cloud model; the method comprises the following steps: collecting a multi-angle reflection image of a pump machine metal shell by rotating a linear polarizer, calculating a Stokes vector to extract a diffuse reflection component, carrying out diffuse reflection component constraint and brightness suppression on a high-reflection area based on a CIE-Lab color space, and outputting a dense three-dimensional point cloud model after texture enhancement; and the size detection precision of the pump machine metal shell is improved.

The invention discloses a multi-sensor fusion positioning mapping and thermal imaging device and a computer readable medium. The equipment integrates positioning mapping sensors such as a thermal imaging camera, a visible light camera, a laser radar and an inertial navigation system, and is specially designed for high-risk and complex fire emergency environments. Through an end-cloud integrated cluster cooperation system and a multi-link stable transmission technology and in combination with intelligent decision support, high-precision real-time positioning and dynamic 3D temperature measurement mapping can be realized in an environment in which satellite navigation signals are weak or unavailable. The multi-mode portability and environmental adaptability design of the system ensures the high efficiency and safety of an operator / unmanned system under extreme conditions, and the advanced data processing and analysis technology of the system significantly improves the accuracy and response speed of rescue operation. Due to the innovative characteristics, the method has remarkable practical application value and market prospect in the field of fire-fighting emergency rescue.

A device, system and method comprising computer vision techniques for fire prevention / detection and risk assessment, as well as for determining deviations from an ideal operational state. The present invention includes for example systems and methods which leverage data collected by camera systems composed of infrared and visible light sensors to detect and / or prevent a fire from starting, and additionally, use this data to determine a risk assessment for the building. The present invention also provides for example a system and method for monitoring and controlling safety risks in indoor industrial environments by determining deviations from an ideal operational state using computer vision techniques and game-theoretic competitive ranking frameworks.

The invention discloses an intelligent photoelectric theodolite aerial target positioning and tracking system, which relates to the technical field of photoelectric detection, and comprises a multi-mode photoelectric sensor module integrating visible light, infrared thermal imaging, a laser radar and a polarized light sensor and supporting spectrum adaptive switching; the dynamic noise suppression processing module is used for eliminating environmental interference based on a time-space domain hybrid filtering algorithm; the multi-target tracking control module adopts a time-sharing partition scanning strategy and a graph neural network data association algorithm; the anti-interference servo driving module is used for realizing stable tracking under strong disturbance through inertial navigation-visual fusion compensation; and the edge computing platform is used for deploying a lightweight deep learning model to complete target recognition and trajectory prediction. According to the invention, through interdisciplinary collaboration of quantum dot materials, graph neural networks and physical equation constraints, the bottleneck of a single technology is broken through; and through closed-loop optimization of dynamic anti-interference and edge intelligence, full-link enhancement of'perception-decision-execution 'is realized.

Exemplary methods, systems, apparatus, and computer programs are disclosed for an unmanned aerial system (UAS) inspection system that includes an unmanned aerial system and analysis system for exterior building envelopes and energy performance evaluation and simulation. The UAS can autonomously and systematically collect data for a building's exterior using a payload comprising (i) nondestructive testing (NDT) sensors configured for imaging (visible, infrared, or more) the building and (ii) one or more multi-spectral sensors (LiDAR, ultrasound, radar, or more). The acquired sensor data are provided to an analysis system comprising computer vision (CV) and signal processing modules configured to analyze the acquired data to i) identify building objects (doors, windows, rooftop units, and others) ii) characterize envelope properties (components, heat resistivity, or others) and 3) identify initial thermal anomalies (thermal bridges, physical defects, or infiltration / exfiltration) in a processing pipeline.

The invention relates to the technical field of road crack detection, in particular to a road crack detection method and system based on a fused image. The method comprises the following steps: acquiring road multi-source monitoring data including a visible light image, infrared thermal imaging data and laser radar point cloud data, and performing multi-modal image fusion and road three-dimensional point cloud reconstruction to generate a fused road image and road three-dimensional modeling data; performing crack curvature analysis based on the fused road image to generate crack curvature data; performing reflection crack contour recognition and positioning on the fused road image through the crack curvature data to generate reflection crack initial positioning data; obtaining road base material data; and performing reflection crack stress field reconstruction on the road area according to the reflection crack initial positioning data to obtain a reflection crack stress field. According to the invention, through multi-modal fusion, curvature identification, stress field modeling and crack channel analysis, the accuracy and strain of road reflection crack detection are improved.

The invention provides an access control system based on face recognition and a recognition method, and the system comprises a multi-modal image collection subsystem which integrates a visible light camera, a depth camera and an infrared thermal imaging camera and is used for synchronously collecting a visible light image, a depth image and an infrared thermal imaging image of a face; the image preprocessing unit is used for carrying out corresponding preprocessing on the image; the feature extraction network module is used for extracting image features; the multi-modal feature fusion and alignment module is used for calculating a mutual information matrix by adopting a feature fusion strategy based on mutual information maximization and adaptive weighting, determining an adaptive weight and performing weighted summation, and performing feature alignment by applying a method based on canonical correlation analysis; and the face recognition classification and verification module verifies the image by adopting a multi-level fusion strategy. According to the method, the false recognition rate and the rejection rate are effectively reduced, the strong anti-cheating capability is achieved, and cheating attacks of various fake face means can be accurately recognized and resisted.

The invention provides a high-speed rail part crack real-time detection method, and belongs to the technical field of image detection based on computer vision. Obtaining a hyperspectral image, a visible light image, three-dimensional point cloud data and eddy current signal data of the surface of the high-speed rail part; performing spatial registration on the high-spectral image and the visible light image on the surface of the high-speed rail part, and performing time registration on the three-dimensional point cloud data based on the eddy current signal data; feature enhancement and standardization processing are carried out; performing feature extraction and fusion on the obtained standardized multi-modal data based on a multi-modal feature fusion network to obtain a fusion feature map representing crack details of the high-speed rail parts; based on a self-adaptive crack segmentation method, a crack contour is extracted from the point cloud, and then whether the part has a crack or not and the length and depth of the crack are calculated. According to the invention, three kinds of modal data are creatively integrated, the information dimension limitation of single-modal detection is broken through, and all-weather and non-contact intelligent efficient diagnosis of submillimeter cracks is realized under complex working conditions.

The invention provides an infrared thermal imaging building facade defect intelligent diagnosis method based on multi-modal fusion, and relates to the technical field of building detection.The method comprises the steps that infrared thermal imaging, visible light images and three-dimensional point cloud data are synchronously collected to construct a multi-modal data set; segmenting a hot spot region by adopting an improved morphological watershed algorithm and extracting contour and temperature features; recognizing a surface crack and peeling area based on a double-branch attention network to generate a texture defect feature map; curvature distribution and thermal deformation gradient are calculated through space registration constrained by a heat conduction equation; multi-source features are fused to calculate a hot spot form dispersion TSMD and a structure risk quantification factor SRQF; constructing a defect risk decision matrix to output defect types, positions and risk levels; and superposing the diagnosis result to a BIM model to generate a three-dimensional visual report and predicting a thermodynamic evolution trend. The multi-modal data collaborative analysis is realized, the defect risk is accurately quantified, and the problems of poor anti-interference performance, inaccurate segmentation and large registration error of a traditional method are solved.

The invention provides a methane leakage anomaly detection system and method based on dual-light fusion, and belongs to the field of industrial safety detection. According to the system, a double-light-path holder type laser detector is arranged in a monitoring area, and methane concentration and image data are synchronously acquired through a 1653nm near-infrared laser module and a visible light image module. Time features (mean values, trends and the like) are extracted from the concentration data through time sequence analysis, and spatial features (edges, textures and the like) are extracted from the image data through deep learning; the system fuses time and space features to generate a comprehensive feature vector, reduces the calculation complexity through a dimension reduction technology, and retains core features at the same time. And constructing an anomaly detection model based on an unsupervised learning algorithm, identifying anomaly features in real time, and triggering an alarm. The system realizes real-time and accurate detection of methane leakage, and enhances the reliability and efficiency of industrial safety.