5696 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 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 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 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.

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 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 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 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.

The invention provides a visible light-infrared image structure adaptive fusion crack detection method and system. The method comprises the following steps: inputting a visible light image and an infrared image of a to-be-detected object into a trained multi-level adaptive fusion detection model; the model comprises a visible light-infrared double-branch encoder and an edge structure guide decoder. Each encoder comprises n feature coding modules used for extracting coding features of multiple levels of the visible light image and the infrared image; fusing visible light and infrared coding features through n multi-mode gating attention modules, and outputting n features of different sizes; the edge structure guiding decoder correspondingly comprises n feature decoding modules, and the n feature decoding modules are used for decoding features of different sizes and inputting the features into the Token-KAN module; through n Token-KAN modules, fracture prediction results of n levels are obtained; and carrying out weighted fusion on the crack prediction results of the n levels to obtain a final crack prediction result. The accuracy of crack detection can be improved.

The invention relates to an unmanned aerial vehicle multi-modal visual positioning method and system in a GNSS denial environment, and the method comprises the steps: constructing a visual inertial mileage calculation method based on deep learning feature extraction and matching, calculating the relative positioning of an unmanned aerial vehicle, and outputting to obtain high-frequency 6-DoF pose information; performing scene matching of infrared RGB image multi-modal fusion based on the Officient-LoFTR, and obtaining absolute positioning in a world system by solving a relative pose between a current key frame picture and a matched satellite image; a factor graph optimization mode is adopted to carry out self-adaptive weight information fusion according to the confidence coefficient of absolute positioning and relative positioning under a world system, and 6-DoF pose information with high frequency and higher precision is output; based on high-frequency and higher-precision 6-DoF pose information, after the unmanned aerial vehicle is recovered, all aerial images and satellite images are fused and updated. The system comprises a calculation module and a fusion updating module. According to the invention, more accurate and high-frequency pose output can be provided.

The invention discloses a power transmission image defect detection and defect duplicate removal method and system based on a deep learning image segmentation algorithm, and belongs to the technical field of intelligent inspection of power equipment. According to the method, real-time tower identification and adaptive shooting are realized through a lightweight YOLO model deployed at the edge end of an unmanned aerial vehicle; pixel-level segmentation is carried out on the infrared image by using an MSAN-Net network, the network integrates a ResNet encoder, a cross-scale attention mechanism and a multi-level feature pyramid, and boundary learning is enhanced by using a composite loss function; based on a multi-view three-dimensional reconstruction technology, two-dimensional defects are mapped into space rays through feature point matching and pose estimation, and defect de-weighting is achieved through ray intersection judgment. Through the MSAN-Net network, the segmentation precision of the infrared component under a complex background is remarkably improved through an attention mechanism and multi-scale feature fusion, and the problem of repeated defect detection in multi-view inspection is effectively solved in combination with a three-dimensional space mapping method.

The invention provides a pseudo-infrared generation and cross-modal fusion defogging method based on single-mode visible light, and belongs to the field of computer vision, and the method comprises the following steps: (1) inputting a single-mode visible light foggy image, firstly carrying out the normalization of the input image, carrying out the dynamic cutting of the normalized image, and enhancing the adaptability of a model to the local fog density; (2) taking the visible light foggy image as input, and generating a pseudo-infrared image by using a cross-modal generation network from visible light to pseudo-infrared; (3) taking the visible light foggy image and the generated pseudo-infrared image as input, and performing feature extraction and fusion in the visible light and infrared defogging fusion network; and (4) adopting an end-to-end joint training strategy. According to the method, the pseudo-infrared image is generated through single-mode visible light input, and the fog penetration characteristic of an infrared mode is effectively simulated under the condition of not depending on a real infrared sensor in combination with a cross-mode fusion mechanism.

The invention discloses a feature integration method for interactive convolution and dynamic focusing of infrared images. The feature integration method comprises the steps that infrared image pairs with different resolutions in various real scenes are obtained through an infrared camera; performing degradation preprocessing on a part of original high-resolution images to obtain low-quality high-resolution images to form a mixed low-resolution data set, and dividing the processed data set into a training set and a test set; constructing a double-layer feature extraction module for feature modeling; training a network by using the processed training set, and optimizing a loss function; and inputting a low-resolution infrared image into the trained network, and outputting a high-resolution reconstruction result. According to the method, local and global features are fused, so that the super-resolution reconstruction quality of the infrared image in complex scenes such as low contrast and fuzzy edges is remarkably improved, and meanwhile, relatively high calculation efficiency is kept.

The invention provides a method for multi-domain division and conquering of super-resolution of an infrared image of an unmanned aerial vehicle based on Mamba. The progressive optimization of the super-resolution result is realized by fusing the feature interpretation of the spatial domain and the frequency domain. The method comprises the following steps: firstly, capturing a long-range spatial dependency relationship through a vision-oriented state space module; then capturing the local feature and texture information of the image through the synergistic effect of a wavelet transform branch, a global branch and a local branch of a feature mapping module based on wavelet transform; finally, for challenges of modal difference and semantic alignment, complementary interaction and fusion of the features are achieved through a cross attention mechanism of a multi-domain attention fusion module, the characterization capacity of global and local features is enhanced, and therefore the robustness of the model is improved.

The invention discloses a visible light-infrared dual-mode image registration method based on deep learning. The method comprises the following steps: acquiring a visible light image and an infrared image in the same scene; preprocessing the image; performing feature extraction on the preprocessed visible light image and the preprocessed infrared image, and performing feature enhancement on a feature extraction result by using a double attention mechanism; performing multi-scale feature fusion on the enhanced visible light image features and the enhanced infrared image features to generate cross-modal fusion features; inputting the cross-modal fusion features into a deformation field estimation network to generate an estimation result of a deformation field; and applying the estimation result of the deformation field to the infrared image to register the corresponding visible light image. According to the method, the problem of cross-modal registration between the visible light image and the infrared thermal imaging of the live pig body temperature non-contact prediction scene is effectively solved, high-precision pixel-level alignment is realized, and a technical basis is provided for non-contact pig body temperature monitoring.

The invention discloses an on-bridge pedestrian abnormal behavior identification and early warning method based on intelligent monitoring, and relates to the technical field of bridge engineering, and the method comprises the steps: carrying out the registration of a current visible light image and a current infrared image, and obtaining a registration dual-mode image based on a current image coordinate system; considering current bridge deformation to establish a current coordinate mapping conversion model for mapping pixel coordinate points in the current image coordinate system to physical coordinate points of a current bridge physical space coordinate system; obtaining the current three-dimensional actual coordinates of the pedestrian; recognizing abnormal behaviors of the pedestrians on the bridge based on the current three-dimensional actual coordinates of the pedestrians and acquiring current dynamic risk score values of the pedestrians; and determining the current risk level of the pedestrian according to the current dynamic risk score value of the pedestrian and a preset risk threshold value to realize multi-level linkage protection response. The method provided by the invention solves the technical problem in the prior art that the current actual coordinate of the pedestrian is difficult to accurately obtain due to environmental interference, and the abnormal recognition of the pedestrian on the bridge is inaccurate.

The invention relates to the technical field of image detection, in particular to a visible light and infrared image combined photovoltaic defect detection method based on an unmanned aerial vehicle, which comprises the following steps of: determining a photovoltaic power station detection area, carrying out synchronous aerial photography by using a visible light camera carried by the unmanned aerial vehicle and an infrared thermal imager, and carrying out local division to extract texture and temperature characteristics; the screening area performs fitting affine parameter generation on an extraction center point, corrects an infrared image to extract contour lines and texture change features, and screens a defect area to extract a positioning coordinate set; according to the method, through synchronously screening generated visible light and infrared image pairs, the resolution of an abnormal region is enhanced, a complex background and an abnormal target are accurately separated, fine-grained adaptive registration is realized through central point extraction and affine parameter derivation, and defect region characteristics are verified bidirectionally through a temperature contour closing proportion and a texture density variable quantity; thermal features and texture features are fused in the defect screening process, the recognition capability of weak anomalies is enhanced, and the defect positioning accuracy is improved.