3242 results about "Infrared image" patented technology

The invention discloses a concrete crack depth detection method and system based on multi-modal data fusion. The method comprises the following steps: synchronously obtaining a visible light image sequence and a thermal imaging image sequence of a concrete crack; the thermal imaging image sequence is obtained based on adjustable thermal excitation; and through a preset multi-modal data registration algorithm, according to the visible light image, predicting a registration displacement vector field to generate a pseudo-infrared image corresponding to the enhanced visible light image, and migrating a temperature field of the thermal imaging image at the same moment and under the same picture to the pseudo-infrared image to generate a fusion modal image, obtaining a fusion modal image sequence; and through a preset heat conduction inversion model and a temperature attenuation characteristic curve generated based on the fusion modal image sequence, obtaining crack depth data and generating a three-dimensional crack map so as to visually present concrete crack depth detection data. According to the invention, the universality and accuracy of concrete crack depth detection can be improved.

The invention provides a defect identification method and device for substation equipment and electronic equipment, and relates to the field of image identification. According to the method, an infrared image, an electric field leakage map and a visible light image are obtained through a multi-channel imaging system deployed in a substation site, and a multi-channel image tensor is generated and input into a multi-channel recognition model to extract fusion features. And fusing the features, inputting the fused features into a YOLOv8 backbone network, constructing a joint attention domain in combination with an equipment prior structure, generating a high-confidence candidate box, and performing non-maximum suppression to obtain a detection result. And constructing an inter-frame residual tensor for a detection result to perform time sequence modeling, thereby improving the detection effect. And for equipment with complex shielding, complementing a structure contour through an edge prediction path, and finally outputting target boundary and defect positioning information. By implementing the technical scheme provided by the invention, defect identification of the substation equipment is facilitated.

The invention discloses an AI-based prefabricated member multi-modal visual quality intelligent detection system and method, and the system comprises a multi-modal visual data collection unit which is used for obtaining the multi-modal visual data of a prefabricated member detection region, calibrating the multi-modal visual data, and constructing a multi-modal data set. And the feature fusion and candidate region extraction unit is used for inputting the multi-modal data set into a multi-modal feature fusion network to extract multi-modal features, fusing the multi-modal features and generating a defect candidate region. And the defect type identification and quantitative analysis unit is used for calling a deep learning detection and segmentation model to carry out defect type classification and defect boundary segmentation on the defect candidate region, and calculating a quantitative index of the defect region in the segmentation boundary by using the three-dimensional point cloud and the infrared image. And the defect grade judgment and component quality evaluation unit is used for carrying out dynamic threshold judgment on the defect area according to the defect type and the quantitative index of the defect area, and outputting the defect severity grade and the quality evaluation result of the prefabricated component.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.

The invention relates to the technical field of infrared target detection, and discloses a YOL0-based adaptive full-scale infrared target detection network, which comprises a trunk feature extraction network, a neck feature fusion network, a detection head network and a training optimization module, and is characterized in that all the modules are sequentially connected in series to form a complete detection link; the infrared image multi-dimensional feature extraction system is used for infrared image multi-dimensional feature extraction and comprises a convolution layer, an SPPF module and a C2MFE module which are connected in sequence, and the C2MFE module replaces a standard convolution layer in a traditional C2f module through multi-kernel feature extraction convolution (MFEEConv) to achieve multi-direction and full-scale feature capture; and the neck feature fusion network is connected with the output end of the trunk feature extraction network, comprises a multi-scale feature fusion module (MFFM) and a feature pyramid structure, and is used for enhancing the feature correlation of different levels. The adaptive full-scale infrared target detection network based on YOL0 can efficiently adapt to complex scenes such as low illumination and severe weather, and realizes cross-scene full-scale infrared target accurate detection.

The invention relates to the technical field of bullet quality detection, in particular to a cartridge case surface defect lightweight detection method and system. The method comprises the following steps: acquiring a visible light image, a near-infrared image and a polarization image corresponding to a cartridge case to be detected, and generating an enhanced image by adopting a dynamic weight fusion algorithm; performing dynamic feature extraction on the enhanced image by using a feature extraction network; and segmenting the extracted feature map by adopting multi-scale segmentation to obtain a defect region, identifying a defect type corresponding to the defect region by utilizing a capsule network-based lightweight hybrid classifier, and outputting an identification result. According to the scheme, the defect detection precision and efficiency are improved, and the problem of poor environmental adaptability is better solved.

The invention provides a visible light and infrared fusion-based river channel fish banning ship monitoring method and system, and relates to the technical field of computer vision, and the method comprises the steps: firstly obtaining a visible light image sequence and an infrared image sequence of a river channel monitoring region; ship visual feature extraction and thermal feature extraction are carried out on the visible light image sequence and the infrared image sequence respectively to obtain a visible light ship feature set and an infrared ship feature set, and cross-source feature fusion processing is carried out on the two feature sets to generate a cross-source fusion feature set; and calling a pre-trained fish forbidding ship identification model to analyze the cross-source fusion feature set, generating a ship monitoring result including the ship type, the position change track and the suspected fish forbidding behavior identifier, and finally generating fish forbidding early warning information including the real-time position and behavior feature description of the suspected fish forbidding ship based on the ship monitoring result. And measures can be quickly taken to stop the fishing forbidding behavior.

The invention discloses a cross-modal target detection method based on learnable Fourier transform, and mainly solves the problem of insufficient fusion of a visible light image and an infrared image in a complex scene due to inter-domain difference in the prior art. According to the implementation scheme, the method comprises the steps that bimodal features are extracted through a double-flow CSPDarknet53 network; a target position guiding module is utilized to enhance target area representation and suppress background interference; the features are converted to a frequency domain, and amplitude texture information of the visible light image and phase contour information of the infrared image are adaptively enhanced through a learnable frequency domain feature enhancement module; suppressing noise through global filtering and then inversely transforming back to a spatial domain; and finally, outputting a target detection result of the multi-modal image by the detection head. According to the method, frequency domain physical characteristics are fully utilized, full complementation and adaptive fusion of cross-modal features are realized, the detection precision and robustness of vehicles, pedestrians and other targets under low-illumination and complex backgrounds are remarkably improved, meanwhile, high calculation efficiency is kept, and the method can be applied to the fields of automatic driving, intelligent monitoring and the like.

The invention relates to the technical field of gas leakage detection, and discloses an infrared imaging radiation correction method and system for gas leakage detection, and the method comprises the steps: obtaining the distribution data of dark current noise, and the mapping relation parameters of an image gray value and a radiation brightness value; acquiring infrared radiation of the gas leakage scene by using an infrared detector to obtain an original infrared image; based on the distribution data of the dark current noise, performing pixel-by-pixel compensation on the original infrared image to generate an infrared image after dark current correction; extracting the radiation intensity distribution of the infrared image after dark current correction, calibrating the radiation value of each pixel in the infrared image based on the mapping relation parameter between the image gray value and the absolute radiation brightness value, converting the image gray value into the absolute radiation brightness value, and generating the infrared image after radiation calibration; and outputting an infrared image after radiometric calibration for subsequent gas leakage area identification and feature analysis. According to the invention, a high-quality and quantifiable radiation quantification image can be output.

The invention discloses a small target recognition method and system for a multi-modal fusion image in a complex environment, and belongs to the technical field of computer vision and image recognition, and the method comprises the steps: obtaining a visible light image, an infrared image and environment sensor data; image registration is carried out on visible light and infrared images, and a multi-scale image feature pyramid is constructed. And respectively extracting visible light and infrared image features to obtain visible light and infrared imaging feature data. And performing multi-modal data fusion on the visible light and infrared imaging feature data based on a cross-modal attention mechanism, and adaptively adjusting a fusion weight based on environmental sensor data to generate fusion features. And performing space-time enhancement processing on the fusion feature to obtain an enhanced fusion feature. And performing target tracking detection on the small target, and outputting position and category information of the small target. According to the method, the small target recognition capability in a severe environment is remarkably improved, and high precision and robustness can still be kept in a foggy, low-visibility and dark scene.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention discloses an extreme sea condition parameter identification system based on deep learning, and relates to the technical field of ship navigation auxiliary equipment, in particular to a self-adaptive sea condition identification device which is used for acquiring image data and inertial measurement data of a current sea condition; the wave field visual depth estimation module is used for extracting visible light image features and infrared image features of a wave area from image data of the current sea condition, fusing the extracted visible light image features and infrared image features, using an encoder-decoder architecture and fusing an energy function to obtain a pixel-level wave height field, and outputting the pixel-level wave height field. The three-dimensional reconstruction of the wave surface is realized; the multi-modal data fusion module uses a filter dynamic model and a cost function to eliminate space-time asynchronous errors between inertial measurement data and visual perception data, performs multi-modal data fusion, and outputs wave field real-time parameterization information. According to the invention, the sea condition parameter real-time high-precision identification capability of the autonomous unmanned ship or the offshore carrying platform can be improved.

The invention discloses a single-person abnormal behavior identification method and system based on multi-modal skeleton feature fusion, and the method comprises the steps: S1, collecting continuous RGB images and infrared thermal imaging images in a monitoring video, carrying out the human body detection and key point estimation of visible light and infrared images through employing a multi-modal fusion model of YOLOv12 in combination with Transform, and constructing a single-person posture time series data set; s2, key point speed vectors are calculated for the continuous skeleton frame sequence of each target person, skeleton key point information and speed information are fused, and an action feature sequence is formed; s3, inputting the motion feature sequence into an MPED-RNN model, decomposing skeleton motion into a global displacement component and a local attitude deformation component, and performing joint coding, decoding and prediction through a dual-channel GRU network; and S4, calculating a prediction error and a reconstruction error according to a reconstruction result and a future skeleton key point prediction result, evaluating whether the current behavior deviates from a normal trajectory, and judging whether the current behavior is in an abnormal state. According to the invention, real-time identification of abnormal behaviors of a single person in a complex scene is realized.

The invention discloses a poultry behavior abnormity real-time monitoring system based on multi-modal image fusion, particularly relates to the technical field of intelligent breeding behavior recognition, and is used for solving the problem of poor behavior monitoring accuracy under feather shielding. The method comprises the following steps: firstly, through combined perception of a visible light image and an infrared image, extracting a claw track interruption point and an anus temperature gradient direction, and realizing analysis of a motion state of a sheltered area; then, in combination with the heat conduction delay characteristic and the group movement direction, the flexion and extension angle of the covered leg joint is inverted, and a complete gait sequence is generated; thirdly, multi-source features such as gaits, temperature differences and body postures are fused, and a dynamic deviation model of the individuals relative to the mass center of the group is constructed; and finally, generating a stress behavior threshold curve according to the ground temperature and the ammonia gas concentration, outputting an abnormal behavior type and confidence, and realizing intelligent distinguishing of mechanical obstacles and adaptive behaviors.

The invention provides a small target detection method and system based on aligned visible light and infrared images, 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 small target; inputting a visible light image and an infrared image into the trained detection model, firstly, respectively performing multi-scale feature extraction on the visible light image and the infrared image by adopting a double-branch structure, and in the extraction process, performing multi-scale feature extraction on the visible light image and the infrared image through interactive collaborative learning of the visible light image and the infrared image; the method comprises the following steps: firstly, carrying out feature alignment, modal interaction correction and multi-scale feature enhancement between two modals to obtain enhanced visible light features and infrared features, then carrying out feature fusion, and finally, carrying out small target positioning and classification by utilizing the fused features. According to the method, feature level alignment of image pairs is realized by using deformable convolution and modal interaction correction, so that the features of visible light and infrared images are effectively and fully interacted and fused, and the accuracy of a detection algorithm is improved.

The invention provides an infrared and visible light image enhancement fusion method and system for a low-illumination scene, and the method comprises the steps: obtaining an infrared image and a visible light image, and carrying out the preprocessing of the infrared image and the visible light image; inputting the brightness image and the illumination prior image into an encoder, and performing single-mode reconstruction pre-training through a minimization reconstruction loss function; performing deep feature extraction on the dual-channel visible light image and the normalized infrared image by using a pre-trained encoder, performing bidirectional feature guidance and fusion in a fusion module in a manner of exchanging query vectors by using a cross-modal cross attention mechanism, and reconstructing the fused features by using a pre-trained decoder; according to the method, low-illumination image enhancement and cross-modal feature fusion are integrated into a unified framework, brightness improvement, detail reservation and modal consistency synchronous optimization are realized through end-to-end joint modeling, and information loss caused by enhancement and fusion process separation is avoided.

The invention discloses a human body infrared image small target detection method based on an improved FGLCM feature, and relates to the technical field of image processing and target detection, and the method comprises the following steps: building a boundary singularity auditing baseline under a unified time baseline, carrying out the multi-scale energy mapping of a curved surface fitting residual error of a human body infrared image, and generating a traction residual error distribution diagram; and constructing a reflection pseudo peak discriminator based on the traction residual distribution diagram, and extracting a pseudo peak kernel position by combining polarization sensitivity estimation and view angle transformation consistency constraint. According to the method, a closed-loop self-adaptive regulation and control mechanism is constructed through residual distribution auditing, pseudo peak identification, gradient registration, differential entropy enhancement and time sequence threshold adjustment, fitting abnormity and false highlight spots in the infrared image are inhibited, the accuracy and stability of small target detection are improved, and the method is suitable for a complex photo-thermal environment.

The invention discloses a sleeve fault diagnosis method and system. The method comprises the following steps: preprocessing an acquired sleeve infrared image to filter out a non-heating part; segmenting the image of which the non-heating part is filtered out to obtain a single sleeve image; diagnosing the sleeve fault according to the segmented single sleeve image. According to the invention, theinfrared image of the transformer sleeve is preprocessed and segmented, therefore, an infrared image of a single sleeve is obtained; the temperature of each part of the sleeve can be conveniently obtained by converting the gray scale of each pixel in the gray scale map of the infrared image of the single sleeve into the corresponding temperature value, the method is simple but very ingenious, thefaulted sleeve and the faulted area can be quickly positioned, the specific fault type can be judged, and convenience and quickness are achieved.

The invention discloses a wild animal target detection method based on improved YOLO11. According to the invention, double-dynamic convolution is designed to improve the feature extraction and nonlinear expression capability of the feature extraction module on the wild animal image so as to flexibly adapt to the feature difference between visible light and infrared images; a multi-scale expansion attention mechanism is introduced to improve the multi-scale target detection precision; unified-IoU loss is introduced to solve the problem of unbalanced quality of a prediction frame; and a DySample dynamic up-sampling operator is introduced, so that the detail recovery capability is improved, and the feature distortion is reduced. The problems that a traditional YOLO feature extraction module cannot effectively process an image shot by a field infrared camera, and an infrared image depends on thermal radiation, is low in contrast, weak in texture, large in environmental thermal noise and large in feature difference with a visible light image and is difficult to adapt are solved, and traditional CIoU loss easily pays attention to and fits a low-quality prediction frame. And a sampling module cannot effectively reconstruct low-resolution infrared image features.

The invention discloses a wildlife detection method fusing an unmanned aerial vehicle thermal infrared image and a visible light image, and belongs to the field of small target wildlife identification, and the method comprises the following steps: S1, obtaining a preprocessed TIR-RGB image pair set; s2, an FDM-YOLO double-source target detection model improved based on YOLOv81 is constructed, and the improved FDM-YOLO double-source target detection model is trained based on the preprocessed TIR-RGB image pair set obtained in the step S1; and S3, inputting an image acquired in real time into the improved FDM-YOLO double-source target detection model trained in the step S2, and outputting a wild animal detection result. By adopting the wild animal detection method fusing the thermal infrared image and the visible light image of the unmanned aerial vehicle, high-precision, real-time and robust detection of a small target of a wild animal in a complex field environment is realized by improving the FDM-YOLO model.

The invention discloses a visible light, infrared and IQ signal fusion individual identification method based on cross-modal cross attention, and belongs to the technical field of artificial intelligence and multi-modal image processing. Aiming at the problems of insufficient multi-modal heterogeneous feature fusion capability, unbalanced modal semantic expression and unstable classification precision in the prior art, a visible light image, an infrared image and an original IQ signal are acquired, and after preprocessing, a convolutional neural network is combined with a space attention module to extract image features; using a convolutional hybrid network to extract signal spectrum features; three groups of modal pairs are constructed by adopting a cross-modal bidirectional cross attention mechanism to carry out bidirectional semantic interaction and feature fusion; and finally, inputting the fusion features into a classifier to obtain an identification result. According to the method, deep semantic fusion can be realized, modal quality changes can be dynamically adapted, and the precision and robustness of target recognition in a complex environment are improved.

The invention discloses a lightweight multi-source unmanned aerial vehicle target detection method and system based on a DEYOLO framework, and relates to the field of target detection, and the method comprises the steps: obtaining an unmanned aerial vehicle visible light image and an unmanned aerial vehicle infrared image which are registered, and inputting the images into a pre-trained target detection model; the model comprises a double-flow feature extraction network module which is used for extracting an unmanned aerial vehicle visible light image and an unmanned aerial vehicle infrared image to obtain a visible light feature map and an infrared feature map; the bimodal adaptive feature weighting module is used for performing bimodal adaptive feature weighting and adding on the visible light feature pattern and the infrared feature pattern to obtain fusion features; the lightweight bimodal attention enhancement module is used for performing feature enhancement on the fusion features; and the detection head is used for detecting the enhanced features. According to the method, the calculation complexity is effectively reduced, and the detection precision and the reasoning speed of the model on the low-slow small target and the robustness of the model on a complex scene are remarkably improved.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a near-infrared assisted low-light scene three-dimensional reconstruction method based on 3D Gaussian splashing, which is characterized in that generation of a Gaussian ellipsoid is dominated by a near-infrared image with a high signal-to-noise ratio, and a stable geometric basis is provided for a visible light information missing area; in the rendering stage, a near-infrared rendering image and a normal visible light rendering image are respectively generated through Gaussian ellipsoid shared geometric parameters and respective opacity and color attributes of two modes; through cross-modal structure similarity loss, a normal visible light image which is forcibly rendered is aligned with a near-infrared light image in structure, and clear near-infrared structure information is used to strictly constrain the recovery process of the color of the visible light image, so that the accuracy and authenticity of a recovery result are ensured.

The invention relates to the technical field of image processing, and discloses an infrared image enhancement method and system based on local phase correlation, and the method comprises the steps: obtaining a plurality of continuous frames of infrared images, carrying out the intelligent partitioning of a reference frame, and calculating the variance feature, the method comprises the following steps: selecting regions of interest with rich information, independently executing phase correlation operation in each region to extract a local translation vector, obtaining global displacement estimation through weighted fusion, adopting an abnormal value detection algorithm to improve robustness, and finally realizing sub-pixel-level image alignment and intelligent weighted fusion. The method is suitable for real-time enhancement processing of satellite-borne infrared remote sensing images, the resource constraint requirement of an embedded platform is met while the processing quality is guaranteed, and an efficient and reliable technical scheme is provided for space remote sensing image processing.

The invention relates to the technical field of multi-sensor identification, in particular to a coal gangue detection and classification method and system based on multi-sensor fusion discrimination, and the method comprises the following steps: obtaining visible light and infrared images, calculating brightness and intensity judgment feature conditions, executing edge detection to extract gray segments, and fusing textures and a thermal field to generate a vector set. And performing clustering analysis to finish classification judgment, and outputting a coal gangue detection classification result. According to the method, a precise trigger mechanism is established through brightness and thermal radiation double-feature screening, boundary recognition sensitivity is enhanced through gray abrupt change point division, salient region extraction capacity is enhanced through weighted fusion of texture energy and gray gradient, and a cross-modal consistency feature group is constructed through combination of two-dimensional vector construction and similarity screening. The recognition expression integrity is improved, static threshold classification is replaced by vector distribution clustering, accurate mapping and classification decision making of material attributes in a complex scene are achieved, and the stability and the recognition rate of a coal gangue detection result are guaranteed.

The invention relates to the technical field of intelligent detection, and discloses a high-temperature alloy casting size on-line detection system based on machine vision, and the system comprises an imaging fusion module which collects image data of a high-temperature alloy casting, carries out the pixel-level registration of infrared image data and visible light image data, and generates a multispectral fusion image. The feature extraction module performs temperature distribution analysis and brightness feature extraction based on the multispectral fusion image, generates an edge confidence map and extracts a feature point set. And the calculation module performs weighted fitting on the feature points according to the feature point set to obtain thermal state size parameters. And the thermal compensation module performs thermal compensation and geometric correction on the thermal-state size parameters to obtain cold-state size parameters. And the judgment module carries out comparison to judge whether the casting is a qualified casting. According to the invention, stable imaging and accurate registration in high-temperature radiation and strong reflection environments are realized, and the detection efficiency and the size control level of the high-temperature alloy casting are improved.

The invention discloses an infrared image power transmission equipment target identification method and system based on YOLOv7, and relates to the technical field of electric power automation, and the method comprises the following steps: obtaining continuous frames of infrared images and visible light images of power transmission equipment, and carrying out the preprocessing; based on the processed continuous frame infrared image and visible light image, constructing a space-time multi-mode input tensor; processing the input tensor through an improved YOLOv7 model, and outputting a first target frame and a corresponding frame-level feature vector; performing cross-modal feature fusion optimization based on the frame-level feature vector to obtain a joint optimization feature; performing confidence coefficient correction on the first target frame by using the joint optimization feature to obtain a second target frame; and outputting a target recognition result based on the second target frame. The method is used for solving the defects of a traditional power transmission equipment identification method in the aspects of information fusion effect, detection result stability and result credibility.

The invention provides an infrared weak and small target detection method based on an adaptive sparse attention mechanism, and the method comprises the steps: collecting an infrared weak and small target image, carrying out the pre-screening through a bright and dark target balance module, and generating an infrared weak and small target data set; an infrared weak and small target detection model is constructed, the infrared weak and small target detection model is trained by using the infrared weak and small target data set, a trained infrared weak and small target detection model is obtained, and the model comprises an infrared image enhancement module, a linear embedding layer, an adaptive sparse attention Transform network and a low-rank approximate global feature fusion module which are connected in sequence; and inputting an infrared weak and small target image to be detected into the trained infrared weak and small target detection model to obtain an infrared weak and small target detection result. According to the method, the problem of insufficient detection precision of an existing method is solved, the robustness, the applicability and the real-time performance are high, and the visual perception capability of an infrared sensor platform to the surrounding environment is enhanced.

The invention discloses a ship anti-collision early warning method based on multi-source heterogeneous information fusion, and the method comprises the steps: S1, obtaining the multi-source target information of a ship navigation radar, an AIS, and an infrared camera, and unifying the targets of all sensors to a same coordinate system; s2, preprocessing the radar and the AIS target; s3, performing information fusion on the preprocessed radar and AIS target; s4, performing information fusion on the target after radar and AIS fusion and the infrared image recognition target; and S5, based on the dynamic information of the final fusion target and the state of the ship, calculating the relative distance and orientation with the ship, and when the target is located in a preset fan-shaped area right in front of the ship and the relative distance is smaller than a dynamic danger threshold value, triggering a multi-stage acousto-optic character alarm. According to the invention, various sensor information can be integrated to detect and track the water surface target, and the robustness and the detection rate are improved, so that a more accurate early warning effect can be achieved on the water surface obstacle when the ship sails.

The invention relates to an intelligent inspection data processing method and system. The intelligent inspection data processing method comprises the following steps: analyzing an infrared image in an inspection scene to obtain an image resolution, a temperature matrix and a pseudo-color image of the infrared image; synchronizing annotation information of the infrared image and the visible light image in the inspection scene to obtain multi-modal annotation data; based on preset reference object information, performing defect quantitative analysis on the multi-modal labeling data, and generating a real physical size quantitative result of the defect; and carrying out associative storage on the image resolution, the temperature matrix, the pseudo-color image and the quantification result. According to the invention, the whole-process optimization of the inspection data can be realized, and the processing stability, the defect detection accuracy and the system practicability are effectively improved.