666 results about "Disease detection" patented technology

The invention discloses an unmanned aerial vehicle laser and vision fusion inspection method and system for bridge bottom disease detection, and the method comprises the steps: carrying out the synchronous data collection through employing a calibrated laser radar, a camera and an IMU, and obtaining a three-dimensional laser point cloud and a two-dimensional visual image of the appearance of a bridge; sharpening the image containing the motion blur and completing brightness self-adaption of the image; stable feature points are extracted, multi-frame matching is carried out, the corresponding poses of the images are estimated, and bridge dense point cloud reconstruction is completed; performing geometric component segmentation on the point cloud to generate a geometric prior region; component segmentation is carried out on a support area in the image, and a continuous and accurate component segmentation result is obtained in combination with a geometric prior area; screening the image, calling a targeted disease detection model in a corresponding component area, and generating a segmentation mask for the disease; obtaining a real disease three-dimensional point cloud, and carrying out quantitative calculation on the physical size of the disease; and displaying the real disease three-dimensional point cloud data and the physical size of the disease. The method is high in efficiency and precision.

The invention discloses a nerve disease detection and analysis method and system, and the method comprises the steps: obtaining a bracelet collection signal, a sphygmomanometer collection signal, a movement behavior image and behavior test data, and extracting tremor intensity features, gait symmetry features and autonomic nerve rhythm features through multi-band decomposition of the bracelet collection signal; analyzing the motion behavior image and the standardized motion test to obtain a motion function score; carrying out heart rate variability analysis to identify a neural function abnormality mode; constructing a neural function state map and calculating a feature weight; predicting a disease progress trend in combination with historical monitoring data; and dynamically adjusting a prediction result through subsequent feedback correction information. Through a mode of combining short-time intensive monitoring and long-term intermittent acquisition, long-term trend prediction and dynamic correction based on initial data are realized, and the reliability and practicability of nerve disease risk assessment in a home scene are remarkably improved.

The invention relates to a bridge and tunnel disease detection method and system based on unmanned aerial vehicle-edge computing collaboration, and solves the problem that the detection efficiency is limited due to the lack of systematic design of a collaboration mechanism of an unmanned aerial vehicle and edge computing. The method comprises the following steps that: a distributed edge computing node fuses multi-source monitoring data to obtain a health index, compares the health index with a multi-level threshold value, generates a message containing space coordinates, levels and characteristics when the health index is abnormal, and transmits the message to an edge computing center; the center screens adaptive unmanned aerial vehicles, plans an optimal path, dispatches collected data, and preliminarily screens diseases through a parallel model; determining disease complexity and types in combination with abnormal features, and establishing a collaborative detection unit to specially collect multi-source data; centimeter-level positioning is realized through BIM registration and SLAM, and an accurate detection report is generated. The method has the following effects: accurate scheduling, real-time processing and centimeter-level positioning of disease detection are realized, an intelligent detection closed loop is constructed, and the accuracy and efficiency of bridge and tunnel operation and maintenance are greatly improved.

The invention discloses an intelligent road disease detection method and equipment based on dynamic multi-scale convolution. The method comprises the following steps: performing target category labeling on a training sample set to form a corresponding relationship between sample data and labels; establishing a detection framework including a backbone network, a neck network and a detection network, the backbone network extracting features through convolution layer and cross-stage local layer stacking, introducing spatial pyramid pooling to enhance multi-scale expression, and enhancing fine-grained disease area attention by enhancing spatial attention; the neck network is integrated with dynamic multi-scale convolution and is matched with an upper sampling layer and a cross-stage local layer to fuse high and low layer features; the detection network is provided with a plurality of parallel detection heads, and disease detection is carried out corresponding to different scale feature maps. According to the method, rapid detection and accurate positioning of disease targets of various sizes can be realized, and the detection capability of sparse and small-scale disease targets is remarkably improved through collaborative optimization of a dynamic multi-scale convolution kernel and an enhanced space attention mechanism.

The invention provides a road underground disease detection model based on medium inversion and deep learning. A generation method of the model comprises the following steps: 1) collecting road underground radar data by using a ground penetrating radar; 2) obtaining the distribution condition of the underground dielectric constant corresponding to the B-Scan, and constructing a B-Scan-dielectric constant distribution data set; 3) constructing a deep learning medium dielectric property inversion model, and performing training by optimizing a comprehensive loss function; 4) utilizing the trained dielectric dielectric property inversion model to perform inversion on the actually measured radar map to generate an underground dielectric property distribution map; 5) performing disease classification marking according to the underground dielectric property distribution diagram, and constructing and training a disease target identification model; and 6) inputting the to-be-detected dielectric property distribution data into the disease target identification model, and outputting a prediction result of the underground hidden disease. Compared with the prior art, the method provided by the invention can realize accurate disease identification and positioning under complex underground medium distribution, and has better accuracy advantage and stronger generalization ability.

The invention discloses a bridge structure low-altitude inspection and disease assessment system and method based on deep learning, and belongs to the technical field of bridge disease detection and structure health monitoring. The system comprises a multispectral adaptive image acquisition module, a multi-scale disease detection and feature extraction module, a digital twin mapping and disease positioning module and a disease evolution prediction and maintenance decision module. The system dynamically adjusts acquisition parameters according to environmental conditions, accurately identifies diseases of different scales based on a multi-scale convolutional neural network, realizes centimeter-level accurate positioning of the diseases through a three-dimensional digital twinborn model, analyzes a disease time sequence evolution trend and generates graded maintenance suggestions, and realizes adaptive optimization through a closed-loop feedback mechanism. The method is high in environmental adaptability, high in detection precision and accurate in positioning, has disease evolution analysis capability, and provides comprehensive technical support for bridge health monitoring and intelligent management.

The invention discloses an underground engineering lining disease detection system based on point cloud, and belongs to the technical field of underground engineering detection. In order to solve the technical problems that an existing underground engineering lining disease detection method is low in detection precision, low in automation degree and the like, underground engineering point cloud data to be detected and corresponding position information are collected, and an improved PointNet + + model is adopted for disease recognition. According to the method, original three-dimensional coordinates of a point cloud are expanded into seven-dimensional point cloud data containing coordinates, normal vectors and reflection intensity, the normal vector standard deviation of points in a neighborhood of each candidate point is calculated to serve as local geometric complexity, a local geometric complexity index is fused into sampling distance measurement, then a multi-scale local neighborhood is constructed by combining sphere query, and therefore the multi-scale local neighborhood is obtained. And extracting geometric features, texture features and deformation features by using a PCA feature dimension reduction technology, carrying out feature fusion based on a normal vector weighting mechanism, finally obtaining disease type classification based on a network model, and calculating the size and position of the disease.

The invention provides an expressway disease detection method capable of accurately positioning. The expressway disease detection method comprises the following steps: S1, image acquisition and data annotation; s2, constructing an instance segmentation model; s3, extracting a multi-scale feature map and fusing multi-scale features; s4, outputting a category label, a bounding box coordinate and a confidence coefficient score of the detection box; s5, performing multi-target trajectory tracking; s6, eliminating global motion influence; s7, establishing an association relationship between the prediction trajectory and the current detection frame; s8, dynamically maintaining a track list; and S9, outputting bounding box coordinates and ID tags in each target continuous frame, and storing the bounding box coordinates and the ID tags as structured data. According to the method, a high-performance target detection framework, an advanced instance segmentation tracking technology and a GPS positioning technology are combined, pixel-level identification and continuous tracking of diseases can be realized while high detection precision is ensured, and the positions of the diseases can be accurately positioned.

The invention discloses an agricultural pest detection method and system based on improved YOLOv11n, and relates to the technical field of pest detection. Acquiring an agricultural pest image data set, and marking the agricultural pest image data set; an AMC-YOLO network model is constructed; training an AMC-YOLO model by using the labeled agricultural disease and insect pest image data set; and inputting a to-be-detected agricultural pest image into the trained AMC-YOLO model, and outputting a pest detection result. The AMC-YOLO network model adopts an APBN asymmetric filling bottleneck structure, through four-direction asymmetric filling and packet convolution, the inhibition of traditional symmetric filling on target direction features is broken through, the receptive field is significantly expanded, and the spatial feature capturing capability on the disease and pest morphology is enhanced; according to the MSCAM multi-scale convolution attention mechanism, through multi-scale convolution kernel parallel sampling, global semantics and local details are fused, and the limitation of single-scale feature expression is solved; the content of the CGSA guides simple attention to integrate three attention of space, channel and pixel levels, and the feature expression of a detection target is enhanced.

The invention discloses a road disease detection method based on an unmanned aerial vehicle, electronic equipment and a program product. The method is realized based on a trained road disease detection model, a C3k2-MDDSC module is introduced into a backbone network of the model, the feature multiplexing capability is enhanced through gradient shunting and multi-scale fusion, the gradient disappearance problem is relieved, and the robustness of the model is improved by means of jump connection and packet convolution. An ACFP module is introduced into the tail end of the backbone network, dynamic fusion of local and global features is realized by using multi-scale cavity convolution and a channel-space attention mechanism, and the complex scene modeling capability is improved. And the neck network is integrated with an SGF module, so that the spatial perception of the model to a tiny target can be improved. Besides, the ES-FPN proposed based on the SGF module not only can enhance the utilization of shallow spatial information, but also can optimize the complementarity of cross-level features. During training, regression loss, namely fast high-quality intersection-to-union ratio loss, is proposed, and angle punishment is introduced to improve the alignment precision of the rotating frame and the convergence speed of the model.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention provides an apple disease detection method based on a Transform framework. The apple disease detection method comprises the following steps: firstly, making an apple disease detection image data set through apple disease image acquisition, apple disease image annotation and apple disease image annotation file division; then, an efficient multi-scale attention mechanism (EMA) is used and integrated with a backbone network, and a new feature extraction module (Basic Block EMA), a new adaptive feature extraction module (LAE) and a new re-parameterized CSP efficient layer aggregation module (RepNCSPELAN4) are designed to optimize an RT-DETR target detection model; inputting the obtained apple disease data set image into the optimized RT-DETR model for training verification, and obtaining an optimal model weight; and finally, importing the optimal weight model into an apple disease detection system, pre-processing the collected apple disease image, inputting the pre-processed apple disease image into the model for reasoning, and outputting a detection result containing disease category and position information, thereby realizing automatic and accurate detection of apple diseases. The invention aims to reduce the huge parameter quantity and model volume of an original network model by improving the RT-DETR model, and improve the detection efficiency and identification precision of the apple disease target.

The invention relates to the technical field of disease and insect pest image analysis, in particular to an AI image recognition and grading method for field crop leaf disease and insect pests, which comprises the following steps: under the irradiation of a field fixed light source, synchronously acquiring a plurality of polarized reflection images around crop leaves at preset angle intervals; extracting a pixel polarization degree matrix of a leaf area in each polarization reflection image; inputting the pixel polarization degree matrix into a polarization transmission model, outputting a cuticle anomaly coefficient graph, and marking an area exceeding a preset anomaly threshold in the cuticle anomaly coefficient graph as a highlight display area; matching an infection type template library according to a highlight display area distribution mode in the abnormal coefficient graph; and calculating an infection intensity value by combining the diffusion gradient of the highlight area, and outputting a pest grade. According to the method, the boundary of the optical mutation region of the focus region is depicted, so that the physical interpretation of disease detection is improved, and distinguishable feature spaces are provided for different infection mechanisms (such as fungal growth layers and insect pest piercing and sucking points).

The invention discloses a crop disease and pest identification method and system based on multi-task learning. The method comprises the following steps: firstly, constructing a crop disease and pest identification model comprising a multi-scale feature information fusion module, a disease detection branch and a severity classification branch; the method comprises the following steps: preprocessing an input leaf image, and extracting and fusing a multi-scale feature map by a multi-scale feature information fusion module; the disease detection branch generates disease and insect pest candidate regions by using a region proposal network, and outputs disease and insect pest positions and categories through disease detection in combination with a multi-scale fusion feature map; meanwhile, the fusion feature map with the maximum size is input into a severity classification branch to realize four-stage evaluation; a weighted loss function design thought is provided, and multi-task network branches can be guided to carry out joint training. According to the method, end-to-end multi-task cooperative processing is realized, disease and pest positioning, classification and severity evaluation are synchronously completed by sharing a multi-scale fusion feature map, the recognition efficiency is greatly improved, and the real-time monitoring requirement of an agricultural scene is met.

The invention discloses a multi-modal dynamic compensation road disease intelligent detection and risk assessment system, and relates to the technical field of artificial intelligence and computer vision, and the system comprises an image collection module which is used for obtaining a road surface image in real time through a camera device, and transmitting the image to a preprocessing module; the preprocessing module is electrically connected with the image acquisition module and is used for carrying out graying, noise reduction, contrast enhancement and geometric correction operation on the image and outputting a standardized image; the feature extraction module is electrically connected with the preprocessing module. According to the road disease detection system provided by the invention, by integrating a plurality of modules, high efficiency and intelligence of road disease detection are realized, compared with traditional manual inspection, the system not only improves the detection efficiency, but also remarkably enhances the objectivity and accuracy of detection, and is particularly suitable for real-time monitoring requirements of a large-scale road network; the image acquisition quality is effectively improved, and the effectiveness of feature extraction can be ensured.

The invention relates to a ground penetrating radar urban road hidden disease image detection method based on YOLO11n-GPR, and the method comprises the steps: obtaining an urban road underground crack and cavity image data set collected by a ground penetrating radar, and carrying out the preprocessing; a target detection model YOLO11n-a based on the improved YOLO11n is constructed, and YOLO11n-b is further obtained according to a structured pruning strategy; the YOLO11n-a serves as a teacher model, the YOLO11n-b serves as a student model, knowledge distillation training is conducted through the training set, and a hidden disease detection model YOLO11n-GPR is obtained; and obtaining a to-be-detected urban road underground ground penetrating radar scanning image, inputting the image into the hidden disease detection model YOLO11n-GPR, and outputting an urban road hidden disease detection result. Compared with the prior art, the method has the advantages of solving the problems of complexity, accuracy and timeliness of existing detection and the like.

The invention relates to a pavement disease detection method, system and device based on a hybrid architecture, and a storage medium. The method comprises the following steps: obtaining pavement image data; the method comprises the steps that data are input into a re-parameterized feature extraction network, the network is based on an HGNetV2 architecture, a RCHGBlock module is formed by embedding a RepConv structure into HGBlock, and a plurality of modules are cascaded and stacked to construct a four-stage progressive feature pyramid structure; based on a high-level semantic layer of a feature pyramid, integrating a space edge perception enhanced attention mechanism, enhancing disease edge features through a dual-path complementary processing framework of an edge extraction path and a standard convolution path, and performing multi-scale feature fusion by using an improved C3K2-SEAM module as a feature fusion unit; and performing end-to-end disease detection based on the fusion features, and outputting disease types, positions and confidence information. Compared with the prior art, the method has the advantage that the disease detection precision and robustness in a complex scene are remarkably improved.

The invention relates to the technical field of bridge disease diagnosis and maintenance, in particular to a bridge disease diagnosis and maintenance measure recommendation method which comprises the following steps: constructing a bridge disease database which comprises feature data and cause data of various types of bridge diseases and maintenance measure data matched with the various types of diseases; receiving field disease information of a target bridge input by a user, wherein the field disease information comprises a disease type and a disease characteristic parameter; performing matching analysis on the field disease information and data in the bridge disease database, and diagnosing a disease cause of the target bridge based on a matching result; according to the diagnosed disease causes, one or more maintenance measures corresponding to the disease causes are called from the bridge disease database and output as recommended schemes, and a complete data link from disease detection to maintenance decision is established by constructing the standardized bridge disease database and a matching mechanism.

The invention discloses an intelligent risk assessment method and system for disease detection, and the method comprises the steps: delimiting a normal fluctuation interval based on the steady state of an individual through collecting the historical physiological measurement values of the individual, extracting daily activity characteristics through combining behavior data, and distributing an initial risk weight according to a preset proportion; and continuously comparing the current physiological index with an individual normal fluctuation interval by accessing a real-time data stream, and screening unexplained abnormal signals in combination with environmental parameters. The method gets rid of dependence on a general standard and single measurement, and can identify an abnormal trend deviating from a self steady state when a physiological index does not break through a general medical threshold value by utilizing individualized baseline and multi-source data fusion. By establishing a dynamic reference system and a context screening mechanism, the system can start a risk assessment process at the initial stage of an abnormal trend, so that the timeliness and pertinence of early warning are remarkably improved, and early capture of potential health risks is realized.

The invention discloses a road disease real-time identification embedded method and system based on a lightweight CNN and an attention mechanism. According to the method, a lightweight feature extraction module integrating reconfigurable convolution and channel attention is constructed, a multi-scale feature fusion network of grouped convolution and cross-stage connection is adopted, and a three-level prediction head focusing on diseases of different sizes is configured, so that the feature sensing and positioning capability on tiny diseases is remarkably improved. A composite loss function fusing classification, regression and attention perception is adopted in model training, and optimization is carried out in combination with self-adaptive strategies such as course learning and difficult case mining. Finally, the model scale is compressed through network pruning, quantification and knowledge distillation technologies, the model is finally deployed on edge computing equipment, and high-precision and low-delay real-time detection and response of road diseases are achieved through a reasoning acceleration engine. According to the invention, the problems of low precision and slow speed of road small target disease detection on an embedded terminal in the prior art are effectively solved.

The invention provides a lightweight intelligent detection device and method for railway tunnel lining diseases, and the method comprises the following steps: detecting the internal structure of a detected tunnel lining, and constructing a tunnel lining finite element model according to a detection result; marking injury points in the tunnel lining according to a detection result to obtain positions of the internal injury points; constructing a disease detection model according to the position of the internal disease and periodically acquired lining inner wall surface image data; and detecting tunnel lining diseases by using the disease detection model. According to the lightweight intelligent detection device and method for the railway tunnel lining diseases, accurate putting of detection resources is achieved, the high-risk disease detection rate is increased to 95% +, and meanwhile the lightweight characteristic (lt; 25MB model), and provides intelligent decision support for safe operation and maintenance of the railway tunnel.

The invention discloses a cotton disease detection method and device fusing adaptive feature enhancement and a lightweight strategy, and the method comprises the steps: constructing a cotton leaf disease data set, dividing the preprocessed cotton leaf disease data set, and writing a data set label file; based on the data set label file, designing a cotton similar disease intelligent detection model based on a high and low frequency feature enhancement module and an attention guidance fusion module; and detecting cotton diseases based on the intelligent detection model for similar cotton diseases. The device comprises a processor and a memory. According to the method, the double problems that the recognition precision of cotton disease detection based on computer vision is limited by a complex background and a small scab target and a high-performance model is difficult to deploy on resource-limited equipment are solved.

The invention provides a corn disease and insect pest detection method based on MTA-YOLOv11. The problem that an existing detection method is insufficient in small target recognition and edge feature perception in a complex environment is solved. According to the method, on the basis of YOLOv11n, an MERDEM module, a TK-Focus Block module and an MLKD module are introduced and used for enhancing a detail edge, focusing a key area and fusing multi-scale global semantics respectively, and therefore the model can more accurately detect pest and disease damage targets under the complex background; the method comprises the following steps: constructing a Corn-d corn disease and insect pest image data set, dividing the Corn-d corn disease and insect pest image data set into a training set and a verification set in proportion, evaluating a training process of an MTA-YOLOv11 model through the verification set and storing an optimal model weight, and reasoning a corn field image to be detected according to an optimal model corresponding to the optimal model weight, and outputting a detection result containing the disease and pest category, the positioning frame and the visual label. The corn disease and pest detection precision is improved, and the method is light in weight and high in adaptability to complex scenes.

The invention discloses an outer wall multi-type disease detection method and system based on unmanned aerial vehicle inspection, and the method comprises the steps: determining an unmanned aerial vehicle shooting route according to the size of a detected building and environment information, and collecting image data; carrying out enhancement processing on the image data and marking external facade crack, peeling, water seepage and hollowing defects; constructing and training an image fusion network based on the GAN, and obtaining a trained fusion image generator; inputting the fused image into an open source model for image instance segmentation, and separating the defect from the external facade background; and carrying out operations such as feature extraction and registration on the local image by using an SURF algorithm, and processing an image overlapping region to obtain a global defect detection image. By means of the unmanned aerial vehicle, the visual and accurate building outer wall disease fusion image can be obtained through the deep learning algorithm, the practicability is high, the limitation of single-mode disease detection is overcome, multiple outer wall diseases can be recognized, and the outer wall disease detection efficiency is improved.

The invention relates to the technical field of building disease detection, in particular to an urban building disease detection method and device, electronic equipment and a storage medium. Multi-modal image data formed by original visible light and thermal infrared image data is obtained, and an original thermal infrared image is subjected to geometric correction; calculating a mapping relation with an original visible light image so as to complete pixel-level registration, obtaining target multi-modal image data, inputting the target multi-modal image data into a hierarchical deep learning recognition model, recognizing building disease information, then performing three-dimensional space mapping, generating a building three-dimensional mesh model containing disease three-dimensional space setting coordinates, and finally performing three-dimensional mesh modeling. And then calculating a relationship between a model surface grid vertex and a disease point cloud density, generating a disease distribution thermodynamic diagram, analyzing disease aggregation characteristics in multiple dimensions according to the thermodynamic diagram, and quantitatively analyzing spatial correlation between the disease and a building construction node in combination with building component information. According to the invention, the urban building disease detection efficiency and precision are improved.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

The invention discloses an intelligent highway pavement disease detection method and system based on multi-source data fusion and a YOLO optimization algorithm. The method comprises the following steps: acquiring a multi-source data set, and constructing a unified data input set through time synchronization, spatial registration and resolution standardization processing; a multi-scale feature extraction network fusing deformable convolution and an attention mechanism is adopted to enhance the feature capture capability of irregular diseases; establishing a cross-modal feature interaction mechanism, and realizing deep fusion of visible light texture, infrared thermodynamics and point cloud geometric features; end-to-end training is carried out by using a multi-objective optimization loss function, and the model adaptability is improved; and finally, automatic identification, positioning and classification of cracks, pit slots and other diseases are realized, and a structured detection report containing disease distribution, statistical evaluation and maintenance suggestions is generated. According to the invention, the detection precision and efficiency are effectively improved, and reliable support is provided for intelligent road maintenance.

The embodiment of the invention provides a tunnel disease detection method and system based on an unmanned aerial vehicle, and belongs to the technical field of defect optical detection. The method comprises the steps that an unmanned aerial vehicle is controlled to fly along a tunnel to collect multichannel image data of the surface of a structure, and the flight attitude is adjusted based on environment illumination information to execute image illumination compensation; performing image alignment of each target anchor point based on a structure anchor point atlas constructed based on historical acquisition images in combination with the flight pose information of the unmanned aerial vehicle and the multi-channel image data after illumination compensation; identifying a disease area in the aligned image, extracting disease features of each target anchor point at this time, and updating the disease of each target anchor point at this time into a time sequence feature data sequence corresponding to each target anchor point; and based on the updated time sequence characteristic data sequence of each target anchor point, Bayesian point change detection is adopted to analyze the disease evolution trend of the tunnel. According to the scheme, the alignment precision, the time sequence comparability and the risk judgment capability of tunnel disease detection are integrally improved.

The invention discloses a bridge disease detection method based on a diffusion model and a bitter fish optimization algorithm, and relates to the technical field of bridge detection. The method comprises the following steps: fixing a visual angle and a distance at an easy-to-peel or crack position of a bridge, and collecting and aligning visible light and near-infrared images; carrying out multi-scale downsampling on the image, and carrying out wavelet denoising, brightness correction and texture smoothing; inputting the preprocessing result into an improved diffusion model, weighting the edge during forward diffusion, and reversely generating and applying texture and contour smoothing; the multi-source feature channel and the reconstructed image are combined and input into a deep segmentation network, shadow and stain are eliminated by using a local difference function, and global search is performed on a segmentation threshold, a noise coefficient and the like based on a disease detection rate, a false detection rate and the like by using a bitter fish algorithm; training and correcting the high-noise area again according to the optimal parameters; and uniformly marking disease areas. According to the method, the recognition recall rate of tiny spalling and irregular cracks in an extreme environment can be greatly improved, and the intelligent level and the practical effect of bridge disease detection are improved.

The invention relates to the technical field of road detection, in particular to an intelligent road disease detection method and system based on a three-dimensional ground penetrating radar, and the method comprises the steps: obtaining and processing three-dimensional radar data, and obtaining a horizontal section image in the depth direction and vertical section images of a plurality of channels in the measuring line direction; determining the boundary position of the underground structure layer; selecting a horizontal slice image with a corresponding depth according to the boundary position, and inputting the horizontal slice image into a first disease detection model to obtain a preliminary disease candidate area; mapping the preliminary candidate region to a vertical profile image, intercepting a local vertical profile image of each channel, and inputting the local vertical profile image into a second disease detection model to obtain disease category information; fusion decision is carried out based on the transverse position of the candidate area and the disease category information of each channel, a final disease type is determined and is associated to the candidate area, consistency of disease positioning and type judgment is realized through cooperative utilization of the horizontal section image and the multi-channel vertical section image, and the accuracy and stability of detection are improved.