415 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 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 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 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 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 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 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 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.

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.

The invention discloses a subway tunnel full-section intelligent inspection robot system. The system comprises a mobile platform; a disease detection module; the data processing unit adopts an embedded industrial mainboard, is internally provided with a filtering algorithm and a ResNet50 deep learning model, processes the image and the point cloud in real time, automatically identifies the type, the position and the size of the disease and generates a three-level report; a main channel of the communication module is a double 5G industrial module, supports real-time uploading of a disease result, automatically switches a local cache when 5G is interrupted and supplementarily transmits the disease result afterwards, and is provided with a Bluetooth unit for on-site short-distance data calling; the power supply module adopts two groups of 12V / 100Ah lithium iron phosphate batteries which are connected in parallel, the endurance is greater than or equal to 4 hours, a brake energy recovery device is integrated, the battery is charged back during downhill or deceleration, the endurance is prolonged by about 15%, and one-hour fast charging is supported; and an auxiliary function module. According to the method, the consistency and reliability of detection data are remarkably improved, and a stable basis is provided for subsequent high-precision disease recognition.

The invention relates to the technical field of fruit tree disease detection, in particular to a fruit tree disease detection method based on image recognition, and the method comprises the steps: collecting images of leaves, fruits and branches of a fruit tree, and corresponding environment and physiological data; image features are extracted through a convolutional neural network, and time sequence modeling is carried out on environment and physiological data; inputting the multi-source features into a fusion network for weighted fusion to generate a fusion feature vector; inputting a disease recognition model, and outputting a multi-disease recognition result; when the confidence coefficient is low or feature conflicts exist, auxiliary judgment and correction are carried out in combination with environment and physiological data; outputting the corrected disease type, the disease spot position and the confidence coefficient, and generating a distribution diagram and a detection report; and marking a low-confidence or unidentified sample as an incremental sample, checking and storing the incremental sample, and performing online fine adjustment or transfer learning on the model based on the incremental sample to realize adaptive optimization of the model.

The invention provides an intelligent road management and maintenance system and method based on multi-model fusion, and relates to the technical field of road maintenance and intelligent traffic. The method comprises the following steps: acquiring road condition data and positioning data through a multi-source data acquisition module; the disease recognition module is combined with a neural network model to analyze the multi-modal data, so that the disease detection and recognition precision is improved; the big data processing early warning module mines a disease rule through a big data model, and realizes disease early warning and predictive maintenance; the digital twin modeling module is used for constructing a road digital twin model and realizing real-time monitoring and simulation analysis; and the decision scheduling module realizes automatic maintenance scheduling based on the output of the modules. Through multi-model deep fusion, the problems of high manual dependence, low precision, lack of predictability and scheduling lag in traditional road management and maintenance are solved, the road maintenance efficiency, quality and intelligent level are remarkably improved, and the maintenance cost is reduced.

The invention relates to the technical field of road detection, in particular to a multi-dimensional road disease detection method and system based on a ground penetrating radar. The method comprises the following steps: acquiring original ground penetrating radar data, performing DC component removal, gain adjustment and background denoising on the original ground penetrating radar data, positioning a suspected disease area and defining the suspected disease area as a disease entity; extracting a time domain feature, a frequency domain feature and a spatial context feature of each disease entity in parallel to form a multi-dimensional feature vector; constructing a road disease knowledge graph according to the multi-dimensional feature vectors, and distributing an initial weight for each graph relation; and performing node matching operation according to the multi-dimensional feature vector and a knowledge graph, performing logical reasoning according to a graph relation path, fusing similarity and reasoning confidence, and outputting a diagnosis tag signal and a confidence signal. According to the method, the positioning accuracy of the suspected disease area is improved, comprehensive utilization of multi-dimensional information is realized, and a visual causal link is provided for diagnosis.

The invention relates to a road infrastructure full-automatic inspection method, device and equipment based on an unmanned aerial vehicle group, and a medium thereof. The method aims at the problem that disease characteristics collected by unmanned aerial vehicles in traffic flow and vegetation shielding scenes are broken, and the method comprises the following steps of: decoupling texture and motion characteristics of a dynamic shielding area; feature maps of the effective area and the shielding area are separated; performing cross-frame splicing and continuous repair on the fracture features by using an optical flow constraint generation network, and reconstructing the geometric integrity of a shielded region; infrared data are fused to correct thermal deformation errors, and a high-precision continuous curved surface model is generated; and finally, based on a disease quantitative feature vector matching maintenance strategy, realizing full-process automation from data acquisition to decision output. According to the method, the continuous modeling precision and the maintenance decision reliability of pavement disease detection in the shielded environment are remarkably improved.

The invention particularly relates to a respiratory disease diagnosis device and method, and the device comprises a respiratory sound signal collection unit which is used for obtaining a current respiratory sound signal; the breathing sound signal preprocessing unit is used for performing segmentation processing and denoising processing on the current breathing sound signal to obtain a preprocessed breathing sound signal; the feature extraction unit is used for extracting multi-modal features of the preprocessed breath sound signals; and the detection unit is used for inputting the multi-modal features into a preset deep learning network model to obtain a respiratory system disease detection result. Therefore, through multi-modal feature extraction, dynamic fusion and an efficient classification strategy, the problems of relatively low feature identification degree, relatively low disease classification accuracy and the like in the process of researching disease diagnosis based on the breath sound signals in related technologies are solved, and the detection performance of chronic respiratory system diseases based on the breath sound signals is remarkably improved.

The utility model discloses a movable inspection vehicle for bridge expansion joint disease detection, which comprises a base assembly, a monitoring assembly, a control assembly and a spray head assembly, the upper end of the base assembly is fixedly connected with the monitoring assembly, the upper end of the monitoring assembly is fixedly connected with the control assembly, the monitoring assembly is also internally fixedly connected with the spray head assembly, and the spray head assembly is fixedly connected with the base assembly. The device is characterized by further comprising a base assembly; the left end and the right end of the bottom plate are rotationally connected with the rotating plate; a first threaded groove is formed in the rotating plate; the first thread groove is further formed in the lower end of the fixing frame; the upper end of the fixing frame is fixedly connected with the lower end of the damping air bag; and by arranging the spray head assembly capable of automatically spraying and marking the disease position, when the device detects the disease position, manual marking is not needed, automatic spraying and marking can be achieved, and convenience is brought to automatic spraying and marking.

The invention discloses an unmanned aerial vehicle road disease detection and analysis system and method based on a YOLO algorithm, and the method comprises the following steps: synchronously collecting a road image and geographic coordinates through an unmanned aerial vehicle, and constructing an image data set with geographic reference; constructing an initial YOLO target detection model based on the data set; introducing an additional detection layer for detecting a pixel area into the model, and embedding a channel attention mechanism into the feature extraction network to obtain an improved YOLO target detection model; reasoning a data set by using the model, and outputting a disease category, a bounding box coordinate and a confidence coefficient; pixel coordinates are converted into geographic coordinates through a coordinate conversion algorithm, and disease records with accurate geographic coordinates are generated; and optimizing and updating the model based on the record as an incremental training sample. According to the method, the disease detection precision and the small target identification capability are effectively improved, and the long-term adaptability and generalization performance of the model are enhanced through a closed-loop incremental learning mechanism.

The invention relates to the technical field of geological survey, in particular to a rapid stratum disease risk detection method based on a ground penetrating radar, and solves the technical problem of depth positioning misalignment caused by propagation parameter fluctuation due to heterogeneity of an underground medium in stratum disease detection of the ground penetrating radar in the prior art. The method comprises the following steps: acquiring a reflected wave signal at each acquisition moment through a ground penetrating radar; performing signal decomposition processing on the reflected wave signal, separating an intrinsic mode component representing dielectric noise, and performing feature extraction on the intrinsic mode component to determine a dielectric noise intensity index; constructing a dielectric noise intensity sequence according to the dielectric noise intensity index, and performing spatial-temporal characteristic analysis to determine the confidence coefficient of stratum diseases; and according to the dielectric noise intensity index and the stratum disease confidence coefficient, in combination with a pre-calibrated stratum average dielectric constant, constructing a depth correction weight, carrying out adaptive correction on the original stratum disease depth, and outputting the corrected stratum disease depth.

The invention relates to an underground pipeline disease intelligent detection and evaluation method and system, and the method comprises the following steps: S1, obtaining underground pipeline multi-source heterogeneous data, and constructing an underground pipeline detection knowledge graph; s2, according to the detection task instruction, based on a multi-modal detection robot, obtaining a pipeline multi-modal original data set; s3, preprocessing the obtained multi-modal original data set of the pipeline to obtain a structured feature data set; s4, constructing an intelligent recognition model, performing defect detection, and outputting a final defect intelligent recognition result through a multi-modal data fusion decision algorithm; s5, performing three-dimensional space positioning on each identified defect according to an intelligent defect identification result, automatically marking the three-dimensional space on a digital pipe network map, and quantifying an RBI risk index of each defect; and S6, according to the RBI risk index of each defect, calling the underground pipeline detection knowledge graph to intelligently match the optimal trenchless repair scheme, and obtaining a detection report. According to the invention, the speed of underground pipeline disease detection and the consistency of evaluation are effectively improved.

The invention relates to the technical field of agricultural disease detection and crop disease resistance screening, in particular to a rice bacterial leaf blight disease resistance screening method based on an unmanned aerial vehicle and deep learning, and adopts the technical scheme that an optimized YOLOv11-OBB model is composed of a C3k2FC module, an SPPFLSKA module, a SlimNeck module and a LiteHead module, is responsible for extracting features in an image and positioning bacterial leaf blight spots, and is used for screening the disease resistance of the rice bacterial leaf blight; bacterial leaf blight can be efficiently and accurately detected; a lightweight deep learning model is used, efficient operation on a resource-limited unmanned aerial vehicle platform can be realized, and the equipment and calculation cost is reduced; through the cooperation of a plurality of optimization modules, the detection precision of bacterial leaf blight spots is remarkably improved, and a stable detection effect can be kept; the operation process is simplified, and full automation of bacterial blight disease resistance screening is realized; therefore, low-cost, efficient, automatic and high-precision bacterial blight disease resistance screening is realized by utilizing the lightweight and optimized YOLOv11-OBB model and combining module design with high calculation efficiency, and the method is suitable for large-scale field application.

The invention relates to a building block pavement disease detection method and equipment based on an RGB-D image, and the method comprises the steps: collecting a pavement color image and a depth image, inputting a pre-constructed semantic segmentation model, and obtaining a segmentation result of a building block and a thin and narrow seam; based on the depth image, mapping a segmentation result of the building block and the thin and narrow seam to a three-dimensional point cloud space; performing plane fitting on the point cloud data by adopting a random sampling consistency algorithm, extracting a point cloud contour, and calculating horizontal displacement between adjacent building blocks; respectively fitting the point clouds of the adjacent building blocks as independent planes by adopting a random sampling consistency algorithm, and calculating the vertical displacement between the adjacent building blocks; and based on the horizontal displacement and the vertical displacement between the adjacent building blocks, generating quantitative data of horizontal displacement and vertical displacement of the building block pavement of the target road section, marking disease positions exceeding a preset limit value, and completing disease detection of the building block pavement. Compared with the prior art, the method realizes accurate quantification of horizontal and vertical displacement between the building blocks of the building block pavement.

The invention discloses a road disease detection and standing book updating method and device, equipment and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected road image and the position information of a road region corresponding to the to-be-detected image; extracting a marking mask image of the to-be-detected road image, performing skeletonization and topological repair processing on the marking mask image, and determining a marking center line; based on the position information of the road to be detected and the position information corresponding to each ledger image in the traffic marking ledger, determining a plurality of candidate ledger images to be updated from the traffic marking ledger; and based on the comprehensive similarity between the to-be-detected road image and the candidate to-be-updated standing book image, determining a standing book updating strategy. According to the invention, intelligent, real-time and refined maintenance management of the road marking and the associated facility ledger is realized.

The invention provides a tunnel lining and pavement disease detection method, and belongs to the technical field of tunnel engineering detection. The method comprises the steps of tunnel detection area differentiation, data acquisition, tunnel three-dimensional model construction based on Gaussian sputtering, and lining and pavement disease intelligent identification. A flight route is planned in a layering-segmenting mode, and images are collected; preprocessing the image, stripping physical information such as position and color of a two-dimensional image to generate a Gaussian kernel, and establishing a three-dimensional model containing information of a whole section of tunnel; based on the constructed three-dimensional model, a YOLOv8 model is adopted for deep learning training, and the purpose of intelligently recognizing diseases is achieved. According to the method, the detection efficiency can be remarkably improved, the detection cost is remarkably reduced, and detection is carried out on the premise that tunnel passing is not affected or safety risk behaviors are not caused.

The invention relates to a highway pavement crack real-time identification method and system based on a millimeter-wave radar, and relates to the technical field of disease detection. The millimeter-wave radar is cooperated with an optical sensor, continuous scanning is performed on a pavement through the millimeter-wave radar, and multi-dimensional features are extracted for primary perception and probability determination; when a suspicious area is identified, an optical sensor is triggered to carry out high-precision image confirmation and crack identification, and finally crack information is output through decision fusion. Large-range and low-power-consumption primary perception is performed through the millimeter wave radar, and the optical sensor is triggered to perform high-precision imaging analysis on a specific area only when radar feature analysis shows that the crack possibility exists. The coarse-to-fine collaborative strategy effectively overcomes the limitation of a single sensor technology, high-precision pavement disease detection is realized, and the crack suspicion can be identified from the physical level first by directly capturing the electromagnetic wave physical property change caused by the crack.

The invention relates to the technical field of visual quality detection, in particular to a traffic engineering quality detection method and system based on machine vision, and the method comprises the following steps: calling a vehicle-mounted camera to collect a video stream and intercept a key frame, carrying out the motion deblurring of the key frame to generate a basic image, extracting high-frequency detail features through a first convolution kernel, and carrying out the recognition of the high-frequency detail features; extracting low-frequency structural features by using down-sampling and a second convolution kernel, splicing the low-frequency features after up-sampling the low-frequency features with the high-frequency features, calculating weights based on channel response intensity and generating multi-scale features, mapping the features into a defect probability matrix and calculating a dynamic threshold, screening regions with numerical values greater than the dynamic threshold as candidate connected domains, and extracting the candidate connected domains from the candidate connected domains; and extracting geometric parameters of the connected domain, comparing the geometric parameters with a standard disease form library, and determining disease categories. According to the method, the problem that fixed threshold segmentation cannot adapt to a complex environment is solved by eliminating motion blur, fusing high and low frequency features and combining dynamic threshold judgment and morphological parameter comparison, and the traffic facility disease detection precision and efficiency are greatly improved.

The invention relates to the technical field of bridge engineering, and discloses a bridge disease detection method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-modal detection information corresponding to a target bridge to be subjected to disease detection; querying a pre-constructed image-text vector library based on the multi-modal detection information to obtain a target image-text vector pair corresponding to the multi-modal detection information and identification information of the target image-text vector pair; based on the identification information of the target image-text vector pair, determining structured description information of the target image-text vector pair; performing joint detection based on the multi-modal detection information and the structured description information by using a disease detection model to obtain an initial detection result corresponding to the target bridge and a confidence coefficient of the initial detection result; and if the confidence coefficient is smaller than a preset confidence coefficient, calibrating the initial detection result by using a disease inference model to obtain a target detection result corresponding to the target bridge, thereby solving the problem that the result of bridge disease detection is inaccurate.

The invention belongs to the technical field of biological detection, and relates to a target area amplification method suitable for long-read-long three-generation sequencing, which comprises the following steps: designing at least one pair of amplification primers, and enabling the amplification primers to cover a target area; if the length of the target area is less than or equal to 20kb, designing a pair of amplification primers; if the length of the target area is larger than 20 kb, multiple pairs of amplification primers are designed, the adjacent amplification primers have overlapped areas, the multiple pairs of amplification primers are divided into two groups, and the coverage areas of the amplification primers in each group are not overlapped; performing long fragment amplification on the gDNA of the detection sample by adopting the amplification primer to obtain a to-be-detected product; carrying out library building and third-generation sequencing on the to-be-detected product; and carrying out single-gene genetic disease detection and / or haplotype analysis to obtain single-gene genetic disease information and / or haplotype information of the target gene. According to the method disclosed by the invention, the sequencing cost and the analysis time are remarkably reduced, and the method has relatively high detection accuracy.

The invention relates to a bridge inspection method and system and a computer readable storage medium. The inspection method of the bridge comprises the following steps: receiving an inspection task, and driving the inspection device according to the inspection task, so that the inspection device travels along the running guide rail of the bridge; obtaining a sequence image of the bridge surface acquired by the image acquisition assembly; identifying the disease type of the bridge based on the sequence image; and determining geometric feature parameters corresponding to the disease type according to the disease type of the bridge, and determining the severity level of the disease type based on the geometric feature parameters. According to the bridge inspection method, automatic grading evaluation of bridge apparent diseases is facilitated, errors caused by manual judgment are reduced, and the accuracy degree of disease detection is improved.

A road disease duplicate removal method based on improved TransTrack and disease continuous frame number estimation belongs to the field of intelligent traffic, and comprises the following steps: S1, vehicle-mounted video data acquisition: adopting a vehicle-mounted camera to acquire road video data; s2, image preprocessing: extracting a region of interest of the image and performing image enhancement processing; s3, road disease detection and tracking: detecting and continuously tracking the road disease by using an improved TransTrack model; s4, calculating the number of disease existing frames: estimating the number of frames experienced by each disease object from the first occurrence to the final disappearance; and S5, disease de-weighting: extracting the clearest frame of the same ID disease before the disease disappears, and realizing de-weighting. According to the method, automatic tracking and duplicate removal of road diseases are realized, the problem of repeated statistics of the same disease caused by continuous shooting of a vehicle-mounted camera is effectively solved, and the completeness of disease information is ensured by extracting the clearest frame.