944 results about "Disease detection" patented technology

The invention discloses an intelligent pavement disease diagnosis method based on image recognition, and relates to the technical field of pavement disease diagnosis, and the method comprises the steps: deploying an image collection device and a pavement monitoring sensor in a target pavement region, so as to obtain multi-source pavement data; preprocessing the multi-source road surface data, and performing feature extraction to obtain a road surface feature sequence; and based on a deep learning algorithm and in combination with the pavement feature sequence, learning features of different disease types, constructing a disease identification classification model, and identifying different types of pavement diseases. Through the high-definition camera and the image processing technology, various disease types such as cracks, pit slots and ruts can be quickly and accurately identified, and in combination with a deep learning algorithm, disease features can be automatically extracted, high-precision identification of road diseases is realized, the disease detection efficiency is remarkably improved, manual intervention is reduced, and the detection efficiency is improved. And timely and accurate data support is provided for road maintenance.

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 is suitable for the technical field of agricultural intellectualization, and provides an intelligent blueberry disease detection method and system based on multi-modal unsupervised learning, and the method comprises the following steps: carrying out the feature extraction and clustering of preprocessed multi-modal data based on an adaptive contrast deep clustering framework, and obtaining a feature extraction result; obtaining a multi-modal preliminary feature and a preliminary clustering result; based on a multi-modal complementary feature fusion mechanism, according to the multi-modal preliminary features and the preliminary clustering result, carrying out adaptive weighted fusion on the multi-modal preliminary features to obtain multi-modal fusion features; performing unsupervised clustering optimization and disease type identification on the multi-modal fusion features to obtain an unsupervised learning model; and performing deployment and incremental learning on the unsupervised learning model, and detecting the blueberry diseases. According to the method, early-stage accurate detection of blueberry diseases is realized through an unsupervised learning algorithm, a new normal form is provided for intelligent accurate management of blueberries, and the disease prevention and control efficiency and industrial economic benefits are effectively improved.

The invention relates to an air-ground integrated bridge disease monitoring method and an application system thereof, and the method comprises the steps: controlling an unmanned plane to take off from a nest, and transmitting a bridge initial inspection task to the unmanned plane; sending the bridge image data to a vehicle-mounted high-performance edge server for primary three-dimensional modeling; carrying out monomer splitting on the general three-dimensional model of the bridge, and carrying out close photogrammetry on the bridge by an unmanned aerial vehicle based on a close inspection route to obtain a monomer high-resolution image of each part of the bridge; the unmanned aerial vehicle automatic inspection and data processing platform executes disease target detection and disease semantic segmentation on the single high-resolution images; and associating and binding disease target detection and disease semantic segmentation results with the bridge refined three-dimensional model to generate a bridge disease detection report. A worker only needs to draw an inspection area, and the unmanned aerial vehicle automatic inspection and data processing platform automatically generates an unmanned aerial vehicle inspection route according to the drawn inspection area, so that the unmanned aerial vehicle autonomously and safely inspects a bridge.

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 provides a fish disease identification method based on deep learning. Firstly, a new multi-branch adaptive reparametric module CKDB is provided, a plurality of parallel branches are used in a training stage to capture abundant fish disease features, and the complex branches are combined into an efficient convolutional layer through adaptive reparameterization in a reasoning stage, so that the feature expression ability of the model is effectively improved. Secondly, providing a novel multi-scale adaptive feature pyramid network N-MAFPN: the network adopts an adaptive feature fusion strategy, and through effective fusion and multi-branch feature extraction of features of different scales, the detection precision of the model on fish diseases is improved; finally, a multi-scale attention enhancement module SM-Detect is introduced to the head of the network; and disease area features concerned by the model are dynamically adjusted by using a self-adaptive attention mechanism on feature maps of different scales, so that the fish disease detection capability of the model is improved. The effectiveness of the proposed algorithm structure is proved through an ablation experiment, the problem of insufficient identifiability of fishes caused by diversity of intensive aquaculture environments and rapidity of movement is solved, and the method is a key progress for promoting intelligent health monitoring of fish diseases.

The invention discloses a lightweight tea disease target detection method based on TeaDisease LiteNet, and the method comprises the following steps: collecting and marking tea disease images in a real environment, and constructing a tea disease data set; the method comprises the following steps of: dividing a main network into a training set, a verification set and a test set, performing data enhancement, using a MobileNetV3 module in the main network, using a Slim-neckAKConv module in the neck network, adding an iRMBEMA attention mechanism, using a Shape-IoU regression loss function, and using a SlideLossEMA classification loss function; the optimized target detection model is obtained through training, the model is used for tea disease detection, and the method has the technical characteristics that the problems that an existing tea disease detection model is high in calculation complexity, large in model parameter quantity, difficult to efficiently operate on equipment with limited resources and the like can be solved.

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 plant disease detection method based on an improved YOLOv8n network, and the method comprises the steps: optimizing a backbone network and a detection head network of the YOLOv8n network based on a DRGhostConv module and an efficient channel attention module for a to-be-detected plant disease image, and employing a multi-scale space attention module as a neck network of the optimized YOLOv8n network, a plant disease detection model is constructed and obtained, the public data set is used as input of the plant disease detection model for training and testing, parameters of the plant disease detection model are updated with minimization of a CIoU loss function as a target, and the trained plant disease detection model is obtained; and identifying and positioning the to-be-detected plant disease image based on the trained plant disease detection model to obtain a disease detection result of the to-be-detected plant disease image. According to the method, the lightweight design of the model is realized while the detection precision is ensured, and the real-time detection capability on resource-constrained equipment is improved.

The invention relates to the technical field of computer vision, in particular to a jujube tree disease and insect pest recognition method and system combined with a vision technology, and the method comprises the steps: extracting closed edge contours of a disease leaf gray image, and obtaining a contour skeleton of each closed edge contour; based on the graphic features of each contour skeleton, obtaining a to-be-recognized region, analyzing the distribution of gradient amplitudes of the skeleton contour edge and inner pixel points and the gradient amplitude distribution of the skeleton contour outer pixel points, and obtaining an edge transition value of each to-be-recognized region; based on the overall distribution characteristics of the gradient amplitudes of all edge pixel points of the skeleton contour of each to-be-identified area, obtaining an edge energy value of each to-be-identified area; and determining an edge distinguishing value of each to-be-identified area, and obtaining a disease identification result of each to-be-identified area. The invention aims to improve the identification capability of the jujube brown spot and the jujube gray leaf spot and improve the identification precision of disease detection.

The invention discloses a fruit tree disease and pest detection method and device based on improved YOLOv11, relates to the technical field of automatic machine learning, and aims to solve the problem that YOLOv11 in the prior art is still difficult in apple disease and pest detection. Taking the fruit tree shot image as input, and outputting based on the fruit tree pest detection model to obtain a fruit tree processing graph; and judging whether the fruit tree has diseases and pests and the types of the diseases and pests according to the fruit tree processing graph, wherein the fruit tree disease and pest detection model is obtained based on improvement of a YOLOv11 model. According to the method, advanced feature extraction, layered feature fusion and enhanced spatial perception functions are integrated, efficient and accurate detection of fruit tree diseases and insect pests is realized, the robustness and generalization ability of a detection model are improved, and meanwhile, relatively high performance is kept in a complex environment.

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 provides a deep high-precision underground disease detection method based on GPR, and relates to the technical field of geophysical exploration. Establishing a road disease forward model, and initializing the road disease forward model; obtaining an initialized road disease forward model and adding preset noise information into a result to generate fused echo data; preprocessing the fused echo data to generate target echo data; constructing a road disease data set according to the target echo data, and performing multi-frequency data fusion in the road disease data set to generate multi-frequency data; constructing a super-resolution convolutional neural network according to the multi-frequency data, and training the super-resolution convolutional neural network; inputting detection data into the trained super-resolution convolutional neural network model, and obtaining a detection report; and the road disease radar detection performance is improved.

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 relates to the technical field of image recognition, in particular to an agricultural crop disease and pest detection method and system based on image segmentation, and the method comprises the steps: collecting a vertical view angle image and an inclined view angle image of a tea garden through an unmanned aerial vehicle, carrying out the image registration through SIFT feature extraction, KNN matching and RANSAC filtering, and carrying out the alignment to a reference image coordinate system. And segmenting the foreground region by adopting a U-Net algorithm, and performing post-processing optimization. Positioning a disease area through a color detection method, extracting disease features, and identifying a disease stage by using a CNN algorithm; and using a YOLOv3 algorithm to identify insect body areas, extracting insect pest features, and evaluating insect pest degrees. And finally, based on the disease stage and the pest degree, using an LSTM algorithm to predict the pest diffusion trend, and generating a space thermodynamic diagram according to the reference image coordinate system, thereby improving the detection precision and timeliness, and providing technical support for intelligent agricultural prevention and control.

The invention provides preparation of an anti-p-Tau217 antibody and application of the anti-p-Tau217 antibody in an Alzheimer's disease detection kit, and belongs to the technical field of antibodies and immunodetection, the anti-p-Tau217 antibody comprises a heavy chain variable region and a light chain variable region, a coding gene of the heavy chain variable region comprises a nucleotide sequence shown in SEQ ID NO.1, and a coding gene of the light chain variable region comprises a nucleotide sequence shown in SEQ ID NO.2. The coding gene of the light chain variable region comprises a nucleotide sequence shown in SEQIDNO.3, the heavy chain variable region comprises an amino acid sequence shown in SEQIDNO.2, and the light chain variable region comprises an amino acid sequence shown in SEQIDNO.3. The invention also discloses a preparation method. The invention provides preparation of an anti-p-Tau217 antibody and application of the anti-p-Tau217 antibody in an Alzheimer's disease detection kit. The successful expression of the monoclonal anti-p-Tau217 antibody with high affinity is realized, and the monoclonal anti-p-Tau217 antibody is used for detecting the Alzheimer's disease.

The invention discloses a road inspection method, device and equipment based on a large model and a storage medium, relates to the field of traffic, is applied to a cloud intelligent layer, and comprises the following steps: obtaining target data sent by a visual perception layer; processing the target data by using a target large model located in a cloud intelligent layer to extract to-be-optimized local features, optimizing the to-be-optimized local features based on traffic physical characteristics, and performing disease detection and risk prediction on a target road based on the target local features to obtain a disease detection result and a risk prediction result, and generating a road maintenance suggestion corresponding to the target road based on the target local feature, the disease detection result, the risk prediction result and the target knowledge graph to complete road inspection of the target road. Wherein space-time synchronization among the cloud intelligent layer, the visual perception layer and the edge calculation layer is realized based on a precision time protocol and cubic spline interpolation. According to the invention, the requirements of real-time early warning and full-life-cycle management in a road inspection scene are met.

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 provides a disease and pest detection method based on YOLOv8, and belongs to the field of target detection. The problems that small targets are difficult to recognize, fruits are densely distributed, leaves are seriously shielded, the environment illumination change is large, computing resources are limited and the like in fruit disease and insect pest detection are solved. Comprising the following steps: constructing a data set and performing preprocessing; an improved YOLOv8 target detection network model is constructed, the model comprises a backbone network, a neck network and a plurality of detection heads, the backbone network adopts a DenseNet and ResNet fusion architecture and comprises variants of a C2f module, the variants comprise a C2f-OREPA module, a C2f-SPD module and a C2f-DBB module, the neck network realizes dynamic feature sampling and splicing through Dysample and Concat, and the detection heads adopt the design of diversified branch blocks; carrying out model training by adopting a loss function for fusing a denoising result with a feature layer through jump connection; the loss function is solved through ADMM iteration, and a solving result is fused with features output by the DAE-DenseNet network; the method is applied to fruit pest detection.

The invention relates to the technical field of road disease detection, in particular to a light unmanned aerial vehicle aerial photography road disease detection method and system for a complex background, and the method comprises the steps: obtaining the data of an unmanned aerial vehicle aerial photography road disease image, and carrying out the preprocessing; inputting the preprocessed image data into a CSGEH-YOLO model to carry out feature extraction, fusion and detection; and outputting a road disease detection result. Wherein the CSGEH-YOLO model comprises a C2f-Start-CAA feature extraction structure in a backbone network, which is called as a CSC feature extraction structure for short, and is used for enhancing the detail capture capability and the global feature extraction capability of complex scene features; an improved generalized generalized feature pyramid network GFPN is introduced into the neck and is used for fusing feature information of different scales; the lightweight detection head EP-Detect is used for reducing the calculation complexity and parameter quantity of the model; and a WiseIoUv3 loss function is fused into the total loss function to optimize bounding box regression. According to the invention, road diseases in a complex scene can be effectively detected, and the precision and the calculation efficiency are remarkably balanced.