5663 results about "Image detection" patented technology

A small-size vehicle detection deep learning model based on feature fusion of multi-scale modules is provided, which solves the problem of small-size vehicle image detection. The model includes a Backbone network, a Neck layer and a Head network, wherein a C2f_DCNv3 module based on the combination of deformable convolution v3 (DCNv3) and a cross stage feature fusion (C2f) module and an SPPF_LSKA module based on the combination of a spatial pyramid pooling fast (SPPF) layer and a large separable kernel attention (LSKA) module are introduced into the Backbone network; a C2f_SCConv module based on the combination of spatial and channel reconstruction convolution (SCConv) and a C2f module is introduced into the Neck layer; and a multi-scale kernel detection (MSK_Detect) module is introduced into the Head network.

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention provides a high-speed rail part crack real-time detection method, and belongs to the technical field of image detection based on computer vision. Obtaining a hyperspectral image, a visible light image, three-dimensional point cloud data and eddy current signal data of the surface of the high-speed rail part; performing spatial registration on the high-spectral image and the visible light image on the surface of the high-speed rail part, and performing time registration on the three-dimensional point cloud data based on the eddy current signal data; feature enhancement and standardization processing are carried out; performing feature extraction and fusion on the obtained standardized multi-modal data based on a multi-modal feature fusion network to obtain a fusion feature map representing crack details of the high-speed rail parts; based on a self-adaptive crack segmentation method, a crack contour is extracted from the point cloud, and then whether the part has a crack or not and the length and depth of the crack are calculated. According to the invention, three kinds of modal data are creatively integrated, the information dimension limitation of single-modal detection is broken through, and all-weather and non-contact intelligent efficient diagnosis of submillimeter cracks is realized under complex working conditions.

The invention relates to the technical field of image detection, and discloses an intelligent detection method for the machining process of an automobile decorative plate. The method comprises the following steps: performing high-resolution image acquisition and preprocessing on the automobile decorative plate to obtain standardized image data; performing region segmentation operation on the standardized image data to obtain a decorative plate target region mask image, and extracting to obtain a feature vector set; inputting the feature vector set into a ResNet-v1-50-based transfer learning network for training to obtain a defect classification model, and calculating candidate defect area masks through the defect classification model; and performing multi-scale analytic hierarchy process on the candidate defect area mask to obtain a defect risk heat map, and performing clamping quality analysis on the connection area of the clamping block and the clamping groove according to the defect risk heat map to obtain a clamping quality evaluation result. According to the method, accurate positioning of the defect area is realized, the defect risk heat map is constructed to quantify the risk levels of different areas, and data support is provided for production process optimization.

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

The invention discloses an AI generation content detection and review method, device and equipment and a storage medium, and the method comprises the steps: receiving multi-modal input data which comprises text data, image data and video data; calling a large language model to carry out compliance analysis on the text data to obtain an analysis result; when the analysis result is compliance, calling a multi-modal model to identify and detect the image data and the video data, and respectively obtaining an image detection result and a video detection result; and aggregating the analysis result, the image detection result and the video detection result to obtain a content detection result. According to the method, the processing paths are automatically allocated according to the content types, redundant calculation is avoided, hardware consumption is remarkably reduced, the multi-modal detection results are aggregated into structured output, the large-batch content processing efficiency is remarkably improved, calculation resource occupation is greatly reduced, and seamless integration of the detection results and a downstream service system is achieved.

A method for generating an agricultural cut-point for an agricultural item includes capturing an image of the agricultural item, generating a depth estimation of the agricultural item, segmenting the image of the agricultural item to generate a segmented image that identifies different segments of the agricultural item, detecting an agricultural feature of the agricultural item based on the image of the agricultural item, generating a two-dimensional cut-point based on the segmented image and the agricultural feature, and generating a three-dimensional cut-point based on the two-dimensional cut-point and the depth estimation of the agricultural item.

The invention discloses an intelligent detection method and device for fusing a medical image learning image, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring and preprocessing a bimodal medical image, and extracting a feature map through multi-scale decomposition; constructing a cross-modal correlation model, and setting a modal attention mechanism (embedding anatomical structure prior guidance feature complementation) and a morphological attention mechanism (setting lesion morphological constraint weight); the method comprises the following steps: collecting multiple types of image samples, pairing according to a focus form and an imaging mode to construct a bimodal joint data set, and correlating and labeling to generate a training data set with modal attributes; after a multi-stage iteration training model, inputting the preprocessed image to carry out feature fusion so as to obtain a fused image; and generating a lesion probability graph according to the fused image, positioning a lesion area through multi-threshold segmentation, and outputting a detection result. The system comprises a data acquisition module, a preprocessing module and the like. The method improves the accuracy and reliability of medical image detection, and is suitable for clinical multi-modal image analysis.

The invention relates to the technical field of image detection, in particular to an inorganic mineral casting detection method based on image processing, and the method comprises the following steps: obtaining an image sequence, carrying out the gray scanning, extracting a connected region, screening a closed image block, generating a candidate, building an edge path, analyzing a position change, correcting a contour, and generating a track image; evaluating the direction distribution labeling consistency to generate a mask layer, extracting a feature judgment category to generate a labeling image, and analyzing an angle difference to identify an interruption calibration position to generate a labeling layer. According to the method, the integrity of target area screening is enhanced through gray abrupt change and connected path extraction in the image sequence, spatial offset interference is eliminated through boundary trajectory stability analysis, the area judgment consistency is improved, directional discrete areas are accurately recognized through standard deviation evaluation of the pixel gradient direction, and the attribution judgment accuracy is improved; according to boundary disturbance analysis, abnormal edge expression is refined, and the image sequence information coherence utilization and abnormal feature distinguishing capability is enhanced.

The invention provides a face target detection method based on improved YOLOv8, and the method comprises the steps: carrying out the preprocessing of a face data set, constructing and training an improved YOLOv8 network model, inputting a to-be-detected image into the model, and obtaining a prediction result; the improved YOLOv8 network model comprises a backbone network, a neck network and a head network; the backbone network comprises a convolution module CBS and a feature fusion module C2f of an original model, a deformable attention mechanism DAttention is introduced to improve the feature fusion module C2f in the backbone network, the C2f of the last layer is replaced, an attention-based intra-scale feature interaction network is used to replace a spatial pyramid pooling module, and the feature fusion module C2f of the original model is obtained. And optimizing the bounding box loss function through the angle constraint loss function. According to the invention, the precision of a face target detection task is effectively improved, and especially for dense small face image detection, the problems of missing detection and wrong detection of targets are reduced.

The invention discloses a pipe online defect detection method and system, and relates to the technical field of image detection. The detection method comprises the following steps: placing a to-be-detected pipe in a dark box environment, irradiating the to-be-detected pipe in a rotating state through a light source, displaying a pipe projection on a projection surface, and setting a detection line at the upper end of the projection surface; comparing the detection line with a set reference line in real time, and marking a suspected warping area according to the local offset; performing three-dimensional image reconstruction on the suspected warping area to generate a simulation three-dimensional point cloud model; the method is technically characterized in that preliminary judgment passes through a grading strategy of'rapid screening-precise reinspection ', and the detection speed and precision are balanced; by means of the optical path principle and threshold value comparison, positioning operation of the whole-surface suspected warping area can be rapidly completed, only data of a local area (suspected warping area operation) is analyzed, the hardware cost and energy consumption are greatly reduced, and the high-speed online detection requirement is met.

The invention discloses an unmanned aerial vehicle target detection method based on a cross-spatial frequency domain and electronic equipment, and belongs to the technical field of image detection. According to the method, an unmanned aerial vehicle target detection network based on a cross-space frequency domain is constructed to carry out unmanned aerial vehicle target real-time detection; the unmanned aerial vehicle target detection network is composed of a cross-space frequency domain feature extraction network, a detail information hybrid encoder and a position relation decoder; inputting the unmanned aerial vehicle target image into a cross-space frequency domain feature extraction network, and extracting multi-scale frequency domain features of the unmanned aerial vehicle target image; performing multi-scale feature fusion and feature enhancement to obtain a multi-scale feature map; and the position relation decoder generates a target category and bounding box coordinates according to the multi-scale feature map in combination with the relative position relation features between the targets. According to the method, complementary information of a spatial domain and a frequency domain and potential spatial position relation characteristics between targets can be fully utilized, and the target detection precision and robustness of the unmanned aerial vehicle under a complex background are remarkably improved.

The invention discloses an image detection method based on an improved YOLO algorithm. The method comprises the following steps: collecting a nuclear magnetic resonance image with a focus; a target detection network model is constructed: basic framework improvement is carried out based on YOLOv8, an improved backbone network comprises a C2f module, a convolution module and an improved multichannel spatial pyramid pooling module SPPMC, and the image feature extraction capability is enhanced; the improved neck network comprises an up-sampling layer, a splicing layer, a C2f module, a convolution layer and an embedded attention mechanism UECA module, multi-scale feature map fusion is carried out, and the feature fusion efficiency is improved; the improved header network adopts an improved bounding box regression loss function L (iS-IoU), the convergence speed of the model is increased, and the positioning precision is improved; inputting a training image set into the target detection network model for training; and performing image focus detection by using the trained target detection network model. The method has the characteristic of effectively improving the robustness and the detection performance of the target detection model.

The invention relates to the technical field of intelligent driving, and discloses an obstacle avoidance analysis system for intelligent driving. The system comprises a data acquisition module, a road feature extraction module, a lane type judgment module, a virtual marking generation module, an obstacle detection and trajectory prediction module, a dynamic obstacle avoidance decision module and a system verification and optimization module. The method comprises the following steps: acquiring data through a laser radar, a camera and the like; fusing point cloud and an image to extract the width and curvature of a road; judging the type of a lane according to the width; generating a virtual marking line on a marking-free road, fitting by using a B-spline curve when the curvature is large, fusing the point cloud and the image to detect an obstacle, and predicting a track by using Kalman filtering or a social force model. In combination with virtual marking and risk assessment, a safe trajectory is generated through an OccupanyGrid map, an A * algorithm and MPC, and a simulation and real road test optimization system is adopted, so that the problem of marking-free road trajectory planning deviation is solved, and the obstacle avoidance precision and the road condition adaptability are improved.

The invention relates to the technical field of computer vision and pattern recognition, in particular to a deep-forged face image detection method based on a double-flow CNN and ViT hybrid model. The method comprises the following steps: preprocessing a real video and a forged video to obtain an original face image; obtaining a high-frequency noise residual error feature image of the original face image based on an improved high-frequency noise residual error feature extraction module; performing first feature enhancement operation on the original face image and the high-frequency noise residual feature image based on a double-flow CNN; carrying out secondary feature enhancement operation on the original face image and the high-frequency noise residual feature image which are subjected to the primary feature enhancement operation on the basis of double-flow ViT; and fusing the features of the original face image and the high-frequency noise residual feature image after the secondary feature enhancement operation, performing true and false prediction on the fused features, and outputting a detection result. The objective of the invention is to solve the technical problems of feature splitting, local-global imbalance and insufficient cross-modal interaction in the prior art.

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

The invention discloses a power equipment defect detection method and system, and belongs to the technical field of image detection, and the method comprises the steps: carrying out the multi-angle image collection of the surface of a transformer through a camera device, and carrying out the image preprocessing through a self-adaptive illumination compensation algorithm; a Faster R-CNN model is utilized to carry out defect area detection on the preprocessed image, and positioning of a defect area is optimized through a non-maximum suppression algorithm; based on the optimized defect area, defect features are extracted by using a multi-modal feature extraction network, feature vectors are generated, and defect classification is carried out; performing correlation analysis on the historical data and the defect classification result through a graph neural network, judging a defect reason and generating a defect detection report, collecting a field maintenance result and updating a defect sample library; and automatic detection and classification of the defects of the power equipment are realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an image detection method, device, equipment and medium based on adversarial generation. Performing global feature modeling by adopting an image feature extraction module of a self-attention mechanism, generating a forgery probability graph in combination with a forgery region recognition module, constructing a joint loss function based on a detection loss value and an adversarial loss value, and optimizing model parameters of a generator, the feature extraction module and the recognition module through the joint loss function to obtain a forgery probability graph; and finally, a counterfeit detection model for identifying counterfeit information in the image is formed. According to the method, the diversity of training data is improved through adversarial sample generation, the image feature modeling capability is enhanced through a self-attention mechanism, multi-dimensional loss optimization is realized through fusion of counterfeit region difference information, and the detection precision and robustness of the model to a counterfeit image are improved.

The invention discloses a concrete structure crack image detection method and system based on deep learning. The method comprises the following steps: acquiring a multi-view image; performing spatial alignment according to the multi-view image to obtain a matched image sequence; performing image fusion according to the matched image sequence to obtain a fused image; performing semantic segmentation based on transfer learning according to the fused image and a preset neural network model to obtain a crack mask; according to the crack mask and the fusion image, skeleton extraction is carried out, and a preliminary crack network topology is obtained; optimizing the preliminary fracture network topology to obtain a fracture network topology; carrying out finite element analysis according to preset material attributes, a stress state and a concrete structure to obtain a stress distribution cloud picture; and according to the stress distribution cloud picture and the fracture network topology, carrying out space matching to obtain a stress fracture correlation model. The method can analyze the dynamic change of the concrete crack.

The invention discloses a plastic particle detection method and system based on image recognition, and relates to the technical field of plastic particle detection, and the method comprises the steps: obtaining plastic particle image data, and obtaining particle weight sensor actual measurement data corresponding to the image data; constructing an image recognition model based on the image data to obtain an image estimation total weight; based on the image estimation total weight and actual measurement data of the weight sensor, constructing a feedback verification model and performing calculation to obtain an artifact suspected degree value; and judging artifact interference in the image recognition model according to the image estimation total weight and the artifact suspected degree value, thereby realizing plastic particle detection based on image recognition. The problems that in the prior art, image recognition errors are difficult to correct, and artifact interference cannot be quantized are effectively solved, the detection accuracy of the detection method in a complex environment is improved, and cooperative control of an image detection system and a feedback device is achieved.

The invention relates to the technical field of image processing, and provides an enteromorpha remote sensing image detection method and system based on deep learning, and the method comprises the following steps: carrying out the edge gradient extraction and small target enhancement processing of an obtained to-be-detected remote sensing image, and obtaining a first feature map after the edge enhancement and small target feature enhancement; transmitting the first feature map to a U-shaped backbone network formed by cascading multiple stages of Ep-VSS block modules, performing multi-stage feature extraction, and obtaining a detection result of the enteromorpha remote sensing image based on the feature map output by the last stage of Ep-VSS block module; according to the method, through edge enhancement, small target sensitive detection, multi-scale texture extraction and spatial context modeling, precise boundary segmentation and long-range dependence modeling are realized, and the accuracy, real-time performance and reliability of enteromorpha remote sensing monitoring are improved.

The invention relates to the technical field of pneumoconiosis image detection, and discloses a pneumoconiosis-oriented chest X-ray image processing method based on an AI small shadow detection rate. According to the method, a basic mask is generated through an initial segmentation network, and then an accurate lung contour is obtained through correction of a hierarchical optimization unit. The multi-scale feature extraction module analyzes gray features in the contour and constructs an initial small shadow probability distribution map. A morphological structure analyzer identifies discrete shadow regions and marks suspicious shadow clusters, and a dynamic confidence feedback mechanism evaluates its credibility to generate an optimized feature map. A shadow cluster topological incidence matrix is established by the spatial relation modeling network and matched with a pneumoconiosis pathological feature library to screen a target shadow area. The three-dimensional reconstruction engine generates a small shadow volume density thermodynamic diagram, the hierarchical fusion module integrates the thermodynamic diagram and original image space coordinates, an enhanced distribution map is output, and finally structured diagnosis report data is generated.

The invention discloses a target detection method suitable for small creatures in an underwater complex scene, and aims to solve the problems that small target features in underwater image detection are fuzzy, small targets are difficult to accurately position and identify by the existing detection algorithm, and efficient detection is difficult to realize on equipment with limited computing resources. According to the method, on the basis of a YOLOv8 network and a backbone network, a P2 feature layer is adopted to enhance the expression ability of a small target, an ADwn lightweight down-sampling module is introduced, efficient feature down-sampling is achieved, a neck network constructs an SOEP small target feature enhancement pyramid module, feature recombination is conducted on P2 through SPDConv, efficient feature integration is conducted through CSP-OmniKernel, and therefore the expression ability of the small target features is improved; and feature fusion is carried out through bidirectional cross-scale connection, so that multi-scale information interaction is enhanced, and the detection capability of small targets is improved. The target detection network is more suitable for small biological target detection in an underwater complex environment, and provides powerful technical support for underwater ecological monitoring, marine organism research and other applications.

The invention relates to the technical field of image detection, and discloses a printing defect detection method and system based on computer vision. According to the method, a multi-mode imaging device collects a printing surface multi-channel fusion image, distortion is corrected through a dynamic segmentation module, a detection area mask is extracted, and a standard image is output. Inputting the standard image into a flaw detection model, extracting microscopic lattice points and macroscopic texture features, and matching with a template library to generate a probability distribution diagram; and performing multi-level judgment according to the image, quantitatively evaluating the flaws, and finally outputting flaw coordinates with space-time labels and type labels. The system comprises a multi-source acquisition module, a geometric correction module, a feature matching module, a grading decision-making module and a closed-loop control module, and real-time detection and feedback control of printing flaws are achieved. The method improves the detection precision and efficiency, is suitable for different types of printed matters, and has a wide application prospect.

The invention belongs to the field of computer vision and industrial defect detection, and discloses a steel coil end face defect detection method and system based on CBAM-BiFPN and multi-loss optimization, and the method comprises the steps: image preprocessing and region extraction: extracting a steel coil end face region through OpenCV and other image processing methods; dividing the high-resolution image into a plurality of small blocks and performing data enhancement; the method comprises the following steps: constructing a YOLOv11 network based on CBAM-BiFPN, introducing a CBAM attention mechanism and BiFPN feature fusion structure, and constructing an improved YOLOv11 detection network; multi-loss function joint optimization training is carried out, and model training is carried out in combination with loss functions such as Focal Loss and WIoU; and fusion of detection results and defect reconstruction output: reconstructing original image defects of all tile image detection results in a space coordinate mapping and redundant region fusion mode, and realizing high-precision overall detection output. Compared with a traditional method, the method still has the high recognition capability in a complex background and low-contrast scene, the detection precision and stability are obviously improved, and higher industrial adaptability and practical value are achieved.

The invention relates to the technical field of optical detection, in particular to an AOI automatic optical detector calibration method and system, and the method comprises the following steps: obtaining a standard color palette RGB reflection reference, collecting a measured value, calculating an offset contrast, extracting image brightness, generating a correction ratio, converting gray level, extracting abrupt change, screening anomalies, and analyzing a brightness trend to generate a finishing record. And formulating a rule matching correction output comparison table. According to the method, the accuracy and reliability of image detection are enhanced through detailed analysis and accurate correction of color offset and brightness difference, the scheme combines real-time light source data and image information, fine changes are effectively identified, the misjudgment rate is reduced, the accuracy of gray and color correction is improved through comprehensive analysis of light intensity offset and regional brightness trend, and the accuracy of image detection is improved. The detection efficiency and quality of a high-density circuit are ensured, the detailed calibration mechanism remarkably reduces detection errors especially in a high-precision environment, and product quality control and the stability of a production process are optimized.

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention discloses a high-precision image detection method for a micro-drill blade surface based on deep learning, and the method comprises the following steps: S1, collecting a visible light image and a structured light image of the micro-drill blade surface, and completing the image preprocessing; s2, performing spatial alignment on the image, executing cross-modal fusion, and generating a feature fusion tensor; s3, inputting the feature fusion tensor into a multi-scale residual backbone network, and extracting a hierarchical semantic feature set; s4, inputting the semantic feature set into three task branches of defect detection, region segmentation and type classification, and outputting a corresponding prediction result; s5, calculating a multi-task loss function, dynamically adjusting task branch weights, and optimizing a feature sharing structure; and S6, generating a detection report according to a prediction result, and outputting defect coordinates, a boundary contour, a type label and a confidence value. According to the method, multi-modal fusion, high-precision identification and structured output of the micro-drill blade surface are realized, and the accuracy, efficiency and automation level of defect detection are remarkably improved.

The invention discloses a remote sensing image anomaly detection system and method fusing multi-source noise feature anomaly in the technical field of remote sensing image detection. The method comprises the following steps: acquiring optical satellite remote sensing image data to be processed; analyzing the optical satellite remote sensing image data to obtain remote sensing image detailed information; performing exception type labeling on the detailed information of the remote sensing image, and constructing an exception feature data set; based on the abnormal feature data set, performing multi-source noise feature detection through a pre-designed abnormal detection fusion framework; and outputting an abnormal region obtained by the multi-source noise feature detection and an abnormal detection result. Through a fusion detection mode, the problems of color anomaly, cloud shadow masking, pixel information anomaly and the like of the remote sensing image are found in time, the universality of a remote sensing image anomaly image detection algorithm is improved, and the remote sensing image anomaly detection efficiency is further improved.

The invention relates to the technical field of image recognition, in particular to a harmful image detection and recognition system and method based on agent collaboration, and the system comprises a first agent module, a second agent module, a third agent module, a fourth agent module and a fifth agent module, the first agent module is used for analyzing image element semantic information, outputting structured data and constructing an image comprehensive semantic knowledge graph; and the second intelligent agent module is used for carrying out harmful feature detection and associated query on the image in combination with the structured data, and outputting a coarse-grained harmful semantic detection result and the confidence coefficient of the coarse-grained harmful semantic detection result. According to the harmful image detection and identification system based on intelligent agent cooperation, through hierarchical processing and cooperative work of the first to fifth intelligent agents, comprehensive analysis of the image is realized to obtain meta-semantic information, coarse-grained and fine-grained harmful semantic detection is performed on the meta-semantic information, and through multiple rounds of challenge and dynamic comprehensive analysis, the detection and identification efficiency of the harmful image is improved. Hidden harmful information can be accurately identified, so that the accuracy and interpretability of the harmful information are improved.