1926 results about "Vehicle 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 provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The invention discloses a self-adaptive illegal parking detection method, a detection system and a storage medium. The self-adaptive illegal parking detection method comprises the following steps: initializing the system; each video frame in an input video stream is processed according to the following steps: vehicle detection; performing regional filtration; performing multi-target tracking; vehicle state calculation: traversing each tracked vehicle, and updating the information of the tracked vehicle in the vehicle information object; multi-dimensional illegal parking judgment and alarm: according to a calculation result in the parking duration calculation step, executing the following judgment logics: traversing all static vehicles, determining a scene, obtaining a dynamic threshold value, triggering condition judgment and triggering an action; result visualization and output are carried out; and cleaning resources. By introducing the multi-dimensional dynamic judgment logic, the technical scheme of the invention can identify illegal parking behaviors more intelligently and more accurately, and the practicability and reliability of the system are significantly improved.

The invention relates to the technical field of unmanned aerial vehicle signal detection, and discloses an unmanned aerial vehicle radio signal cross-scene detection method based on transfer learning. According to the method, unmanned aerial vehicle radio signals in a target area are collected to generate original signal spectrum data, and then a plurality of signal detection scene categories are divided according to spectrum distribution characteristics. The method comprises the following steps: processing original data through a multi-scale feature extraction network, generating a signal feature space with cross-scene invariance, and inputting the signal feature space into a source domain channel and a target domain channel of a dual-channel residual transfer learning model; a feature alignment module in the model is used for dynamically compensating feature distribution difference between a source domain scene and a target domain scene, a cross-scene detection result is generated through a decision tree integration mechanism, and finally, based on the matching degree of the result and a preset signal feature library, an unmanned aerial vehicle identity label and a behavior type are output. The method can effectively cope with signal feature differences in different scenes, and improves the adaptability and accuracy of unmanned aerial vehicle detection.

The invention relates to the technical field of unmanned aerial vehicle detection and recognition, in particular to an unmanned aerial vehicle detection system and method based on vision, laser radar and sound waves, and the system comprises a sensor unit, a data processing fusion unit and a target recognition tracking unit. The data processing fusion unit performs multi-source data alignment and weighted fusion to extract texture, shape and color features of the image, point cloud generates a depth map, sound wave signals are mapped into a two-dimensional feature map, and weights are dynamically distributed based on sensor reliability to generate a unified feature map; the target identification and tracking unit identifies the type of the unmanned aerial vehicle by using the optimized deep learning model and realizes accurate prediction and updating of position and speed states in combination with Kalman filtering, and the decision response unit triggers sound-light alarm and wireless alarm for the non-cooperative unmanned aerial vehicle in real time and transmits target dynamic information to the ground station. And the target detection accuracy of the unmanned aerial vehicle in a complex environment is improved.

The invention discloses an unmanned aerial vehicle detection method based on multi-band radio spectrum analysis, and the method can recognize the flight control behaviors of a target, such as hovering, cruising or hovering, through the extraction of a signal track and the introduction of a behavior modeling mechanism, and effectively overcomes the defect that the prior art lacks the dynamic behavior analysis capability. A multi-node synchronization mechanism and a TDOA positioning method are combined, so that high-precision traceability of a target space position is realized; meanwhile, risk level assessment is carried out based on signal features, a track mode and a space region, so that the system has intelligent early warning capability. According to the whole scheme, a full-link unmanned aerial vehicle detection method from multi-frequency receiving, frequency spectrum identification, behavior judgment, target positioning to risk output is constructed, and the problems of single frequency band, rough identification means, lack of tracking and evaluation capability and the like in the prior art are effectively solved; and the practicability and the accuracy of the unmanned aerial vehicle detection system in a complex airspace environment are remarkably improved.

The invention relates to the technical field of intelligent traffic and energy-saving illumination, in particular to an intelligent management and control method and system for energy-saving illumination of a highway tunnel and a computer program, and is suitable for an intelligent illumination control system for improving tunnel driving safety and energy efficiency of an illumination system. Dynamic response and fine energy consumption adjustment of tunnel lighting are realized through out-of-tunnel brightness multi-source fusion prediction, entrance section feedforward, closed-loop dimming control, a segmented intelligent lighting strategy based on vehicle detection and an in-tunnel brightness closed-loop and stepless dimming mechanism. The system has a multi-sensor redundancy mechanism and can automatically return to a safety mode when equipment fails, so that the continuity and the safety of illumination are guaranteed; and meanwhile, data summarization and energy-saving statistics are combined, control parameters are optimized in cooperation with a digital twinning or self-learning algorithm, the energy-saving effect and the operation and maintenance efficiency are further improved, and the purposes of traffic safety and low-carbon operation are considered.

The invention discloses a multi-mode cooperative unmanned aerial vehicle highway illegal parking intelligent identification method and system. The system comprises a multi-mode sensing layer and a dynamic focusing control engine. The multi-mode sensing layer is connected with an unmanned aerial vehicle end, and the multi-mode sensing layer comprises a vehicle detection module, a lane semantic segmentation module and a vehicle tracking module; the vehicle detection module positions a vehicle target from an unmanned aerial vehicle cruise acquisition road video stream obtained from the unmanned aerial vehicle end; the lane semantic segmentation module can perform anti-distortion lane semantic perception; the vehicle tracking module establishes a motion model based on the target vehicle positioned by the vehicle detection module; the dynamic focusing control engine obtains a lane mask graph and a vehicle state according to the lane semantic segmentation module and the vehicle tracking module; and the dynamic focusing control engine is also used for performing illegal judgment and evidence generation and sending the illegal judgment and evidence generation to the traffic management law enforcement platform. According to the invention, precise identification and automatic law enforcement evidence collection of illegal parking targets in the highway scene are realized.

The invention provides an unmanned aerial vehicle target detection method based on DC GMA-YOLOv10 infrared and visible light fusion, and relates to the technical field of image detection. The method comprises the following steps: firstly, collecting and manufacturing infrared and visible light unmanned aerial vehicle image data sets of an unmanned aerial vehicle target in a complex environment; then, a DGM-YOLOv10 model is constructed, and the DGM-YOLOv10 model is trained according to the image data set; according to the DGM-YOLOv10 model, a feature extraction network of the YOLOv10 model is changed into two branches, and an improved group mixed attention module DC GMA is introduced to obtain a bimodal feature extraction network; a cross-modal differential perception fusion module CMDAF is introduced between the bimodal feature extraction networks; an information enhancement sampling module MSFS is introduced into the neck network; and on the basis of the trained DGM-YOLOv10 model, paired visible light and infrared unmanned aerial vehicle images are input for detection. According to the invention, based on the recognition of the convolutional neural network model, the robustness and performance of the unmanned aerial vehicle detection system can be improved.

In a traffic scene, a traditional target detection algorithm always faces the problems of strong background interference, difficulty in small target detection and the like, and detection precision and robustness are affected. Therefore, the invention provides an improved YOLOv12n vehicle detection method in which an EMA (Empirical Multi-scale Attention) attention mechanism and an SFA (Space Feature Aggregation) attention mechanism are fused. The invention further provides a method for detecting the YOLOv12n vehicle based on the improved YOLOv12n vehicle based on the attention mechanism of the EMA (Empirical Multi-scale Attention) and the attention mechanism of the SFA (Space Feature Aggregation). The SFA module is deployed in a shallow network, key target area expression is enhanced by aggregating spatial features, and background noise interference is suppressed; the EMA module is embedded into a neck network, and the global information capture and multi-scale sensing capabilities are improved by adopting multi-scale convolution, cross-space modeling and feature grouping mechanisms. According to the method, the real-time performance is kept, meanwhile, the detection precision in a complex scene is remarkably improved, and particularly, higher robustness is shown in the aspects of small target recognition and shielding processing.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

The invention relates to the field of digital image detection and signal processing, and particularly discloses an unmanned aerial vehicle tracking aerial target vehicle trajectory generation method, which comprises the following steps of: constructing a moving target detection neural network model, and performing stage processing on micro, medium and fast moving optical flow features on an input image by the model through a hierarchical cascade optical flow attention mechanism to obtain a moving target detection neural network model; motion processing of video frames is improved using bidirectional timing optical flow enhancement. Inputting a target vehicle video into a model to obtain a center coordinate of a target vehicle detection frame in each frame as a position coordinate of a vehicle, and connecting the position coordinates according to a time sequence to form a preliminary track; performing decoupling compensation of the motion of the unmanned aerial vehicle on the initial track through a multi-scale adaptive dense optical flow algorithm; performing coordinate transformation to obtain a roughly estimated trajectory of the trajectory after decoupling compensation in a geodetic coordinate system; and identifying an abnormal frequency through wavelet transform, removing noise points by using Lagrange interpolation, and de-noising by applying extended Kalman filtering to generate an accurate trajectory of the target vehicle.

The invention discloses a lightweight traffic vehicle detection method under the view angle of an unmanned aerial vehicle, and relates to the technical field of target detection, and the method is based on a YOLOv11 algorithm, and achieves the improvement of the vehicle detection performance through the following aspects: on the basis of maintaining a three-scale detection architecture, adding a high-resolution detection layer for a small target, more detail features are reserved; a shared lightweight detection head is adopted, so that the network parameter quantity and the calculation quantity are reduced; wavelet transform is used in the backbone and neck network to replace traditional convolution, and low-frequency and high-frequency components are used to extract multi-scale features; an RDS module is embedded in the C3K2 module to realize high-level feature sensing range expansion and deep and shallow feature fusion; in a target frame screening stage, a Soft-NMS algorithm is adopted to replace NMS, high-confidence prediction in an overlapped frame is reserved through a softening suppression strategy, and missing detection of small targets due to hard threshold filtering is avoided. The method is applied to vehicle detection under the view angle of the unmanned aerial vehicle.

A method and an apparatus for automatically recognizing an unmanned aerial vehicle, an electronic device, and a computer medium are provided. A specific implementation of the method includes: in response to determining that a radio detection device detects a radio signal, preprocessing the radio signal to generate preprocessed radio spectrum data; extracting radio signal features in the preprocessed radio spectrum data; inputting the radio signal features into a pre-trained unmanned aerial vehicle signal recognition model to obtain an unmanned aerial vehicle signal recognition result; in response to determining that the unmanned aerial vehicle signal recognition result represents an unmanned aerial vehicle signal, controlling a high-definition camera apparatus associated with the radio detection device to photograph an environmental video of a surrounding environment at a first time in real time; and generating unmanned aerial vehicle detection information corresponding to the first time according to a three-dimensional feature information group.

A method to prevent intoxicated operation is described. The method includes providing instructions to a vehicle user to position a vehicle user body portion posture in a predefined alignment. The method further includes obtaining the vehicle user body portion posture from a first vehicle detector, and determining whether the vehicle user body portion posture is in the predefined alignment. The method includes activating a plurality of vehicle visual indicators to illuminate in a predefined manner when the vehicle user body portion posture is in the predefined alignment. The method further includes providing instructions to the vehicle user to move vehicle user eyes to track the plurality of vehicle visual indicators, and obtaining a vehicle user eye movement from a second vehicle detector. The method further includes determining whether the vehicle user eye movement meets a predetermined condition, and actuating a control action accordingly.

The invention provides a multi-frequency adaptive unmanned aerial vehicle spectrum detection method and system, and the method comprises the steps: extracting the spatial and temporal distribution characteristics of carbon particle concentration and temperature gradient intensity based on a real-time environment data set, determining the attenuation coefficient distribution of different frequency band signals in a current environment, and obtaining a frequency band sensitivity analysis result; on the basis of a frequency band sensitivity analysis result, identifying a frequency band range which is most seriously influenced, and generating a signal data set which is optimized according to frequency bands; according to the signal data set optimized in different frequency bands, in combination with turbulence energy spectrum distribution and refractive index disturbance intensity, determining a random interference mode of a fire scene turbulence field on unmanned aerial vehicle detection signals and frequency dependence of the random interference mode; and adjusting the working frequency range of the spectrum detection equipment according to the frequency self-adaptive correction propagation path data set, and generating a multi-band collaborative optimization detection parameter set.

The invention discloses a vehicle detection and identification method and system based on laser sensing, and relates to the field of vehicle detection.The method comprises the steps that firstly, obstacle reflection signals are obtained through a multi-band laser array, preliminary classification is conducted through wavelength, time difference and intensity difference, and point cloud features are constructed; a lightweight network is adopted to filter noise points, and point cloud holes and shielding areas are intelligently complemented; then, performing hierarchical voxelization processing according to the obstacle distance, performing coarse-grained downsampling on a long-distance target, and keeping details on a short-distance target fine-grained; and finally, through a double-stage verification mechanism of a lightweight PointPill network and a three-dimensional convolutional network, precise grading detection and target reliability rechecking of far and near targets are realized, real-time performance and accuracy are considered, the problems of poor adaptability, low precision and insufficient dynamic scene adaptability of an existing laser radar detection technology are solved, and the method has the advantages of high detection precision and high reliability. According to the invention, high-precision and high-real-time vehicle detection and identification can be realized.

The invention discloses a road vehicle detection method based on high-frequency edge feature enhancement, which is characterized in that on the basis of a YOLOv11n model, an MSFE-C3k2 module is designed to replace a C3k2 module in a backbone network, and the perceptual ability of the model to target edge features is improved. An ASFPN structure is introduced into a Neck part, the multi-scale feature expression ability of the model is effectively enhanced, and the learning ability of small target vehicle features is further optimized in combination with a small target detection head. And a shared detection head SEHead is designed, so that the parameter quantity is effectively reduced while the detection precision is improved. And finally, a WMIoU loss function is proposed to replace the original CIoU, so that the bounding box regression precision is further improved. According to the method, the detection performance of the small target vehicle can be remarkably improved by strengthening the high-frequency edge features, and the regression precision of the model bounding box is optimized.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention relates to an AI identification road traffic safety illegal behavior image evidence obtaining method and device based on an unmanned aircraft, equipment and a medium, and the method comprises the steps: calling a no-parking region identification model, so as to determine a no-parking region mask in a to-be-inspected region image, calling a potential violation vehicle detection model to identify potential violation vehicles in the no-parking area mask so as to determine bounding boxes of the potential violation vehicles and vehicle types corresponding to the bounding boxes; and calling a target tracking algorithm to perform target tracking on the potential violation vehicle, and determining the potential violation vehicle as a target violation vehicle when detecting that the intersection-to-union ratio between the bounding box and the no-parking area mask in the plurality of continuous image frames exceeds a preset threshold value and the potential violation vehicle is in a static state. And sending the violation evidence packet corresponding to the target violation vehicle to a traffic management system. According to the invention, the efficiency of the monitoring system is improved, and accurate and verifiable violation evidences are provided for the traffic management system.

The invention discloses an unmanned aerial vehicle detection method based on radio frequency spectrum identification and deep learning, and relates to the technical field of unmanned aerial vehicle monitoring, and the method comprises the steps: deploying a radio receiving station to collect radio signals, converting the radio signals into a time-frequency graph, extracting multi-scale signal features, carrying out the global average pooling of the multi-scale signal features, and carrying out the global average pooling of the multi-scale signal features; calculating a scale adjustment coefficient to generate a weighted feature, extracting a time sequence signal feature to generate a fusion feature, constructing a full connection layer to perform multi-task processing on the fusion feature, and outputting an unmanned aerial vehicle detection result; a state space is defined, an action space is defined according to environmental parameters, nonlinear harmonic response characteristics in a current state are measured to construct a nonlinear reward function, a comprehensive reward function is formed in combination with action interference effects, and a final interference strategy is output through reinforcement learning. According to the invention, the scientificity and robustness of the identification and interference strategy are significantly improved, the accuracy of the interference strategy is improved, and the interference effect of the unmanned aerial vehicle is effectively improved.

The invention relates to a robust unmanned aerial vehicle detection method based on dynamic feature fusion and context attention, and belongs to the technical field of image processing. Aiming at the problems of small target feature loss, semantic gap, background noise interference and the like caused by a fixed convolution kernel scale, one-way feature fusion and a static attention mechanism in an existing unmanned aerial vehicle aerial image target detection method, the method comprises the following steps: constructing a detection model comprising a backbone network, a neck network and a detection head network; a feature rearrangement and extraction module is designed in the backbone network to enhance feature learning, an enhanced double-flow feature fusion pyramid is designed in the neck network to optimize multi-scale feature fusion, and a dynamic multi-scale context attention mechanism is designed in the detection head network to suppress irrelevant background noise. The method effectively improves the accuracy and robustness of small target detection, and achieves a clearer and more stable detection effect in a complex environment.

The invention relates to the technical field of data processing, in particular to a booster station vehicle detection system which comprises an environment perception optimization module, a joint denoising module, a data fusion module, a threshold value correction module and a closed loop feedback module. The environment perception optimization module generates an anti-interference node deployment map through a genetic algorithm spatial layout model, and optimizes view field overlapping parameters of the laser radar and the ultrasonic sensor; a joint denoising module calls a genetic optimization filter coefficient based on the electromagnetic resonance frequency band mark vector, and executes time-frequency domain joint filtering processing; and the closed-loop feedback module constructs an interference source fingerprint characteristic spectrum and an evolvable alarm rule set, and reversely optimizes sensor layout parameters and decision logic through disposal effect data. And the data circulation control module synchronizes genetic algorithm parameters and state data among multiple modules, so that full-link collaborative optimization is realized, a false alarm phenomenon under a dynamic working condition is effectively inhibited, and the detection precision and the anti-interference capability are improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle detection tracking device, method and equipment, which comprises a data sensing unit, a calculation unit, a mechanical and power supply unit, a communication and storage unit and a countering execution unit, and is characterized in that the data sensing unit is used for acquiring image data and servo turntable rotation angle data; the calculation unit is used for deploying a lightweight neural network model and a multi-algorithm fusion process, processing sensor data and realizing unmanned aerial vehicle target detection and tracking logic control; the mechanical and power supply unit is used for providing physical support, servo motion capability and energy supply for the device; the communication and storage unit is used for real-time data transmission and storage and encryption archiving of historical data and image videos; and the countering execution unit is used for implementing signal interference or physical interception on the detected and tracked target. Therefore, the problems of easy target loss, low small target identification precision, poor complex environment adaptability, insufficient countering means and the like of a traditional device are solved.

The invention provides an unmanned aerial vehicle multi-scale fusion detection method based on bimodal inconsistency suppression, and relates to the technical field of image detection, and the method comprises the steps: collecting a visible light image and an infrared image of a complex environment, carrying out the screening, registration and labeling of the collected visible light image and infrared image of the complex environment, and constructing a cross-modal data set; the method comprises the following steps: constructing a BiFPN-CALNet model, wherein the BiFPN-CALNet model comprises a double-flow backbone network, a CALNet network, a weighted bidirectional feature pyramid network BiFPN and a detection head; the cross-modal data set is used for training the BiFPN-CALNet model, and the trained BiFPN-CALNet model is obtained; and based on the trained BiFPN-CALNet model, inputting a visible light image and an infrared image of a to-be-detected unmanned aerial vehicle to obtain a target detection result. By means of cross-modal conflict correction and multi-scale feature transfer, more accurate target detection can be realized in a complex environment, and an efficient and reliable solution is provided for an unmanned aerial vehicle detection task.

The invention relates to the technical field of unmanned aerial vehicle detection, and discloses a common-aperture unmanned aerial vehicle detection method and system, and the method comprises the steps: obtaining multi-sensor original data and a state data set, and carrying out the standardization; in combination with the state data set, performing state updating and de-noising processing; if data missing or abnormal fluctuation is detected, filling and removing and time sequence storage are carried out, and a historical track sequence is formed; performing feature calculation to obtain a motion feature vector; classifying by using a support vector machine to obtain a target motion mode classification label; if the trajectory is an evasive type or an aggressive type, triggering a high-priority tracking process, and training by using a long-short-term memory network to obtain a predicted trajectory coordinate point set; unifying the coordinates to a global reference framework to obtain a global prediction trajectory, and calculating an intersection point of the global prediction trajectory and a preset no-fly zone to obtain an intersection detection result; generating a real-time alarm signal; and performing comprehensive threat assessment to obtain a threat target detection conclusion. According to the method, the threat assessment accuracy can be improved.

The invention relates to the technical field of digital image processing, in particular to an unmanned aerial vehicle image distortion correction and coordinate positioning compensation method, which comprises the following steps: collecting a large amount of unmanned aerial vehicle image data containing a damaged house, labeling the damaged house in the image, and generating a labeling file required by a YOLOv5 model; performing parameter calibration on the unmanned aerial vehicle lens to obtain a distortion coefficient of the lens, and constructing a nonlinear distortion correction model based on a lens parameter calibration result; establishing a dynamic affine transformation matrix in combination with the real-time flight attitude data of the unmanned aerial vehicle and the pixel coordinates of the detection frame; in a YOLOv5 detection stage, fusing feature maps with different resolutions, and fusing corrected coordinate data with a damaged house detection frame output by a YOLOv5 model; calculating coordinates of a center point of the damaged house according to the real-time latitude and longitude of the unmanned aerial vehicle; through dual optimization of image distortion correction and dynamic positioning compensation, the problem of coordinate misalignment caused by hardware limitation and unstable flight attitude in traditional unmanned aerial vehicle detection is solved.

The invention discloses an unmanned aerial vehicle detection countering method, system and equipment and a medium, and the method comprises the steps: obtaining a task instruction and sensing signals of a plurality of sensing devices in a monitoring region in real time, carrying out the preprocessing of the task instruction and the sensing signals, and obtaining the standardized modal data; performing feature extraction and high-level semantic extraction on the task instruction and the standardized modal data corresponding to each sensing signal in parallel to obtain a task context parameter and a high-dimensional feature vector of each sensing signal; fusing the high-dimensional feature vectors based on the task context parameters by using a space-time alignment multi-modal fusion algorithm to obtain multi-modal fusion features; using a neural network model to predict and generate a flight trajectory of the unmanned aerial vehicle in a future time period according to the historical trajectory data and the multi-modal fusion features; and performing threat level judgment and behavior anomaly detection based on the task context parameters and the flight trajectory, generating alarm information, and executing a security countering strategy generated based on the alarm information.

The invention discloses a multi-spectral image fusion building surface biological attachment area detection method and device and a medium, relates to the technical field of image processing, and discloses a multi-spectral image fusion building surface biological attachment area detection method comprising the following steps: based on a to-be-detected building, obtaining a visible light image collected by a visible light camera of an unmanned aerial vehicle, the multispectral camera acquires a multispectral image sequence based on at least two different frequency bands; registering the visible light image and the multispectral image based on a calibration and preprocessing module to obtain a pixel-level aligned visible light image and multispectral image sequence; and according to a deep learning feature identification module, identifying the visible light image and the multispectral image sequence after pixel-level alignment, and obtaining a pixel-level segmentation result of the organism attachment area of the to-be-detected building. Therefore, detection is carried out based on the unmanned aerial vehicle, deep learning and an image feature fusion mode are combined, and the building surface bioattachment recognition accuracy is improved.

The invention relates to the technical field of machine vision, in particular to an automobile detection system and method based on machine vision, and the system comprises an image processing module, an automobile body feature extraction module, a defect positioning module, a defect analysis module, a decision support module and a detection evaluation module. According to the method, the U-Net model is combined with the conditional random field, various vehicle body defects such as scratches and recesses can be finely recognized and accurately positioned, the recognition capability of different defect types is enhanced by extracting complex geometric and textural features from the image, the defect classification is more accurate, and the accuracy of vehicle body defect classification is improved. The defect boundary is continuously optimized, the detection parameters are automatically adjusted, the reliability of decision support is improved, the repair process is more targeted and efficient, human errors are reduced through automatic detection and parameter adjustment, the operation cost is reduced, and potential maintenance cost and risks caused by defect omission are reduced.