1159 results about "Vehicle detection" patented technology

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 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 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 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 provides a pedestrian and vehicle detection method and device based on multi-scale perception fusion, and the method comprises the steps: building a multi-scale perception fusion network structure which comprises an SPDConv module, a PSSF module, an FCPAM module and a DyHead module, solving a detection error caused by the problems of target shielding, size change, far-small proportion and the like in a complex traffic environment, and improving the detection precision. And the stability, the precision and the deployability of the detection model are improved.

The invention relates to an abnormal unmanned aerial vehicle detection method fusing internal anomaly detection and external environment adaptation, which adopts a three-layer collaborative architecture design and comprises an external environment perception module, an internal anomaly detection module and a virtual-real fusion digital twinning module. And the external environment sensing module is responsible for external environment interference identification, regularly acquiring data of each unmanned aerial vehicle in the unmanned aerial vehicle group, and realizing stability discrimination. Environmental stability judgment and task switching are regarded as conditions of entering a buffer state, and are used for internal anomaly detection of the unmanned aerial vehicle group. In an internal anomaly detection module, real-time operation data of multiple unmanned aerial vehicles are analyzed based on a data flow driving mechanism, and abnormal individuals are identified. And the virtual-real fusion digital twinning updating module is responsible for constructing a high-fidelity digital twinning virtual space and dynamically updating internal and external constraint information to a digital twinning platform to support the next decision. The collaborative fusion of the three modules breaks through the limitation of a single constraint view angle, and realizes the collaborative constraint of internal state monitoring and external environment perception.

The invention relates to the technical field of unmanned aerial vehicle radio detection, in particular to an unmanned aerial vehicle detection and judgment method based on cooperation of an air-ground system and radio detection, and the method comprises the steps: finding a suspected target through scanning of a multi-frequency-band radar, triggering an infrared thermal imager carried by an aerial unmanned aerial vehicle to approach an ultra-wide-frequency-band spectrum sensor for verification; accurate positioning is realized by combining ground photoelectric equipment multi-view triangulation positioning and a signal arrival time difference algorithm, a command and control center calculates threat levels through a multi-source data fusion engine, and navigation decoy of dynamically adjusting intensity is adopted for targets smaller than or equal to two levels. Hard killing of ground laser locking rotor center and air electromagnetic pulse synchronous coverage is executed on a target of more than or equal to three levels, the equipment state is synchronized in real time through an optical fiber link, high-energy-consumption equipment is intelligently closed after threat is relieved, a platform starting strategy is optimized, a detection closed loop of'three-dimensional perception-graded treatment-intelligent energy saving 'is constructed, and the intelligent energy saving effect is achieved. And an efficient and accurate low-altitude safety protection scheme is provided for a sensitive area.

The invention discloses a port forbidden area vehicle illegal parking monitoring method based on transformer and time sequence detection, and the method comprises the steps: carrying out the video collection and preprocessing, carrying out the target detection of each frame of image of a video through a detection model constructed based on a deep convolutional neural network, and carrying out the target detection through a multi-target tracking algorithm, and performing ID distribution and trajectory tracking on the same vehicle in the video sequence, analyzing the motion state of the vehicle in combination with a time sequence detection model, identifying whether a forbidden zone staying or illegal parking behavior occurs, and judging whether the vehicle is illegal. According to the scheme, CNN and Transform attention mechanisms are combined, the detection precision of vehicles in a complex port scene is improved, a multi-target tracking algorithm is utilized in vehicle trajectory matching, high-precision matching of a vehicle detection frame and a tracking trajectory is achieved in combination with a Hungary algorithm, a time sequence detection model is utilized to detect the vehicle, and the vehicle trajectory matching precision is improved. The system can recognize behavior changes of illegal parking vehicles in the time dimension, automation of the whole illegal parking judgment and warning process is achieved, and the safety response efficiency is improved.

The invention provides a comprehensive photoelectric platform method and system for unmanned aerial vehicle detection, electronic equipment and a storage medium, and relates to the technical field of unmanned aerial vehicle detection and localization. Synchronously triggering a photoelectric platform to capture a suspicious airspace high-definition video stream and establishing a space-time corresponding database of the two; synchronizing each radio frequency receiving node by taking a high-precision atomic clock as a time reference, and processing multiple paths of signal data to obtain a time-aligned basic signal; basic signal radio frequency features are extracted and associated with equipment identification, a mapping relation between the basic signal radio frequency features and remote controller models is established, and a hardware feature library is constructed; and finally, in combination with the library identification result, the signal time difference and the optical data, correcting a multipath error by using particle filtering, predicting and updating the position of an operator, and realizing comprehensive detection and positioning of the unmanned aerial vehicle after iterative convergence, thereby realizing comprehensive detection and positioning of the unmanned aerial vehicle in combination with radio frequency detection and optical observation.

The invention discloses an unmanned aerial vehicle multi-source data fusion unmanned aerial vehicle management method and system, and the method comprises the steps: obtaining original monitoring data from a plurality of unmanned aerial vehicle detection devices, carrying out the time-space standardization processing of the heterogeneous data, building a unified time-space reference, building an unmanned aerial vehicle mapping relation through target correlation matching, and carrying out the target correlation matching. And performing multi-source data fusion on the standardized monitoring data after association mapping, finally performing airspace supervision analysis based on the generated unmanned aerial vehicle comprehensive state data, and outputting an airspace supervision decision report including monitoring information, an authentication state and an early warning prompt. According to the invention, through cooperative sensing and fusion of multi-source data, the problems of sensing blind areas, insufficient data reliability and the like existing in single detection equipment are effectively solved, the all-time accurate supervision capability of cooperative and non-cooperative unmanned aerial vehicles is remarkably improved, and reliable technical support is provided for airspace safety management.

The invention discloses a sequence-based vehicle type category correction method and device, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: obtaining a vehicle detection model, and collecting a backflow video data set; traversing the set, detecting each frame of vehicle target by using the model, and associating cross-frame targets by using a target tracking technology to obtain a vehicle trajectory and form a plurality of vehicle target sequences; traversing the sequence to count vehicle types, and correcting the vehicle types according to a statistical result to obtain a plurality of corrected vehicle target sequences; identifying a missing detection frame based on an original sequence, predicting a missing detection target position, and obtaining a plurality of missing detection data sets; and iteratively updating the vehicle detection model in combination with the correction sequence, the missing detection data and the original sequence. According to the invention, the technical problems of vehicle type judgment errors and target detection deficiency due to the fact that multi-frame associated information is not combined in a traffic scene in traditional vehicle detection are solved, and the technical effects of improving the vehicle type detection accuracy and integrity and realizing continuous optimization of the detection capability are achieved.

The invention relates to the technical field of low-altitude unmanned aerial vehicle detection, in particular to an unmanned aerial vehicle detection method based on radar infrared visible light multi-mode fusion, which comprises the following steps: S1, respectively inputting an obtained unmanned aerial vehicle infrared image and an obtained visible light image into two visual branches of a double-flow feature extraction network, and extracting scale features by adopting a lightweight convolutional backbone network; s2, acquiring three-dimensional coordinate data of the unmanned aerial vehicle after radar detection and clutter removal processing in real time, and projecting the three-dimensional coordinate data into two-dimensional image coordinates through a camera external parameter matrix; s3, generating a two-dimensional Gaussian distribution probability graph based on the real-time two-dimensional image coordinates, and inputting the two-dimensional Gaussian distribution probability graph as a mask into the radar feature extraction network to extract a radar feature graph; and S4, under the same scale, performing dynamic weighted fusion on the scale features of the infrared branches and the visible light branches to generate a visual fusion feature map. The method greatly improves the capability of detecting the low-altitude unmanned aerial vehicle.

The invention provides an unmanned aerial vehicle detection method, system and product based on radio fusion vision. The method comprises the following steps: respectively extracting radio characteristics of a target radio signal and image characteristics of a target visual image; carrying out feature fusion on the radio features and the image features to obtain a fusion feature vector; inputting the fusion feature vector into a trained unmanned aerial vehicle detection model for target detection, and obtaining a target detection frame and unmanned aerial vehicle category information; and inputting the target detection frame and the unmanned aerial vehicle category information into a dynamic weight distribution model for weighted correction to obtain a final unmanned aerial vehicle detection result. Compared with a traditional method, the method has the advantages that through radio and visual feature fusion, complex electromagnetic interference and severe environment influences such as strong light, backlight and haze can be resisted, the signal confusion problem of single radio detection is avoided, the imaging limitation of single visual detection is made up, the multi-scene detection requirement can be effectively met, and the detection precision and reliability are improved.

The invention discloses an unmanned aerial vehicle detection and directional interference system based on an artificial intelligence technology, which belongs to the technical field of unmanned aerial vehicle detection interference and comprises a data acquisition module, an unmanned aerial vehicle identification module, a geographic information correction module, a trajectory prediction module and a directional interference module. The data acquisition module is used for acquiring geographic information data of a target area based on a geographic information system, comprises a radar, an acoustic sensor and a radio frequency spectrum detection sensor, and is used for acquiring feature data of a moving object in the target area; and the unmanned aerial vehicle identification module obtains feature data of a moving object in the target area based on the data acquisition module. According to the unmanned aerial vehicle detection and directional interference system based on the artificial intelligence technology, the obstacle in the target area is identified, the personalized behavior of the unmanned aerial vehicle is analyzed, the trajectory prediction and interference strategy of the unmanned aerial vehicle is optimized in combination with topographic features, and the trajectory prediction precision and the directional interference effect are improved.

The invention relates to the technical field of unmanned aerial vehicle detection and computer vision, in particular to an application method of a multi-scene throwing object recognition FOD algorithm carried by an unmanned aerial vehicle, and the method comprises the steps: dynamically fusing data through a multispectral camera and a laser radar, carrying out the scene self-adaptive defogging and illumination equalization preprocessing, inputting an improved twin network-FPN model, and extracting features; then, a target frame is screened by adopting a DIOU-NMS containing a scene complexity coefficient; and three-dimensional positioning is realized by combining RTK-GPS and a laser radar, the data is transmitted to a ground terminal through 5G and is subjected to secondary verification, and the models can be dynamically switched according to the flight height. The method solves the problems of missing and false detection, poor environmental adaptability and the like of complex scene FOD recognition, data integrity is improved through multi-sensor fusion, robustness is enhanced through scene self-adaption, the model and dynamic strategy balance precision and real-time performance are improved, multi-scene adaptability is high, and the recognition effective rate exceeds 98%.

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to a civil small unmanned aerial vehicle cat eye detection and countering system which comprises the steps that a cat eye detection module collects cat eye reflection characteristic signals, environment sensing data and unmanned aerial vehicle flight state associated information in an airspace; the threat level evaluation center identifies the type of the unmanned aerial vehicle and the intrusion risk level through an improved random forest model, and measures the real-time airspace position; the dynamic trajectory modeling module constructs a digital twin flight model of the civil small unmanned aerial vehicle, and simulates a flight trajectory and a potential intrusion path by using an intention pre-judgment algorithm; the grading countering unit generates a non-destructive countering strategy according to the intrusion risk grade, the flight path and the potential intrusion path; the man-machine interaction unit constructs a visual airspace monitoring scene, renders a detection result, an intrusion risk level and a countering effect in real time, and carries out man-machine interaction operation. Therefore, the problems of insufficient perception, poor decision collaboration and the like in the prior art are solved.

The utility model discloses an Internet of Things tunnel interval controller combined with a digital microwave radar, and the controller comprises a radar module which is used for obtaining traffic flow information in a non-contact manner; the control module is used for executing logic judgment and generating a control instruction according to the traffic flow information acquired by the radar module; the data storage module is used as a nonvolatile storage space of the Internet of Things tunnel interval controller; the radio frequency communication module is used for realizing networking and remote management through an Internet of Things communication protocol; the power supply module is used as a power supply basis of the Internet of Things tunnel interval controller, and the power supply module is connected with the radar module, the control module, the data storage module and the radio frequency communication module; a tunnel interval control solution with high integration level, accurate vehicle detection, intelligent dimming decision, flexible wireless communication and high-reliability data management is jointly realized.

The invention discloses a traffic jam detection and early warning method based on an unmanned aerial vehicle video stream, and belongs to the technical field of intelligent traffic. The system is combined with a floating vehicle triggering model, collects road videos in real time through an unmanned aerial vehicle, and realizes high-precision vehicle detection and tracking in combination with improved YOLOv8 and DeepSort algorithms; unmanned aerial vehicle data and floating vehicle GPS data are fused, three elements of traffic flow are quantified, and a dual-mechanism congestion detection model is constructed; respectively utilizing a GRU cooperative traffic wave theory to predict the congestion dissipation duration and utilizing an improved GRU + GCN model to predict the traffic situation; the signal lamps are dynamically regulated and controlled through multi-agent reinforcement learning, and active congestion relieving is achieved. The method has the advantages of wide-area coverage, high-precision perception and intelligent response, and the urban traffic control efficiency is remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle detection, and discloses a simulation deduction method and system based on low-altitude airspace multi-source fusion, and aims to realize quasi-synchronous acquisition, format standardization processing and feature-level intelligent fusion of heterogeneous data and solve the contradiction between timeliness and consistency through a strategy of edge collaborative fusion and cloud unified modeling. By constructing a low-altitude airspace digital twinning system, high-precision and high-timeliness three-dimensional space expression capability is realized; setting an analogy threshold according to the historical conflict data, and classifying the future trajectory sequence of the aircraft; in combination with the three-dimensional grid model, abnormal behaviors are pre-judged, conflict risks are pre-warned in advance, scheduling suggestions are generated, a dynamic three-dimensional digital airspace model is finally constructed, and the safety guarantee and management scheduling requirements in a complex low-altitude traffic scene are met.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle countering method and system, and the method comprises the steps: carrying out the unmanned aerial vehicle detection of a corresponding detection region based on an unmanned aerial vehicle tracking device; in response to the detected unmanned aerial vehicle, laser emission parameters of a laser module are adjusted based on the detected distance between the unmanned aerial vehicle and the unmanned aerial vehicle tracking device; and the laser module after adjustment of the laser emission parameters emits laser to interfere with the unmanned aerial vehicle detected by the unmanned aerial vehicle tracking device. According to the scheme, the accuracy and the safety of countering the unmanned aerial vehicle through the laser are improved.

The invention discloses an audio-visual fusion unmanned aerial vehicle detection and positioning method, and belongs to the technical field of unmanned aerial vehicle detection and positioning, and the method comprises the steps: collecting sound signals of a detection region through a sound array, obtaining the local strongest direction of the sound signals, and forming an unmanned aerial vehicle candidate direction set; signals of candidate orientations of all unmanned aerial vehicles are directionally received, whether the unmanned aerial vehicles exist in the candidate orientations or not is judged through the unmanned aerial vehicle sound signal anomaly detector based on deep learning and the unmanned aerial vehicle image target detector based on deep learning, and an unmanned aerial vehicle orientation set is obtained; acquiring an image of a detection area through a binocular camera to obtain a depth image of a reference camera visual angle; uniquely corresponding the orientation of the unmanned aerial vehicle to the determined position of the depth image, and taking the depth value of the position as the distance of the unmanned aerial vehicle; and combining the unmanned aerial vehicle azimuth and the unmanned aerial vehicle distance to obtain three-dimensional coordinates of the unmanned aerial vehicle. The unmanned aerial vehicle can be identified and positioned by adopting an audio-visual fusion method.

The invention relates to a visual scale adaptive unmanned aerial vehicle detection method based on HRRP physical perception, and belongs to the technical field of mobile communication. The method comprises the following steps: constructing a cooperative monitoring architecture of an ISAC base station and a visual sensor, and establishing a space-time mapping relation between a radio frequency sensing coordinate system and a visual sensor coordinate system; performing macroscopic parameter estimation based on echo signals received by the ISAC base station, resolving spatial position coordinates of a target, generating a servo control instruction to drive a visual sensor holder to rotate, and locking the target at the center of a visual image field of view; extracting a frequency domain channel response of the echo signal, generating a time domain HRRP through IDFT, and estimating a radial physical span of a target in combination with a constant false alarm detection algorithm; constructing a self-adaptive image slice model based on physical structure constraint, and performing resampling on slices by using a scale normalization scaling factor; and inputting the normalized image block into the feature fusion detection network to complete fine detection of the unmanned aerial vehicle target.

Systems and techniques for inspecting vehicles, such as cars or trucks. Some embodiments provide a method for inspecting a vehicle using a vehicle inspection system. The vehicle inspection system may include a vehicle exterior inspection system having a plurality of exterior sensor arrays, the plurality of exterior sensor arrays including respective sets of sensors oriented in different directions. The vehicle inspection system may include a vehicle undercarriage inspection system having an undercarriage sensor array including cameras configured to capture images at different angles, and a sensor configured to output vehicle position / motion data. In some embodiments, the method includes: moving the vehicle and / or vehicle inspection system; triggering capture, by the exterior sensor arrays and undercarriage sensor array, of sensor data about the vehicle; capturing the sensor data; stopping capture of the sensor data; stopping movement of the vehicle and / or vehicle inspection system; and generating a vehicle condition report.

The invention discloses an unmanned aerial vehicle detection method and system based on generative multi-modal fusion. The method comprises the following steps: acquiring an RGB image of an unmanned aerial vehicle; inputting the RGB image of the unmanned aerial vehicle into a pre-trained infrared image generation model, and generating a corresponding virtual infrared thermal induction image; respectively inputting the unmanned aerial vehicle RGB image and the virtual infrared thermal induction image into a target detection model, carrying out modal specific feature extraction, modal implicit alignment processing and cross-modal feature fusion, and carrying out detection through a detection head and repeated training to obtain a final target detection model; and obtaining a to-be-detected unmanned aerial vehicle RGB image, sequentially inputting the to-be-detected unmanned aerial vehicle RGB image into the pre-trained infrared image generation model and the final target detection model for detection, and obtaining a prediction category and a prediction bounding box coordinate of the unmanned aerial vehicle. According to the method, a conventional RGB image is used as input, and a corresponding infrared thermal induction image is automatically generated. According to the design, extra infrared hardware is not needed, the remote false detection rate is greatly reduced, and all-weather detection is achieved.

The invention relates to a fire area inversion method based on airborne dual-spectrum detection and depth estimation, and belongs to the field of unmanned aerial vehicle detection. The method comprises the following steps: acquiring a multi-dimensional data set of a dual-spectrum image, a temperature image, an unmanned aerial vehicle attitude and the like; constructing a multi-modal space collaborative perception segmentation network, combining temperature change characteristics with temperature space distribution characteristics of flames to generate temperature region distribution characteristics, and coupling absolute temperature and pixel information to enhance flame weak edge extraction; designing a temperature-guided space structure loss function TSSLoss, and combining gradient change consistency constraint and temperature weight constraint on a segmentation loss function for network training; and according to the unmanned aerial vehicle pose, the target depth and the fire area segmentation pixel area, an early fire area is derived in combination with an airspace transmission inversion formula, and an actual fire area is calculated. According to the method, multi-source information is effectively combined for physical constraint, and more accurate fire detection segmentation and fire area calculation can be realized.

The invention discloses an over-height vehicle management and control method based on traffic signal linkage, and the method comprises the following steps: setting an over-height vehicle detection section in a first lane group where an over-height vehicle is located, installing an over-height vehicle detection facility, carrying out the recognition of the over-height vehicle, and judging whether an over-height vehicle forbidding event is triggered or not; when an over-high vehicle forbidding event occurs, a first lane group signal lamp is regulated and controlled, and a normal state is switched to a first emergency state; regulating and controlling a second lane group signal lamp conflicting with the bypass dispersion of the ultrahigh vehicle, switching to a second emergency state, synchronously regulating and controlling the first lane group signal lamp, and switching to a third emergency state; and regulating and controlling the first lane group signal lamp and the second lane group signal lamp at the end of the detouring guidance, and switching to a normal state. The invention further discloses an over-height vehicle management and control system based on traffic signal linkage. The invention solves the technical problem that the prior art cannot actively control the vehicles intruding into the height-limited road section.

The invention relates to the technical field of unmanned aerial vehicle detection and security and protection, and discloses a low-altitude unmanned aerial vehicle dual-mode detection method based on micro Doppler radar and infrared thermal imaging. According to the method, a monitoring area is continuously scanned through a micro-Doppler radar, and when a suspected target with the signal intensity lower than a preset threshold value is recognized, an infrared thermal imaging device is triggered to be started. The infrared device directly adjusts the orientation and focuses to a specific area according to the target orientation and distance information provided by the radar, and obtains a high-resolution thermal imaging sequence. And then the radiation temperature distribution, the temperature gradient and the thermal profile morphological characteristics of the target are analyzed, and final judgment is completed. According to the invention, on-demand starting and rapid and accurate guiding of the infrared sensor are realized, the overall power consumption of the system is reduced, and the cooperative detection response speed and the identification precision of the rapid moving target are improved at the same time.

A vehicle detection and collision alerting device and method is described. The vehicle detection device is used by a user to detect an approaching vehicle. The vehicle detection device may include a housing and a mount attached to the housing for securing the housing to the user or the users equipment. An optical sensor secured to the housing, facing generally outward. A processor is in communication with the optical sensor to receive data and execute an algorithm to analyze the received data. This algorithm is configured to determine the size and distance of the approaching vehicle and also calculate its velocity and trajectory. Alerting modules to alert the user and to alert the approaching vehicle are configured to receive signals from the processor when a potential collision is identified based on the data being processed.