251 results about "Adaptiv" patented technology

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention relates to the technical field of building structure health monitoring, in particular to a building defect detection method based on infrared thermal imaging and visible light bimodal fusion and a matched intelligent imaging device. According to the method, infrared thermal imaging and visible light bimodal image fusion are combined, and an improved YOLOv8m-seg model is combined, so that accurate detection of the defects of the outer wall of the building is realized. Firstly, a bimodal data set containing multi-material temperature difference data is constructed, ORB feature matching is combined with a feature alignment module (FAM) to achieve accurate image alignment, the defect detection robustness is improved through an improved bimodal feature fusion network, and finally automatic recognition and quantitative analysis of cracks, fractures and other defects are achieved. The problems that single-mode detection is interfered by dirt and is poor in multi-material adaptability are solved, the detection accuracy rate reaches 96% or above, the recall rate exceeds 93%, the single image detection time is shorter than or equal to 0.3 s, and the method is suitable for efficient detection of various building outer wall defects.

The invention discloses a poultry behavior abnormity real-time monitoring system based on multi-modal image fusion, particularly relates to the technical field of intelligent breeding behavior recognition, and is used for solving the problem of poor behavior monitoring accuracy under feather shielding. The method comprises the following steps: firstly, through combined perception of a visible light image and an infrared image, extracting a claw track interruption point and an anus temperature gradient direction, and realizing analysis of a motion state of a sheltered area; then, in combination with the heat conduction delay characteristic and the group movement direction, the flexion and extension angle of the covered leg joint is inverted, and a complete gait sequence is generated; thirdly, multi-source features such as gaits, temperature differences and body postures are fused, and a dynamic deviation model of the individuals relative to the mass center of the group is constructed; and finally, generating a stress behavior threshold curve according to the ground temperature and the ammonia gas concentration, outputting an abnormal behavior type and confidence, and realizing intelligent distinguishing of mechanical obstacles and adaptive behaviors.

The invention discloses a single-frame infrared thermal imaging building outer wall hollowing detection method and system based on heat flow evaluation. The method comprises the following steps: acquiring a single-frame infrared thermal image and a visible light image which are synchronously acquired; generating an infrared thermal imaging sequence based on the single-frame infrared thermal image and the Green function of the heat conduction control equation; performing time-space frequency domain transformation and column harmonic function inversion on the infrared thermal imaging sequence, and inversely transforming back to a time-space domain to generate a virtual heat flow image sequence; sequentially carrying out binarization processing and discrimination function enhancement processing on the single-frame virtual heat flow image to obtain an outer wall enhanced binarization image; identifying an interferent from the visible light image by adopting a target detection network; and carrying out registration and mapping on an identification result in the visible light image and the infrared thermal image, and removing interferents in the outer wall enhanced binarization image to obtain a pure outer wall binarization image. The method has the advantages of high environmental adaptability, high recognition precision and the like, and is suitable for safety detection and maintenance of building exterior walls.

The invention provides a power adapter appearance quality detection method and system based on machine vision, and particularly relates to the technical field of power adapter appearance quality detection.The method comprises the steps that a main control module controls a light source module to output an illumination condition matched with a surface material of a power adapter, and an industrial camera is triggered to collect a surface image; preprocessing the image to generate preprocessed image data; inputting the preprocessed image data into a defect identification model to extract defect features and generate a defect classification result; if the classification result contains the reflective interference mark, obtaining material information through an infrared sensor, adjusting light source parameters, and re-collecting and processing the image; otherwise, generating a quality detection signal according to a classification result, and transmitting the quality detection signal to a classification execution mechanism to separate the defective power adapter. According to the method, self-adaptive high-precision detection on a low-cost embedded hardware platform is realized, and the efficiency and the adaptability are improved.

The invention relates to the technical field of perimeter intrusion monitoring, in particular to a perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm. The perimeter intrusion monitoring system based on infrared thermal imaging and intelligent algorithm cooperation comprises a multi-source sensing layer, an edge computing node, a cloud analysis platform and a response execution layer. According to the perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm, environmental noise interference is effectively suppressed through a dynamic threshold compensation mechanism, and accurate classification of personnel, vehicles, unmanned aerial vehicles and animals is realized by combining multi-modal feature fusion of infrared thermal imaging, visible light vision and millimeter wave radar; according to the method, intention modes of passing, observation, invasion and the like can be identified, threat level dynamic adaptation is realized in combination with a hierarchical response system, the technical bottlenecks of poor environmental adaptability, high target misjudgment rate, behavior analysis deficiency and the like of a traditional system are solved, and the intelligent level and all-weather protection capability of perimeter security and protection are remarkably improved.

The invention discloses an unmanned aerial vehicle and bird target identification method and system based on multiple modes, and belongs to the technical field of computer vision and target identification. The method comprises the steps of collecting an RGB image, an infrared image and a continuous frame image of a monitoring area, extracting visual features, thermal radiation features and motion features after preprocessing, fusing multi-modal features by adopting an adaptive weight fusion strategy, detecting a potential target through a multi-scale feature fusion detection head, and obtaining a target detection result. The multi-mode classification module judges the target category and triggers early warning; the system comprises a data acquisition module, a preprocessing module, a feature extraction and identification module, a decision and early warning module and a database management module. Through multi-modal fusion, a self-adaptive weight strategy, multi-scale detection and multi-branch classification, the method can effectively overcome the limitation of a single modal, improves the recognition accuracy, the small target detection capability and the environmental adaptability in a complex environment, and meets the requirements of real-time recognition and early warning.

The invention relates to an RGB-T saliency target detection method based on a query guide specific learning network, and the method comprises the steps: constructing a network model of an encoder-decoder architecture, and setting a visible light image saliency detection module, an infrared image saliency detection module and a bimodal saliency detection module according to the network model, visual features of the visible light RGB image are obtained through an encoder of the visible light image saliency detection module, and infrared features of the infrared thermal imaging image are obtained through the infrared image saliency detection module; in a bimodal information fusion module in the bimodal saliency detection module, generating an RGB modal feature, an infrared modal feature and a cross-modal fusion feature; and the three-feature cross block outputs the enhanced multi-modal fusion features. Compared with the prior art, the method has the advantages of high accuracy, high modal adaptability, high efficiency and the like.

The invention relates to a camouflage design method and system based on an adversarial sample, and aims to solve the problem that a current camouflage pattern is difficult to adapt to a multi-terrain background camouflage demand, and the method comprises the steps: constructing a training data set; carrying out adversarial camouflage texture initialization by adopting a multi-scale adaptive disturbance weighted Voronoi graph; constructing a camouflage texture generation model based on the confrontation camouflage texture initial value; generating a camouflage target image in the physical scene based on the target mask image in combination with the original image and the camouflage texture output by the camouflage texture generation model, calculating an adversarial loss function through the adversarial image, and obtaining the camouflage color with the optimal effect by iteratively training the camouflage texture generation model; the method can effectively interfere with the recognition capability of the AI detection model, is suitable for the stealth technology, privacy protection, security and other scenes, and remarkably improves the efficiency and adaptability of the camouflage technology.

The invention discloses a multi-modal data fusion-based DC insulator degradation diagnosis method and system. The method comprises the following steps of obtaining infrared image data and current data of a to-be-detected insulator string; constructing an insulator chain model according to the infrared image data, and obtaining the temperature of each insulator according to the insulator chain model and the infrared image data; obtaining the theoretical temperature of each insulator according to the current data; and according to the theoretical temperature of the insulator and the temperature of the insulator, degradation identification of the insulator is realized. Obtaining historical maintenance data of the insulator, constructing a self-adaptive threshold according to the historical maintenance data of the insulator, and performing secondary judgment on a degradation identification result through the self-adaptive threshold; according to the method, the problems of diagnosis one-sidedness, poor environmental adaptability, low degradation positioning precision and the like caused by dependence on a single data source in a traditional method are effectively solved, and the accuracy, robustness and engineering practicability of direct current insulator degradation diagnosis are remarkably improved.

The invention relates to the technical field of radar data processing, in particular to a method which is suitable for a multi-external radiation source and multi-station system, carries out single-frame and two-frame multi-target measurement value matching under the condition of no bistatic distance information, has good adaptability to the multi-target positioning problem of external radiation source systems of different systems, and is high in positioning accuracy. The bistatic joint multi-target positioning method based on the external radiation source system is beneficial to the processes of multi-target positioning, real-time tracking, track generation and the like, and comprises the following steps: matching multi-source and multi-station angle measurement values to obtain candidate association, and estimating the intersection point position and the speed state of the angle measurement values; approximately constructing a linear equation of bistatic speed and angle measurement value errors, establishing measurement value error estimation of candidate associations and a real target, and removing wrong candidate associations by using an association hypothesis decision; and establishing a cost function according to the measured value error estimation of the candidate association between the two frames and the state mahalanobis distance, and removing the error candidate association between the two frames by using an association hypothesis decision.

The invention provides a system device for intelligently sorting coal gangue. The system device comprises a feeding unit, a machine vision recognition unit, a main control unit and a sorting execution unit which are connected in sequence, the feeding unit comprises a vibrating feeder and a refining and shaping mechanism; the machine vision identification unit comprises a high-speed industrial camera and a near infrared spectrum camera; a coal gangue recognition model based on deep learning is arranged in the main control unit; the sorting execution unit comprises a plurality of high-pressure electromagnetic air valve nozzles which are evenly distributed in the transverse direction of the conveying belt. The vibration feeding mechanism and the material uniformizing and shaping mechanism are combined, so that single-layer uniform distribution of materials is ensured, and overlapping and shielding of the materials are effectively avoided; machine vision and near infrared spectrum multi-modal information fusion is adopted, and a deep learning algorithm is combined, so that the accuracy and adaptability of coal gangue recognition are remarkably improved; and through the time delay compensation and accurate positioning technology, it is ensured that the sorting execution mechanism accurately hits the target, and the sorting efficiency is improved.

The invention relates to the technical field of intelligent inspection of power equipment, and particularly discloses a power transmission line inspection system based on an autonomous correction long-endurance unmanned airship. The system aims to solve the problems of short endurance, small operation radius, single image angle, intermittent operation, leak detection, low precision, poor environmental adaptability and the like caused by dependence on manpower or GPS navigation in the conventional unmanned aerial vehicle inspection. The system comprises an energy supply module, a data acquisition module, an intelligent control module and a communication and data return module. Wherein the energy supply module is integrated with a solar energy collection, storage and intelligent scheduling unit to realize day-and-night continuous energy supply; the intelligent control module adopts an RT-DETR visual algorithm to identify a tower in real time, performs automatic deviation correction by fusing pose information, and accurately tracks a preset track; the data acquisition module supports multi-angle high-definition imaging, and the communication module guarantees real-time data return. And high-precision unmanned inspection with ultra-long endurance, full-automatic and wide-area continuous coverage is integrally realized.

The invention relates to the field of kiln early warning, and discloses an abnormal behavior detection early warning system in real-time kiln video monitoring, which is used for realizing a leap-type innovation of kiln anomaly detection from single threshold alarm to intelligent hierarchical control. Three-dimensional temperature field data of the surface of a steel pipe is collected through an infrared thermal imager arranged in an annular array, a multi-spectral video stream of a slab stacking area is synchronously obtained in combination with a visible light camera, and a vibration sensor array is deployed to collect roller way vibration phase signals. And environment radiation compensation is carried out based on the Stefan-Boltzmann law, and surface cracks and internal slag inclusion defects are identified through a Kirchhoff law deviation threshold. According to the scheme, through multi-modal data space-time synchronous coding, dynamic weight adjustment and gradient template library self-adaptive correction, the anomaly detection precision and the working condition adaptability are remarkably improved.

The invention discloses an electric power inspection system and method based on image fusion and an improved target detection algorithm, and relates to the technical field of electric power inspection, the system comprises a double-spectrum data acquisition module, an image registration module, an image fusion module, a target detection module and a human-computer interaction interface which are connected in sequence; the double-spectrum data acquisition module acquires an infrared image and a visible light image of the power equipment; the image registration module carries out registration processing by adopting a CAO-C2F method; the image fusion module performs feature extraction, shallow detail fusion, deep semantic fusion and scene reconstruction on the registered dual-spectrum image to obtain a fusion image fusing infrared features and visible light features; the target detection module adopts an improved target detection algorithm to carry out target detection; and the human-computer interaction interface displays the defect detection result of the power equipment and the coordinate information of the target position in real time. The environment adaptability and universality of power inspection can be improved, and the precision and efficiency of power inspection are improved.

The invention belongs to the technical field of artificial intelligence, particularly relates to the technical field of reinforcement learning, and particularly relates to an unmanned system autonomy evaluation method based on reinforcement learning, which can be applied to intelligent unmanned aerial vehicle manufacturing, and comprises the following steps: constructing an unmanned aerial vehicle autonomy evaluation index system; constructing an unmanned system autonomy scoring model based on reinforcement learning; and training an autonomous scoring model of the unmanned system. According to the method, a three-level index system taking the autonomy of the unmanned aerial vehicle as a root node is established, a double neural network structure based on reinforcement learning is designed, and an autonomy score is calculated. A multi-head attention mechanism is embedded in the value network, and adaptive learning of each leaf index weight is realized. According to the method, the importance weights of different evaluation dimensions are dynamically adjusted, and accurate and adaptive autonomy scores are provided. According to the method, the problems of fixed weight and lack of adaptability in a traditional evaluation method are solved, and the evaluation strategy can be dynamically optimized according to actual task requirements.

The invention relates to a system architecture for high-precision situation awareness of an unmanned ship. Three detection combinations of a Ku wave band detection radar, a laser radar and a visible light / infrared camera are used for covering all-weather detection data requirements under different distance ranges and environment conditions. The data fusion and monitoring system receives detection data and longitude and latitude, speed and vertical and horizontal shaking data of a ship transmitted by an external satellite compass in real time, time synchronization is carried out by using timestamps of the satellite compass, and space alignment is completed by calibrating installation positions of different detection loads. According to detection ranges, environmental adaptability and action characteristics of different detection loads, two or three kinds of detection combination data are subjected to data fusion regionally, a high-precision and multi-source complementary sensor system architecture is used for achieving high-precision situation awareness of the periphery of the unmanned ship in a complex environment, and support is provided for a collision avoidance algorithm of follow-up unmanned navigation; a data fusion algorithm is used to eliminate redundancy and contradiction possibly existing among multi-sensor information, and the uncertainty is reduced.

The invention relates to a transformer substation bird three-dimensional positioning method and system based on double thermal infrared imagers, and belongs to the technical field of transformer substation safety monitoring. The system comprises a data acquisition module, a background heat source filtering module, a three-dimensional coordinate fusion module, a deep learning recognition module, a multi-modal meteorological compensation module, a trajectory prediction and early warning module and a data output and linkage control module. The method comprises the following steps: acquiring data cooperatively through double thermal infrared imagers, accurately identifying a bird target by using a deep learning model after background filtering and temperature threshold preliminary screening, calculating a three-dimensional space position of the bird target through coordinate fusion, and dynamically optimizing detection parameters in combination with real-time meteorological data. And finally, an improved Kalman filtering algorithm is adopted to predict a bird movement track, and early warning and linkage with a bird repelling device are realized. The problems of inaccurate positioning, poor environmental adaptability, early warning lag and the like in the prior art are solved, and the accuracy, the reliability and the active defense capability of bird monitoring of the transformer substation are improved.

The invention relates to the technical field of image processing, in particular to a flame detection method based on a multi-modal fusion image, which comprises the following steps: acquiring a visible light near-infrared thermal infrared image and matching positions, extracting multi-channel difference to generate a feature set, combining adjacent blocks to judge a direction and a brightness trend, and identifying a temperature rise gathering area. And combining the three types of images to confirm flame characteristics and position a detection result. According to the invention, by collecting visible light, near-infrared and thermal infrared images, cross-modal accurate alignment is realized by using interpolation, and the fusion effect is optimized. The flame area is accurately extracted according to the pixel deviation characteristics, and false alarm and missing alarm are reduced. The positioning precision is improved by combining block boundary tracking and temperature change analysis, and a flame target is effectively recognized in a complex environment. The multi-mode complementation enhances the adaptability of the system, and ensures that the system has high stability and robustness in a dynamic interference environment.

The invention relates to a passive multiband sensing array system and a multi-target tracking method. The system comprises a multiband sensor group, an environment illumination sensor, a synchronization and control unit and a preprocessing unit, the multi-band sensor group comprises a visible light sensor, a near-infrared sensor and a long-wave infrared sensor which are integrated in a unified shell through a coaxial light path and do not comprise active emitters; the synchronization and control unit generates a synchronization signal and controls the multi-band sensor group and the environment illumination sensor to perform synchronous data acquisition; and the preprocessing unit receives the original image data, the environment illumination data and the synchronizing signal, outputs a structured multi-mode data packet and provides the structured multi-mode data packet to the multi-target tracking engine at the rear end, and the multi-target tracking engine outputs the tracking states of multiple targets. According to the invention, stable, continuous and high-precision tracking of a plurality of targets can be realized in a plurality of complex scenes, and the problems of poor environmental adaptability, dependence on active emission and insufficient robustness of the existing multi-target tracking scheme are effectively solved.

The invention belongs to the technical field of building engineering intelligent detection, and particularly relates to an unmanned aerial vehicle-based fabricated building defect automatic identification system, which comprises a data acquisition module, a data registration module, a sleeve positioning module, a feature extraction module, a defect identification module and a report output module. A sequential three-dimensional data field is constructed, environmental interference influences are eliminated through preprocessing, visible light image features, infrared thermal imaging features and three-dimensional point cloud geometric features are extracted in parallel, internal and external comprehensive diagnosis of the grouting full state is achieved, grouting insufficient cavities, later-stage contractile cavities and grouting vertical depth deficiency defects are accurately recognized, and the grouting quality is improved. Dynamic tracking, accurate quantification and traceability are synchronously carried out, the problems of single dimension, poor space-time continuity, weak environmental adaptability, difficulty in accurate quantification and traceability of defects and the like of existing prefabricated building sleeve grouting fullness state detection are effectively solved, and the intelligent reliability of quality control is improved.

The invention discloses an on-orbit automatic focusing method for a spaceborne space target observation camera, relates to the technical field of space application of spaceborne cameras, and solves the problems of poor real-time performance and insufficient unattended operation adaptability of a manual focusing technology in the existing focusing technology. And meanwhile, in the automatic focusing technology, the hardware structure is complex, the space environment adaptability is weak, satellite-borne platform resources are limited and difficult to deploy, and focusing is difficult in a severe out-of-focus scene. According to the invention, the realization of automatic focusing is divided into a coarse adjustment stage and a fine adjustment stage. A large step pitch is adopted in the coarse tuning stage, rapid crossing is achieved, and an area adjacent to a positive focus position is rapidly positioned; in the fine adjustment stage, small step pitch is adopted, fine adjustment is achieved, and the peak position is accurately locked. The contradiction between the search speed and the positioning precision is balanced. When far away from the positive focus, a large-step-pitch fast approximation peak region is used, so that the number of iterations and the number of image acquisition frames are greatly reduced; when the focus is approached, small step pitch is switched for fine tuning, and the final precision is ensured.

The invention provides a photovoltaic power equipment fault detection method and system based on an unmanned aerial vehicle, and belongs to the technical field of photovoltaic operation and maintenance and image intelligent recognition. The method comprises the following steps: carrying out aerial photography on a photovoltaic power module through an unmanned aerial vehicle to obtain an infrared image; the collected image is preprocessed to enhance fault features, and labeling processing is carried out; carrying out model training by adopting an improved Faster R-CNN algorithm in combination with transfer learning; deploying the trained model to an edge calculation unit of the unmanned aerial vehicle; finally, the real-time detection of the fault of the photovoltaic module is realized. According to the method, the hot spot defect in the photovoltaic equipment can be effectively identified, the multi-fault target detection precision is improved, the method has high environmental adaptability and deployment flexibility, and powerful technical support can be provided for intelligent operation and maintenance of a photovoltaic power system and an unmanned aerial vehicle edge computing platform.

The invention discloses a multi-band multi-polarization SAR (synthetic aperture radar) image fusion classification method for modal missing and non-registration scenes. The method comprises the following steps: step 1, data preprocessing; step 2, feature extraction; step 3, carrying out cross-modal constraint; step 4, carrying out modal Dropout; 5, performing feature fusion; and step 6, performing classification decision. According to the method, spatial alignment or resampling does not need to be carried out on the original image, semantic consistency and modal fusion robustness among different frequency bands can be effectively improved, high-reliability and high-adaptability polarized SAR image intelligent classification in a complex remote sensing scene is realized under the condition that the frequency bands are incomplete or the resolutions are inconsistent, and the method is suitable for popularization and application. The method solves the problem that the classification precision of the existing multi-band multi-polarization SAR image is reduced under the conditions of inconsistent image size, incapability of aligning frequency bands, mode loss and the like, and is suitable for various remote sensing application scenes such as disaster monitoring, land coverage identification, military reconnaissance and the like.

The invention relates to the field of pavement crack detection, in particular to a pavement crack detection device and method based on machine vision, and the device comprises a multi-modal data acquisition module, a lighting unit, a central processing unit, a display module, a storage module, a positioning and communication unit, a power supply module and a supporting vibration reduction structure. The multi-modal data acquisition module comprises a high-definition RGB camera module, a binocular stereoscopic vision module and an infrared thermal imaging module; the detection method comprises the following steps of S01, data acquisition and preprocessing, S02, multi-modal fusion and input preparation, S03, crack identification processing, S04, post-processing and parameter extraction, and S05, result output and storage. The problems that in the prior art, crack detection is poor in adaptability, insufficient in real-time performance, high in false detection and omission ratio, incomplete in quantitative measurement and the like are solved.

The invention discloses an infrared target detection method based on a bidirectional receptive field attention feature network, which is realized by constructing a model comprising a feature coding network, a feature fusion network and a detection head, and improves the detection precision and model generalization ability of an infrared remote sensing image, especially a small target. A receiving field attention convolution module is introduced into the feature coding network, different spatial positions in a receptive field are independently weighted, fine-grained feature extraction is enhanced, and the problem that multi-scale target feature extraction is insufficient is solved; further, the feature coding network optimizes feature extraction and fusion operations by alternately stacking RFAConv modules and standard convolution modules, so that key features of a target are reserved and enhanced to the greatest extent, and the problem of poor adaptability of a complex background is solved; the feature fusion network adopts a bidirectional feature pyramid network, high-level semantics and low-level detail information are bidirectionally and efficiently fused, and the problem of information loss in the fusion process is solved.

The invention discloses an optical system of an infrared thermal imager, and the system comprises a super-structure lens module which employs a silicon-based super-structure lens, and achieves 3-5 [mu] m and 8-12 [mu] m multi-band focusing through a single sheet; the free-form surface lens is matched with the super-structure lens to correct asymmetric aberration; the multi-view-field switching mechanism is used for driving a zoom group through a micro stepping motor to realize switching between a wide angle and a narrow angle; the active athermalization compensation unit comprises an MEMS driver and a temperature sensor, and the lens spacing is dynamically adjusted through closed-loop control; and the intelligent processing module is integrated with an embedded FPGA processor and operates a deconvolution algorithm to suppress diffraction stray light at the edge of the super-structure lens. According to the optical system of the infrared thermal imager, the problems that a traditional infrared optical system is large in size, high in weight and poor in environmental adaptability are solved through the innovative optical design and manufacturing process, and efficient and long-time patrol flight detection of the unmanned aerial vehicle-mounted infrared thermal imager is achieved.

The invention provides an obstacle warning method and device, electronic equipment and a storage medium. The method comprises the steps that sensor data, environment information and vehicle speed information are acquired after a vehicle is started; processing the sensor data based on the vehicle speed information and the environment information to obtain target data; according to the target data, detecting whether an obstacle appears in a preset area range of the vehicle; according to the first radar data and the image data, whether a transparent obstacle appears in a preset area range of the vehicle is detected; and when the target obstacle is detected, outputting target alarm information matched with the target obstacle. According to the invention, by fusing the data of the vision, the ultrasonic radar and the millimeter-wave radar and combining dynamic optimization processing of the vehicle speed and the environment information, the obstacles in the preset area of the vehicle are accurately identified, and the matched graded alarm information is output, so that the reliability and comprehensiveness of obstacle detection in a complex scene are improved; and the safety and scene adaptability of vehicle driving are enhanced.

The invention relates to the technical field of intelligent defect detection, in particular to a strain clamp defect detection method and system based on unmanned aerial vehicle digital X-rays. The method comprises the steps of obtaining target equipment information, and obtaining a detection strategy parameter according to the target equipment information; an imaging control parameter is generated by acquiring spatial positioning data and combining detection strategy parameters, a detection area is divided into a plurality of scanning subareas, and a special detection angle, hovering time and an image overlapping rate are set for each subarea; the defect detection result is subjected to confidence analysis, the detection confidence is calculated, when the detection confidence is lower than an acceptable range, a detection optimization adjustment instruction can be automatically triggered, self-adaptive adjustment is executed according to compensation control information, and detection parameters and a detection process are dynamically optimized; and the detection precision, reliability and adaptability are improved.

The invention relates to the technical field of photoelectric detection and radars, in particular to a calibration method, system and device for a radar and a laser anti-collision device and a medium, and aims to track and lock the same dynamic non-cooperative target through the laser anti-collision device and the phased array radar. Synchronously acquiring spherical coordinate observation data of the dynamic targets in the public view field under respective coordinate systems; and after the spherical coordinates are converted into rectangular coordinates, calculating a point set centroid, constructing a covariance matrix and performing singular value decomposition based on a rigid body transformation model, and directly solving a rotation matrix and a translation vector from a radar coordinate system to a laser anti-equipment-free coordinate system, thereby completing calibration. No external absolute coordinate reference is needed, only relative observation data of equipment is utilized, and the method has the advantages of being rapid in deployment, high in environmental adaptability, low in cost and high in calibration speed. The problems that in the prior art, a calibration system depends on RTK or a high-precision map, the environmental adaptability is poor, and the deployment requirement is high are solved.