331 results about "Camera array" patented technology

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention relates to the technical field of panoramic image construction, in particular to a panoramic image reconstruction method and system based on multi-angle imaging. The method comprises the following steps: collecting a multi-angle original image based on a distributed multi-camera array, carrying out adaptive filtering denoising and adaptive panoramic imaging adjustment, and constructing a multi-angle imaging geometric constraint network; performing multi-view semantic information deviation elimination based on a multi-angle imaging geometric constraint network, and performing global semantic feature fusion to obtain a unified semantic space representation framework; identifying illumination feature information of different visual angles, performing multi-angle illumination corresponding compensation on the multi-angle original image, performing image semantic distortion correction based on a unified semantic space representation framework, and constructing a multi-angle illumination compensation image; and performing multi-scale texture structure analysis on the multi-angle illumination compensation image to generate a high-fidelity texture fusion image. According to the invention, a natural and seamless panoramic image is provided, a panoramic scene is perfectly presented, and the immersive visual experience of a user is improved.

The invention relates to the technical field of image compensation, in particular to a reflection compensation method based on image processing, and provides the following scheme: acquiring an environment panoramic image by using a panoramic camera to establish a scene global coordinate system, and acquiring a multi-view original image in combination with a camera array arranged in the circumferential direction of an object; determining a rotation symmetry axis according to the multi-view contour features, reconstructing a three-dimensional geometric body, and calculating normal distribution and curvature change; generating a prediction image based on diffuse reflection and specular reflection hypothesis in the surface expansion domain through virtual visual angle disturbance, identifying a reflection region according to color and gradient consistency, and generating a reflection mask; and performing texture reconstruction on the reflective area by using geometric registration and multi-view compensation, and finally performing splicing and fusion to obtain a non-reflective high-fidelity panoramic image. The method does not need to change the field illumination condition, and can achieve the precise recognition and compensation of the complex curved surface reflection in the cultural relic in-situ collection environment.

The invention relates to the technical field of artificial intelligence and computer vision, discloses a subway key component fault detection method and system based on an AI visual large model, and aims to solve the problems of low detection precision, weak generalization ability, insufficient multi-mode understanding, poor real-time performance and lack of state evolution modeling in the prior art. The method comprises the following steps: acquiring images of key components through a multi-view industrial camera array, and performing distortion correction, illumination normalization and noise suppression; a pre-trained visual large model is utilized to extract deep space features, and modeling is carried out on a continuous frame feature sequence through bidirectional LSTM to capture a time sequence change trend. By introducing the large-scale visual large model and spatio-temporal joint modeling, the identification capability of tiny defects is improved, the discrimination stability is enhanced, high-precision and low-delay automatic detection is realized, the false alarm rate and the omission ratio are remarkably reduced, and the detection efficiency and the system maintainability are improved.

The invention relates to the technical field of rock mechanical tests and safety monitoring, and discloses a rock fracture prediction method based on acoustic emission and surface strain cooperative monitoring, and the method comprises the steps: building a monitoring system comprising acoustic emission and an industrial camera array, and building a unified timestamp of each subsystem through a GPS time service module; through load triggering logic, a synchronous trigger is utilized to synchronously acquire an acoustic emission signal and a sample surface image sequence, and evolution characteristics of three-dimensional coordinates of an acoustic emission source and surface full-field strain data are solved. And on the basis, calculating a main strain field variation coefficient, and performing cross-correlation analysis on the resampled energy rate and strain rate. And finally, according to multi-parameter coupling criteria of acoustic emission time sequence parameters, positioning events and surface strain, identifying rock fracture precursor types, and predicting fracture moments and areas by combining a Voight model inverse velocity method and a seismic source projection technology.

The invention relates to a six-eye camera panoramic video real-time splicing method and system and computer equipment, and the method comprises the steps: synchronously carrying out the image collection through a six-eye camera array, and adding a timestamp to each video frame image; performing real-time feature extraction on the video frame image, and establishing a feature matching relationship in a view field overlapping region of adjacent cameras; based on the feature matching relationship, mapping each video frame image to a unified splicing coordinate system through projection transformation; performing splicing processing on the video frame images after projection transformation through multi-band fusion, and eliminating splicing traces at the boundary of the field of view; and reconstructing the panoramic frame images obtained after splicing processing according to a time sequence, and outputting a continuous panoramic video stream so as to realize real-time and seamless splicing and correction of six paths of high-definition video streams and finally output a global panoramic video stream with no visual fracture and consistent time and space.

The invention provides a robot-based blade interior autonomous crawling sensing method and system, and the method comprises the steps: carrying a multi-sensor array on a robot, the multi-sensor array comprises a binocular fisheye camera array and a ToF sensor array, controlling the robot to start from a path travel starting point, obtaining a panoramic depth image based on the multi-sensor array, and transmitting the panoramic depth image to the robot; recognizing an obstacle based on the panoramic depth image, constructing a three-dimensional point cloud map based on the obstacle, avoiding the obstacle based on the three-dimensional point cloud map, generating a feasible path in the blade, and enabling the robot to move based on the feasible path until the robot reaches a path travel terminal point or a minimum space in which the robot cannot pass. Autonomous crawling perception of the robot in the blade can be ensured, movement of the robot in the blade is ensured, and detection is realized.

The invention relates to the technical field of circuit board detection, in particular to a quality detection system of an integrated circuit board, which comprises an image acquisition module used for cooperatively acquiring a surface image and a temperature distribution image of the integrated circuit board through a camera array and a multispectral light source; the preprocessing module is used for carrying out noise suppression, distortion correction and multispectral fusion on the surface image and the temperature distribution image and generating a standardized detection image; the defect detection module is used for extracting welding spot contour features, wire connectivity features and dielectric layer thickness features from the standardized detection image, the welding spot image and the inner layer wiring image of the integrated circuit board can be separated, interlayer interference is eliminated, multi-dimensional more accurate evaluation of defect risks is achieved, and the defect detection accuracy is improved. Misjudgment caused by traditional isolated analysis is avoided, the irradiation angle and intensity of the multispectral light source can be dynamically adjusted, and the reliability and efficiency of quality detection of the integrated circuit board are improved.

The invention provides a panoramic image processing method and device suitable for an unmanned aerial vehicle and a panoramic unmanned aerial vehicle. The method comprises the following steps: acquiring multi-view images in real time through a multi-camera array of an unmanned aerial vehicle, acquiring pose data by using an inertial sensor, converting each view image into a first projection image and a second projection image, and performing dynamic target detection and recognition on the second projection image to obtain a dynamic target, geometric scene features and target distribution. Differentiated processing areas are divided, specifically, a high-precision algorithm is adopted for the first processing area, a low-precision simplified algorithm is adopted for the second processing area, and computing power focusing and processing capacity optimization are achieved. In the splicing stage, a dynamic target historical trajectory optimization splicing algorithm is combined, and processing strategies are adapted to different areas, so that the quality of the key areas is guaranteed, and the process of the non-key areas is simplified to reduce the calculation load. According to the scheme, the problem of real-time panoramic splicing under the condition that unmanned aerial vehicle hardware resources are limited is effectively solved, and the processing real-time performance and efficiency are remarkably improved.

The invention relates to an intelligent oil containment boom mechanical arm device and a control method thereof. The intelligent oil containment boom mechanical arm device comprises a mechanical arm, a hydraulic driving system, a sensing system and a control unit. The mechanical arm comprises a transmission base, a steering stand column, a large arm, a small arm, an overturning head and a tail end grabbing hook assembly. The hydraulic driving system comprises a hydraulic pump station, a proportional reversing electromagnetic valve group and a plurality of execution oil cylinders; the sensing system comprises a gyroscope array and a camera array to form a distributed attitude sensing network and a distributed visual sensing network; the control unit identifies and positions an oil containment boom target based on a sea surface image acquired by the distributed visual perception network, performs motion planning on the mechanical arm, and controls the mechanical arm to move and grab the target; in the target positioning process, posture changes of all parts of the mechanical arm are sensed in real time based on the distributed posture sensing network, and dynamic compensation is conducted on the posture error of the mechanical arm. According to the device, the oil containment boom can be quickly, accurately, reliably and intelligently grabbed, and the laying efficiency and safety of the oil containment boom are improved.

Provided are a camera-array-based intelligent measurement and control method and system for an ultra-high bridge tower (1). The system is provided with a camera array monitoring module, an image capture and processing module, a monitoring apparatus, a bridge tower (1) status evaluation and prediction module, and a bridge tower (1) intelligent control and early warning module. Targets (3) are vertically arranged on a bridge tower (1), and an array camera monitors the real-time displacement of each target (3) across the full height range of the ultra-high bridge tower (1); the monitoring apparatus acquires environmental parameters and stress data; a bridge tower (1) structure status model is established in the bridge tower (1) status evaluation and prediction module to predict the future alignment change trend of the bridge tower (1); and the bridge tower intelligent control and early warning module performs real-time early warning and control on the bridge tower (1). Thus, all-weather automatic monitoring and intelligent control of the alignment of the structure of a bridge tower (1) are realized, thereby effectively improving the measurement efficiency, reducing the labor costs, ensuring the accuracy and reliability of measurement results, and thus enhancing the construction quality of the bridge tower (1).

The invention relates to the technical field of ceramic intelligent manufacturing, in particular to a ceramic firing process thermal deformation intelligent prediction and self-adaptive temperature precise regulation and control system, which comprises a multi-source sensing module (a thermal infrared camera array, a temperature sensor, a laser displacement meter and a powder characteristic analyzer); a hybrid prediction model (a space-time convolutional neural network constrained by a physical mechanism); and the self-adaptive temperature control actuator comprises a partitioned burner group, a quenching / slow cooling pipeline and a gas-air volume decoupling controller. According to the scheme, through a multi-mode perception-hybrid modeling-dynamic optimization full-process closed-loop architecture, the single-point technical limitation of existing patents is broken through, and the zero-defect precise manufacturing target of ceramic firing is achieved.

The invention discloses a precise mark-free positioning system applied to an oral cavity. A camera array composed of a plurality of cameras which are annularly or specifically arranged is arranged on a mobile phone; an infrared imaging assembly for solving the problem of cooling water interference in the operation is designed on at least one camera; the processor is configured to integrate the acquired data including preoperative CBCT (cone beam computed tomography), oral scanning data and digital data of occlusal relationship to construct a complete preoperative oral three-dimensional model; establishing a spatial relationship between a visual system formed by registration of the turbine handpiece and the camera array and a preoperative oral cavity three-dimensional model, and obtaining adaptive relative poses among the parts; after the body position of a patient is fixed during an operation, a complete oral environment point cloud is formed for each frame of input data currently collected by the visual system, and the complete oral environment point cloud is fitted with a patient oral model obtained through oral scanning and CT scanning in advance, so that the oral position of the patient is positioned in real time.

A ship sub-assembly weld seam identification method, comprising: creating a point cloud weld seam identification training dataset by means of point cloud discretization, virtual sampling, weld-zone semi-automatic interactive annotation, and style transfer-based augmentation of ship sub-assembly CAD data; training a point cloud weld seam identification model, and exporting a weld seam identification model data file; using a 3D point cloud camera array as a sampling camera to collect workpiece 3D point cloud data of a sub-assembly workpiece to be welded at the production site; by means of the weld seam identification model data file, obtaining weld seam annotation position data and positioning a weld seam; and issuing a welding process plan and instruction to control a welding robotic arm and a control device therefor to complete sub-assembly workpiece welding. Also provided are a ship sub-assembly weld seam identification system, a computer device, and a computer-readable storage medium. The method overcomes the shortcomings in conventional 3D weld seam identification methods such as point cloud incompleteness and identification failures caused by steel types and the imaging angles of 3D cameras, thereby greatly improving the adaptability of automatic weld seam identification methods to complex workpieces.

The invention relates to the field of textile production, and discloses an intelligent control method for a textile finished product production line, which comprises the following steps: acquiring a textile surface image through a camera array to obtain textile surface image data; performing fiber arrangement density analysis on the textile surface image data to obtain fiber arrangement density difference data; according to the yarn gap distribution characteristic extraction method and device, environmental factors are monitored and compensated in real time through the temperature and humidity correlation deformation correction coefficient, it is ensured that under different environmental conditions, fabric tension is kept stable, the product quality and the production stability are further improved, and the yarn gap distribution characteristic data are obtained. An equipment cooperative control model constructed by using a depth deterministic strategy gradient algorithm realizes real-time speed adjustment and tension compensation of the textile machinery group through edge computing nodes, and the collaboration and automation level of the production process are effectively improved.

The invention belongs to the technical field of road tunnel illumination, and particularly relates to a control method and system for adjusting road tunnel illumination based on dynamic traffic, which comprises the following steps: firstly, acquiring vehicle information through a high-definition camera array, a microwave radar detector and an illumination intensity sensor in a multi-source manner, then preprocessing the multi-source data, and applying an algorithm to adjust the illumination of a road tunnel; traffic flow statistics and trend prediction, vehicle driving state analysis and vehicle type accurate classification statistics are realized; based on this, the traffic flow is counted and predicted in real time through the dynamic traffic information acquisition and analysis module, the illumination brightness reference value is set and corrected based on the traffic flow by combining the multi-dimensional illumination parameter intelligent decision module, and then the illumination execution and dynamic adjustment module accurately executes the brightness instruction. The method has the advantages that the illumination brightness is dynamically matched with the traffic flow, the traffic safety requirement during high traffic flow is met, and energy waste during low traffic flow is avoided.

The invention provides an isomorphic sensor dexterous hand-tactile glove system and a data acquisition method, and belongs to the technical field of tactile sensing, the system comprises a tactile data acquisition module which comprises a glove main body and a fixed flange arranged on the glove main body, a tactile sensor array is arranged on the palm side of the fixed flange and used for collecting tactile data of interaction between the glove body and an object; the visual perception module comprises an infrared camera array and an RGB (Red, Green and Blue) camera array, and is used for tracking marking balls arranged on the fixed flanges so as to solve pose data of the fixed flanges and collecting RGB images of interaction between the glove main body and an object; and the multi-modal data processing module is used for receiving the tactile data, the pose data and the RGB image, realizing space-time alignment of the data and generating a standardized data set. The system is simple in structure, rapid in deployment and high in acquisition safety, and can be suitable for tactile big data acquisition under multi-scene and multi-task conditions.

The invention discloses a bolt surface defect detection method and system based on computer vision. The method comprises the steps that 360-degree image collection and preprocessing are conducted on a bolt through an annularly-arranged multi-camera array; fourier transform is adopted to analyze thread periodic texture features, and an adaptive filter is designed to enhance the image; multi-scale morphological operation is adopted, and morphological gradient features are extracted by using structural elements for crack, pit and scratch type defects; a self-adaptive threshold segmentation method based on local statistical characteristics is combined with thread direction information to dynamically adjust a segmentation threshold, and defect candidate areas are segmented; and extracting geometric and textural features of the candidate region, and identifying the type, position and severity of the defect by adopting a random forest classifier. According to the invention, high-precision automatic detection and classification of various defects on the surface of the bolt are realized.

The invention relates to the technical field of intelligent old-age care and health monitoring. The invention provides a tumble detection and emergency response system for the elderly at home based on multi-modal sensing fusion, and the system comprises a sensing module which is used for collecting the multi-modal data of the elderly and at least comprises a depth camera array, an inertial measurement unit, a millimeter wave radar, an audio sensor array, and a pressure sensing pad; the decision-making module is used for receiving, synchronizing and processing the multi-modal data, comprises a dynamic confidence fusion engine and is used for calculating a dynamic confidence score for the data streams acquired by the sensing module in real time and carrying out information fusion through a collaborative game algorithm based on the dynamic confidence score of each data stream to obtain a multi-modal data fusion result; generating a fall detection decision with a situation tag; and the response module is used for executing grading emergency response operation and comprises a local alarm unit, a remote communication unit and an interface unit linked with a community service and emergency center.

The invention belongs to the technical field of industrial robot motion control, and discloses an industrial robot precise path generation system based on three-dimensional visual reconstruction, comprising an environment sensing device which is composed of a multi-baseline structured light projector and a multi-view camera array and obtains point cloud data through light interference suppression and spectrum correction; the three-dimensional reconstruction processing unit generates a three-dimensional model through rigid transformation and deep learning correction; the geometric feature analysis unit performs curvature analysis and boundary recognition to form region description; the trajectory planning device adopts a weighted B spline to generate a trajectory under curvature constraint and energy optimization; the dynamic correction unit is switched between vision feedback and force feedback according to an error threshold value; the control conversion processor combines inverse kinematics and dynamics compensation and predicts deviation to output a control instruction; and the execution driving mechanism utilizes a servo motor and closed loop feedback to execute actions, so that high-precision generation and stable execution of a track under a complex working condition are realized.

The invention discloses a textile dyeing defect intelligent detection method and system based on AI vision, and relates to the technical field of textile quality detection.The method comprises the following specific steps that multi-modal image collection is conducted, specifically, a multi-angle industrial camera array and a multispectral light source containing white light, blue light and near-infrared light are deployed at a production line detection station, dynamically matching and collecting the frame rate and the light source brightness through a synchronous control assembly, and synchronously collecting to form a multi-mode original image set; according to the invention, through the combined design of the double-feature separation network and the mutual exclusive attention mechanism, deep mining is carried out respectively for the geometric morphology features of the wrinkles and the color texture features of the dyeing defects, and the mutual exclusive attention mechanism effectively shields an interference channel between the two types of features by generating an exclusive mask weight matrix, so that feature separation is realized, and the detection accuracy is improved. The system can accurately distinguish wrinkles and dyeing defects, and the misjudgment risk is remarkably reduced.

The invention discloses a vehicle emission identification system and method based on artificial intelligence, and relates to the technical field of vehicle emission detection. The snapshot recognition module is provided with a multispectral camera array and a synchronous controller, dynamically adjusts imaging parameters in combination with a laser radar and a millimeter wave radar, and supports multi-type vehicle recognition; the AI algorithm processing module adopts a heterogeneous computing power architecture to allocate resources; the vehicle anomaly judgment module constructs a multi-dimensional anomaly feature chain, calls OBD data to study and judge anomaly and identifies blacklist vehicles; the data storage and calling module adopts block chain evidence storage and hierarchical storage; the mobile law enforcement adaptation module supports end-side cloud collaboration; the multi-source data fusion module realizes data space-time alignment; the dynamic model self-updating module optimizes the model based on federal learning. The method improves the comprehensiveness of vehicle identification and abnormity determination, guarantees the law enforcement data to be legal and credible, relieves the computing force pressure of a mobile terminal, supports model continuous adaptation technology iteration, assists efficient mobile law enforcement, and provides powerful support for environmental protection law enforcement.

The invention provides a large-view-field high-resolution compound eye camera array system based on bionic human eye vision. The system is composed of a curved surface support and an industrial camera array arranged on the curved surface support. Reverse extension lines of optical axes of all the cameras intersect at the same point to form a concave surface structure similar to the retina, and the fixed view field overlapping rate of the adjacent cameras is kept. The industrial camera array is used for collecting a target scene image, large-view-field information is obtained by fusing and splicing all camera images, and a high-resolution image is obtained by conducting super-resolution reconstruction on a plurality of related images in a view field center interested area. The system provides three working modes, namely a low-resolution mode, a conventional-resolution mode and a super-resolution mode, which can be switched by adjusting camera parameters. Finally, the system can perceive the dynamic change of the edge of the field of view in a high-frame-rate and low-resolution mode, magnify the details in the center of the field of view in a super-resolution mode, realize efficient non-uniform imaging, and optimize the information acquisition and processing efficiency and the structure compactness.

The invention discloses a three-dimensional space perception and reconstruction system, method and device, a medium and a product, and relates to the field of computer vision, three-dimensional reconstruction and SLAM, and the system comprises a hemispherical compound eye camera array module which comprises a pedestal and a plurality of cameras; the plurality of cameras are arranged on the base in a non-coplanar manner, and the spatial distribution of the plurality of cameras forms a hemispherical curved surface; the optical axis of each camera points to different directions, and the combined view field covers a solid angle greater than 180 degrees; the synchronization and control module is electrically connected with the hemispherical compound eye camera array module and is used for sending hard synchronization signals to all cameras; the computer processing module is connected with the synchronization and control module and used for receiving and processing the image data of all the cameras and executing a three-dimensional space perception and reconstruction method, and the three-dimensional map reconstruction efficiency is improved.

The invention relates to the technical field of image processing and recognition, and discloses a shell type recognition method and system based on deep learning, and the method comprises the steps: collecting a two-dimensional image sequence, three-dimensional point cloud data and size parameters of the surface of a shell through an industrial camera array, and carrying out the registration of the three-dimensional point cloud data through an ICP algorithm, and generating a complete three-dimensional model; performing denoising, contrast enhancement and distortion correction on the two-dimensional image sequence to extract image features, performing voxelization downsampling on the point cloud, and extracting curvature to generate roughness features; the image and the point cloud features are fused into a unified feature tensor through an attention mechanism, the unified feature tensor is input into a visual analysis large model to complete feature extraction and classification, and initial recognition results such as ammunition types and calibers and confidence coefficients are output; and when the confidence coefficient is insufficient, triggering an auxiliary identification mechanism to carry out probability correction, and finally generating an identification report. According to the invention, the judgment time is shortened, and the probability of safety accidents caused by man-made misjudgment is reduced.

The invention discloses an underground engineering structure total space deformation analysis method based on a multi-depth camera array, and relates to the field of space deformation, and the method comprises the steps: employing the multi-depth camera array to collect a depth image and a color image of an underground engineering structure, and generating an alignment depth image of each depth camera; performing internal reference calibration on the depth camera, and converting an aligned depth image of the depth camera into three-dimensional color point cloud data of the depth camera; performing external parameter calibration on each depth camera to obtain an optimized transformation matrix from each slave camera to the master camera; transforming the three-dimensional color point cloud data of each subordinate camera to a coordinate system of the main camera by adopting an optimized transformation matrix to obtain a three-dimensional model of the underground engineering structure; and determining corresponding point pairs in the three-dimensional model obtained in different periods, and calculating the Euclidean distance between the corresponding point pairs to realize the full-space deformation analysis of the underground engineering structure. According to the invention, low-cost and high-precision underground engineering deformation monitoring is realized.

The invention discloses a tippler dynamic unhooking robot control method based on visual identification, and belongs to the field of industrial robots, and the method comprises the steps: deploying a visual camera array in a tippler operation area, collecting tippler-carriage image data, and carrying out the dynamic target identification of the tippler-carriage image data; constructing a compartment claw instance segmentation network to perform segmentation positioning on the dynamic instance target set; acquiring claw relative motion state data to predict claw motion, compensating a claw motion deviation prediction parameter to a vehicle claw connection position, and determining an unhooking operation position parameter; and performing smooth unhooking control on an execution mechanism of the dynamic unhooking robot of the car dumper through the fuzzy logic controller based on the unhooking operation position parameters. The technical problems that in the prior art, unhooking of a car dumper depends on manual assistance or a fixed program, the adaptability to dynamic conditions such as claw vibration and image interference is poor, and unhooking stability and accuracy are caused are solved.

The invention relates to the technical field of education and teaching, and particularly discloses a real-time correction method, system, equipment and medium for Chinese exam martial arts, and the method comprises the steps: obtaining a Chinese exam martial arts standard action library which comprises martial arts routine action sequences conforming to the national Chinese exam martial arts scoring detailed rule, each action sequence is composed of calibrated standard three-dimensional human body joint point trajectory data; in the training process, a multi-view two-dimensional image sequence of a plurality of students executing the same martial art routine is collected in real time through a camera array composed of a plurality of synchronous high-speed cameras arranged in a training field; according to the invention, the limitation of a single sensor is broken through, the AHRS / IMU inertial navigation system and multi-view visual attitude estimation are creatively and deeply fused, advantage complementation is realized, the integrity and stability of motion capture are remarkably improved, and the method is particularly suitable for complex posture changes such as flying, turning, low attitude and the like which are common in martial arts.

The invention discloses a self-calibration method, device and equipment for a thunder multi-source sensor and a storage medium, and relates to the technical field of external parameter calibration, and the method comprises the steps: obtaining laser radar point cloud data collected by a laser radar in the inspection process of a vehicle-mounted mobile inspection platform and a road image shot by a distributed multi-camera array; extracting radar point cloud features and image semantic features corresponding to the calibration reference object in the inspection road scene; constructing a multi-dimensional decoupling geometric constraint; optimizing the external parameter to be optimized until the nonlinear optimization objective function converges, and obtaining a reference rigid external parameter matrix; and based on the reference rigid external parameter matrix and the micro-deformation compensation matrix, defining an external parameter time-varying function, maintaining a sliding time window with a variable length, optimizing the micro-deformation compensation matrix of the external parameter time-varying function in the window, and outputting a corrected external parameter matrix. According to the invention, the precision of thundersight data fusion is improved.

The invention discloses a fish behavior observation system in a rice-fish mode based on a neural network, and belongs to the field of fish behavior observation, and the system comprises the steps: obtaining an underwater image through a multispectral camera array, and obtaining a high-quality image sequence under a turbid water body and variable illumination through adaptive filtering, Retinex enhancement and wavelet turbidity removal processing; continuous tracking of fish individuals is realized by combining convolutional neural network detection, feature refining and Kalman filtering; a speed-direction clustering analysis behavior mode is utilized, an exception verification mechanism is introduced, group interaction frequency and spatial distribution are calculated in real time, a behavior integrity evaluation report is output, and an integrated solution from image acquisition to behavior quantification is formed.