415 results about "Spatial perception" patented technology

According to the medical image segmentation method and device based on spatial perception and frequency domain information, the precision and robustness of medical image segmentation are effectively improved by combining frequency domain information guidance and a multi-head state spatial perception technology. Frequency domain transformation is performed on a medical image, high-frequency and low-frequency components of the image are separated by using a multi-scale decomposition technology, and low-frequency features are extracted to obtain global information. By introducing a learnable noise filtering mechanism, noise and irrelevant background information in a frequency domain are suppressed, so that the model can be focused on a lesion area more accurately. A multi-head perception visual state space module is designed on a bottleneck layer, lesion features of different scales are captured through a multi-scale adaptive feature fusion mechanism, and the capability of segmenting small-size lesions and complex structures is enhanced. A context focusing attention mechanism is introduced into jump connection, fusion of global information and local details is further enhanced, and the accuracy of a segmentation result is ensured; and finally, recovering a high-resolution segmented image through a decoder.

The invention discloses an industrial equipment fault early warning analysis method and system based on a graph neural network, and relates to the technical field of intelligent fault diagnosis and equipment state monitoring, and the method comprises the steps: constructing a time slice data tensor, and forming a graph neural network input structure; on the basis of the graph neural network input structure, feature learning and correlation analysis of the graph neural network are executed, and node-level embedding representation is obtained; and based on the node-level embedding representation, calculating fault relevance response distribution of the nodes, evaluating propagation risks and positioning equipment fault sources. According to the method, the problem that a traditional method cannot effectively express a spatial structure or a cooperative relationship between measuring points in an early modeling stage is solved, the spatial perception capability of overall modeling is improved, so that a data foundation is laid for subsequent feature extraction and anomaly analysis, and the reconstruction accuracy is improved by comparing an embedded reconstruction value with an original observation value and calculating a node-level reconstruction error. Fault influence range evaluation and traceability positioning are realized, and quantitative support is provided for early warning response and maintenance decision.

The invention relates to the field of airline planning, and discloses a digital twin system for low-altitude airline planning and a construction method, and the method comprises the steps: obtaining a spatial perception data set of a preset sensor in a target airspace, carrying out the fusion of the spatial perception data set, obtaining the fusion perception data, obtaining the historical flight hidden danger data of the target airspace, and obtaining the historical flight hidden danger data of the target airspace; performing multi-layer convolution on the fusion perception data by using the historical flight hidden danger data to obtain a risk analysis result, performing fuzzy fusion on preset risk factors, generating a dynamic obstacle risk field according to the risk factors after fuzzy fusion and the risk analysis result, performing flight simulation on the dynamic obstacle risk field, and obtaining a flight simulation result; and collecting flight simulation real-time data in the flight simulation process, generating an air route space cost map according to the flight simulation real-time data, and performing optimal path search on the air route space cost map to obtain a low-altitude air route scheme. In the face of airspace actual risks, the searching efficiency of the optimal path in low-altitude route planning is improved.

The invention belongs to the technical field of computer vision and artificial intelligence, belongs to the cross technical field of target detection, deep learning and image processing, and particularly relates to a YOLOv8 algorithm improvement method based on an unmanned aerial vehicle aerial image small target detection model, which comprises the following steps of: introducing a user-defined feature enhancement module into a YOLOv8 backbone network, a neck part and a detection head part; the self-defined feature enhancement module comprises a context guide self-adaptive fusion module introduced into a backbone network so as to replace part of traditional convolution operation; a space edge sensing feature up-sampling module and a space sensing enhanced convolution module are adopted in the neck fusion network; a fine-grained dynamic pruning detection head is introduced into a detection head detection network. According to the method, the performance of the model in a small target detection scene is effectively enhanced, and the accuracy, robustness and real-time response capability of a detection system are remarkably improved.

The invention relates to the technical field of automatic dispensing, and discloses an automatic dispensing method and system based on machine vision, and the method comprises the steps: firstly, precisely obtaining the pixel coordinates of a preset Mark point on a workpiece through machine vision, and calculating a set of 2D deviation vectors of the preset Mark point and a Mark point at an ideal position; then, the set of the Mark point 2D deviation vectors is sent into a pre-trained Z-axis compensation prediction model; according to the model, a corresponding Z-axis deviation value can be learned and predicted from 2D plane deviation, and complex nonlinear mapping possibly existing between the 2D deviation and the Z-axis deviation is effectively solved. And finally, comprehensively utilizing the calculated 2D deviation vector and the Z-axis deviation value predicted by the model to carry out comprehensive three-dimensional adaptive adjustment on the original dispensing path, thereby overcoming the limitation that the traditional method only focuses on 2D compensation and the resulting problem of misalignment of linear and nonlinear spatial perception dimensions. And the accuracy and the adaptability of automatic dispensing are obviously improved.

The invention discloses an efficient open vocabulary-oriented panoramic segmentation method. The method comprises the following steps of S1, performing visual feature extraction and aggregation based on a multi-scale feature extractor and a lightweight aggregator; s2, encoding any category of vocabularies by using a text encoder to obtain text embedding # imgabs0 # S3, improving semantic understanding of visual aggregation features based on a vocabulary perception selection module, and reducing feature interaction burden of a mask decoder; s4, on the basis of bidirectional dynamic embedding experts, generating instance embedding with semantic perception and spatial perception by dynamically distributing expert weights; s5, based on a lightweight decoder, using the object kernel to perform mask prediction and refinement layer by layer, and using the object kernel and text embedding to perform dot product as category prediction; the method aims at reducing the model calculation overhead and increasing the reasoning speed while achieving considerable performance, and has remarkable practical value and application prospect.

The invention discloses a remote sensing image segmentation method fusing frequency modulation and spatial perception, and the method comprises the steps: obtaining and preprocessing an original remote sensing image, and generating a standardized input image; the image is input into a multi-scale frequency domain enhanced feature extraction network, features are extracted step by step according to a plurality of feature levels, each level realizes frequency adaptive semantic enhancement through frequency domain modulation transformation and spatial feature fusion, and deep feature expression is enhanced through feedforward neural network modeling and residual connection output and cross-level residual fusion introduction; the final multi-scale features are decoded through a decoding module, the spatial resolution is recovered, and a pixel-level segmentation result is generated; and constructing a composite loss function containing classification errors, boundary perception and frequency consistency items, and carrying out optimization training on the network. According to the method, semantic complementarity of a remote sensing image in a frequency domain and a space domain is fully mined, so that segmentation precision and robustness of a ground object target in a complex scene are improved, and the method has good generalization ability and engineering practicability.

A system for three-dimensional spatial perception underwater that includes a primary submersible vehicle. Frontward facing projectors are used to scan beams of light across a selected field of underwater view. Further, event cameras with pixels triggered by events are used to detect corresponding spots of the scanned beams that are reflected from underwater objects or surfaces. Circuitry is configured to determine positions of the objects based on the pixels that are triggered. Additionally, pilot submersible vehicles the projectors and event cameras are tethered to the primary submersible vehicle to provide extend detection of spots corresponding to reflections from underwater objects or surfaces.

The invention relates to the technical field of civil engineering construction, in particular to a precise positioning civil engineering pile foundation construction system which comprises a multi-source space sensing and positioning module, a space-time multi-dimensional data fusion module, a three-dimensional guiding and correcting module, a hydraulic control module and a closed-loop feedback optimization module. The multi-source space sensing and positioning module obtains pile machine position and attitude information through the Beidou / GNSS high-precision positioning unit, the inertia high-precision navigation unit and the laser horizontal measurement unit, and the space-time multi-dimensional data fusion module processes and fuses the information to generate unified space position data. The three-dimensional guiding and correcting module compares the data with preset pile position coordinates, if the deviation exceeds a threshold value, a correcting instruction is generated, the hydraulic control module executes the instruction and adjusts the position and the posture of a pile machine, the closed-loop feedback optimization module collects the data in the correcting process and optimizes a correcting strategy, all-weather centimeter-level precision space positioning is achieved, and the positioning precision is improved. And the accuracy and efficiency of pile foundation construction are improved.

The invention discloses a VR teaching experience enhancement system and method, and belongs to the technical field of virtual teaching, and the method specifically comprises the steps: collecting the position data and posture data of a trainee in a VR environment, building a multi-user coordination interaction model based on the position data and posture data of the trainee in the VR environment, and carrying out the interaction of the multi-user coordination interaction model. The method comprises the following steps: acquiring training participants, identifying an interaction relationship and an interaction event among the training participants, generating tactile feedback data corresponding to the interaction event based on the interaction event, performing calibration processing on the tactile feedback data, and synchronously sending the calibrated tactile feedback data to VR equipment of the training participants, the calibration processing comprises adjusting the tactile feedback data based on the spatial propagation difference quantity and the tactile feedback time offset value of the trainees; according to the invention, when multiple trainees cooperatively operate the same virtual object or scene, consistent and real-time tactile response and spatial perception can be obtained, and the teaching experience in a multi-person cooperation scene is improved.

The invention discloses an aberration correction and image quality enhancement method for a laminated structure image, and the method comprises the steps: carrying out the deconvolution preprocessing of a to-be-detected marked image according to an aberration priori set, and obtaining an aberration-free image; meanwhile, combining label data to obtain a data set; feature extraction is carried out based on shallow convolution according to the data set, and global feature information is generated through activation function operation; enhancing the feature data by adopting a frequency domain feature and spatial domain feature fusion strategy; the enhanced feature map realizes initial aberration restoration through an aberration correction module; the corrected feature map is processed by a double-channel attention mechanism, and the global context modeling capability of the self-attention mechanism and the spatial perception characteristic of the position attention unit are fused in parallel; and the image resolution is improved through a super-resolution reconstruction module comprising a sub-pixel convolution layer. By adopting the technical scheme of the invention, the accuracy of overlay error detection is improved.

The invention discloses a digital twin interaction control system based on robot vision and tactile fusion, and the system comprises a data collection module which is used for collecting visual image data and tactile sensing data; the preprocessing module is used for preprocessing; the multi-mode perception modeling module is used for executing spatial perception modeling and tactile state modeling; the fusion state vector construction module is used for generating a fusion state vector; the digital twinborn mapping module is used for constructing a digital twinborn body; the simulation interaction control module is used for executing interactive action control and generating a control instruction parameter group; the action execution and feedback module is used for controlling the robot to execute the actual action and updating the fusion state vector; and the dynamic evaluation and self-adaptive adjustment module is used for dynamically optimizing and adjusting the fusion state vector. Visual and touch information of the robot is fused, a digital twinning synchronous control mechanism is constructed, and the interactive optimization method which is fast in operation response, high in execution precision and stable in control process is achieved.

The invention discloses a fruit tree disease and pest detection method and device based on improved YOLOv11, relates to the technical field of automatic machine learning, and aims to solve the problem that YOLOv11 in the prior art is still difficult in apple disease and pest detection. Taking the fruit tree shot image as input, and outputting based on the fruit tree pest detection model to obtain a fruit tree processing graph; and judging whether the fruit tree has diseases and pests and the types of the diseases and pests according to the fruit tree processing graph, wherein the fruit tree disease and pest detection model is obtained based on improvement of a YOLOv11 model. According to the method, advanced feature extraction, layered feature fusion and enhanced spatial perception functions are integrated, efficient and accurate detection of fruit tree diseases and insect pests is realized, the robustness and generalization ability of a detection model are improved, and meanwhile, relatively high performance is kept in a complex environment.

The invention discloses a road disease detection method based on an unmanned aerial vehicle, electronic equipment and a program product. The method is realized based on a trained road disease detection model, a C3k2-MDDSC module is introduced into a backbone network of the model, the feature multiplexing capability is enhanced through gradient shunting and multi-scale fusion, the gradient disappearance problem is relieved, and the robustness of the model is improved by means of jump connection and packet convolution. An ACFP module is introduced into the tail end of the backbone network, dynamic fusion of local and global features is realized by using multi-scale cavity convolution and a channel-space attention mechanism, and the complex scene modeling capability is improved. And the neck network is integrated with an SGF module, so that the spatial perception of the model to a tiny target can be improved. Besides, the ES-FPN proposed based on the SGF module not only can enhance the utilization of shallow spatial information, but also can optimize the complementarity of cross-level features. During training, regression loss, namely fast high-quality intersection-to-union ratio loss, is proposed, and angle punishment is introduced to improve the alignment precision of the rotating frame and the convergence speed of the model.

The invention provides a stack image enhancement processing method, and relates to the technical field of image enhancement, and the method comprises the steps: collecting and preprocessing a stack image, and constructing a training sample set containing a risk level and an inclination angle label; an encoder-classifier dual-stage model is constructed, an encoder extracts features through a multi-scale feature extraction and enhancement module, a space perception attention enhancement module, a dynamic feature pyramid fusion module and a deformation gradient enhancement residual module, and a classifier outputs a risk level and an inclination angle; during training, classification and regression loss weights are dynamically adjusted, an adversarial enhancement sample is generated to enhance robustness, and a gradient is adaptively cut based on a channel attention enhancement vector. According to the method, local illumination interference can be effectively eliminated, multi-scale features are fused, the sensitivity to a sparse risk area is enhanced, and the self-adaptive enhancement processing effect of the heaped image in a complex environment is improved.

The invention discloses a zero sample anomaly detection method and system based on a triple perception learning enhanced visual language model, and relates to the field of computer vision, and the method comprises the steps: extracting global and local visual features from an input image; in the visual coding process, local features in a deep network are corrected through a spatial perception attention enhancement module, and fine-grained attribute text description is generated for abnormal visual features; performing deep semantic alignment on the attribute text description and the general text prompt through an attribute perception guide module; calculating the similarity between the enhanced visual features and the optimized text features, and generating a pixel-level abnormal segmentation map; in the inference stage, the segmented image is converted into a space attention weight through an anomaly perception reconstruction module, the space attention weight is fed back to a visual encoder to generate final global feature representation, and an anomaly score is calculated. According to the method, under the condition that a target domain training sample is not needed, the anomaly detection and positioning accuracy and generalization ability of the model under the scenes of industrial defect detection and the like are remarkably improved.

The invention is applicable to the technical field of medical image processing and mixed reality, and provides a laparoscopic surgery mixed reality navigation method based on deep learning and dynamic point tracking, which comprises the following steps: dynamically registering a three-dimensional model containing kidney, tumor and vessel with an initial frame of a laparoscope through a mixed reality alignment technology; the method comprises the following steps: constructing an operating forceps motion sensing model based on a time sequence deep neural network, realizing real-time control and parameter locking of a three-dimensional model pose, dynamically updating a two-dimensional feature point set by adopting a multi-feature-point combined tracker, constructing a candidate feature combination through a cross-quadrant sampling strategy, and constructing an operating forceps motion sensing model; a candidate feature combination is generated through a four-quadrant division and cross-regional sampling strategy, an optimal camera pose parameter is generated in combination with a re-projection error and pose continuity constraint, and an operation video is dynamically covered with a semitransparent three-dimensional model. The method can significantly enhance the spatial perception capability of the kidney anatomical structure, reduce the registration error of the kidney in the three-dimensional integrated kidney structure model and the laparoscope video, and improve the navigation precision.

The present disclosure provides a method for calibrating a humanoid robot, comprising obtaining a humanoid robot with original kinematic biasing values, controlling the humanoid robot through predetermined poses, capturing image data of body parts using vision sensors mounted on the humanoid robot while moving through the poses, determining revised kinematic biasing values by processing the image data using a bipedal spatial perception model trained using synthetic image data containing keypoints, and replacing the original kinematic biasing values with the revised kinematic biasing values. The bipedal spatial perception model processes captured image data to generate observed keypoint locations on robot components, which are compared with kinematic-based locations from joint encoder measurements to minimize discrepancies through optimization algorithms.

The invention relates to the technical field of alignment of photoetching machines, and discloses an alignment method, device and equipment for a photoetching machine and a storage medium. The method comprises the following steps: carrying out image acquisition and space registration on a plurality of small-size alignment marks preset on the surface of a wafer to obtain a virtual large mark image; performing double-light-source imaging on the target area of the virtual large-mark image by adopting an ultraviolet light source and an infrared light source respectively to obtain an ultraviolet light image group and an infrared light image group; performing multi-level fuzzy discrimination and spatial perception double attention processing on the ultraviolet light image group and the infrared light image group to obtain a multi-level optimization feature set; according to the multi-level optimization feature set, guiding filtering and self-adaptive parameter fusion are executed, an optimal alignment coordinate is obtained, and the photoetching machine is driven to execute alignment operation based on the optimal alignment coordinate, so that the optimal configuration of alignment parameters under different process conditions and environment interference is ensured, and the alignment accuracy is improved. And the alignment accuracy and reliability of the photoetching machine are improved.

The invention discloses a robust point cloud registration method and system based on global spatial perception and multistage filtering. The method comprises the following steps: respectively obtaining point cloud initial features and spatial structure features of global spatial perception based on point cloud information; inputting the point cloud initial features and the spatial structure features into a transform network for feature enhancement, and obtaining enhanced features; and performing up-sampling processing and multi-stage filtering on the enhanced features to obtain a transformation matrix, and completing point cloud registration. According to the method, global spatial perception and multi-stage filtering are introduced, so that abnormal values can be effectively eliminated while feature matching is completed finally, and the precision and efficiency of point cloud registration are improved.

The invention discloses a traffic flow prediction method based on multi-scale dual hypergraph fusion. Aiming at the problem that an existing traffic flow prediction model is difficult to capture a multi-scale high-order spatial dependency relationship of a traffic road network, the method comprises the following steps of: firstly, constructing an urban road dual hypergraph with three scales of microscopic individual travel intention, mesoscopic community commuting interaction and macroscopic area flow conduction, and then designing a traffic flow prediction method of space-time perception based on the urban road dual hypergraph. Wherein the spatial perception module extracts and fuses a dependency relationship between a high-order local spatial feature and a global spatial feature in a multi-scale dual hypergraph; the time sensing module captures short-term fluctuation and long-term trend of traffic flow; and residual connection is introduced to enhance spatio-temporal feature fusion, and finally multi-step traffic flow prediction is realized. The invention provides a traffic flow prediction model with multi-scale high-order space perception, which adaptively fuses high-order space features of three scales of microcosmic, mesoscopic and macroscopic, and significantly improves the precision and generalization ability of traffic flow prediction.

The invention discloses a visual language action large model design method for enhancing spatial perception ability, which comprises the following steps of: acquiring a plurality of frames of task scene images, inputting the images into a two-dimensional image encoder and a visual geometry guidance Transformer VGGT encoder, extracting image features and performing time sequence spatial embedding, encoding a natural language instruction into language embedding representation, and extracting a visual language action large model. Image features and language embedding are fused through a cross attention mechanism, a pre-training large model is input to generate cross-modal representation, the fusion features are input into an action expert module to output an action control sequence in combination with robot body state information, and a robot is driven to execute an operation task. Therefore, damage of additional information to an original pre-training model is avoided, and compared with an original combined structure of a pre-training visual language model and an action expert, utilization of multi-view picture information is enhanced, so that higher understanding ability on space depth is achieved in the task execution process, the task success rate is increased, and the task execution efficiency is improved. And a more efficient and more robust robot sensing and decision-making integrated system is realized.

The invention provides a remote sensing small target detection method based on position information transmission and feature decoupling. The method comprises the following steps: acquiring a to-be-recognized remote sensing image; inputting a to-be-recognized remote sensing image into the trained target detection model, and reasoning the to-be-recognized remote sensing image by a student model of the trained target detection model to obtain a small target recognition result of the to-be-recognized remote sensing image; wherein in the training stage, knowledge migration is achieved through a teacher-student cooperative training framework, image features are transmitted and fused through position information transmission and a feature decoupling mechanism, and the spatial perception ability of a teacher model is inherited. The student model after knowledge distillation optimization does not need to depend on a complex feature fusion structure in the reasoning stage, and the remote sensing small target detection precision can be improved directly through a lightweight network.

The invention relates to a multi-feature fusion driven high-quality drainage pipeline defect sample automatic labeling method, system and device and a medium, and the method comprises the steps: carrying out the frame extraction of drainage pipeline image data; performing defect visibility screening on the image after frame extraction, and performing forward diffusion noise injection, noise distribution prediction and reverse diffusion iteration denoising on the image which does not meet a visibility threshold to obtain a defect image which meets a labeling requirement; the defect image is sent to the labeling terminal, and a feedback labeling result is subjected to multi-stage quality verification to generate a data set with contour labeling; and constructing an improved YOLO11-OfficientViT fusion model, loading a pre-training weight, training a data set by adopting a pipe diameter perception training mode, and generating a defect detection model with spatial perception capability. According to the method, the efficiency, accuracy and model generalization ability of drainage pipeline defect marking are remarkably improved through systematic technical improvement.

The invention discloses a bridge inclination and settlement monitoring method and system based on multi-sensor fusion, and belongs to the field of bridge structure monitoring, and the method comprises the steps: obtaining the data of a multi-source heterogeneous sensor; the sensor data is converted into space-time diagram data, and the space-time diagram data comprises the steps that each sensor is mapped into nodes in a diagram, edges between the nodes are defined according to the physical connection relation of the bridge structure, and multi-source heterogeneous sensor data are unified into dynamic feature vectors with the same dimension on the nodes through learnable feature mapping; inputting the time-space diagram data into a preset neural network model, performing spatial feature aggregation on the dynamic feature vector through a diagram attention mechanism, performing time feature extraction through a time convolutional network, and generating a hidden state vector fused with time-space information; and generating a monitoring state value of the bridge based on the hidden state vector. According to the invention, depth feature fusion of spatial perception is realized, and the sensitivity of anomaly recognition is improved.

The invention discloses a Bluetooth hearing-aid earphone optimization method and system for adaptive hearing compensation. The method comprises the following steps: S1, acquiring a personalized hearing curve of a user and background audio data of left and right ears in each typical scene, and respectively preprocessing the background audio data; s2, extracting time-frequency fusion features of each typical scene, generating scene feature vectors, and constructing a scene feature vector library; s3, calculating a scene perception conversion vector, an abrupt change risk vector and a smooth gain vector; s4, calculating a binaural gain change vector, a binaural synchronous entropy vector, a phase alignment factor vector and a left and right ear cooperative gain vector; and S5, according to the left and right ear cooperative gain vectors, processing the background audio data after left and right ear preprocessing in the current scene, and outputting the processed background audio data to the Bluetooth hearing-aid earphone. According to the invention, the problem of gain abrupt change generated during scene switching of the existing Bluetooth hearing-aid earphone and the problem of spatial perception distortion caused by binaural coordination imbalance can be solved.

The invention discloses a real-time rendering method for a three-dimensional scene based on spatial perception, and the method comprises the steps: obtaining an initial sparse point cloud of a scene image, carrying out the denoising of the initial sparse point cloud, then extracting a spatial boundary of the scene, carrying out the voxelization of the denoised point cloud, taking the center of each voxel as an anchor point, randomly selecting a camera pose, and carrying out the recognition of the spatial boundary of the scene. The method comprises the following steps: generating a rendered image according to a current anchor point and a neural Gaussian attribute thereof, calculating loss and carrying out back propagation, cutting an original anchor point, generating a new anchor point based on spatial perception, finally terminating iteration after an iteration termination condition is reached, and generating the rendered image according to the current anchor point and the neural Gaussian attribute thereof. According to the method, it is guaranteed that anchor point growth only occurs in a reasonable space range, the anchor points can effectively cover an under-reconstruction region, prediction of neural Gaussian related attributes is optimized, and the processing capacity for complex geometrical shapes and details is enhanced while the reconstruction quality is guaranteed.

The invention provides a liver and tumor recognition method and device fusing an attention mechanism and multi-scale convolution and a readable storage medium, and aims to solve the problems of insufficient global information capture, poor multi-scale feature adaptation, weak spatial perception and the like in the existing medical image segmentation technology. A convolution attention module and a coordinate attention module are embedded in jump connection to enhance key feature focusing, and a multi-scale down-sampling block is designed to adapt to tumor multi-scale features. Through data preprocessing and model training and optimization, accurate segmentation of the liver and the tumor is realized. Experiments show that the method significantly improves the segmentation precision in public and clinical data sets, especially improves the detection rate of small tumors and the boundary integrity of large tumors, and provides reliable support for clinical diagnosis.

The invention relates to a remote sensing image target detection method and device based on an adaptive auxiliary head structure. According to the method, a remote sensing image target detection network based on a self-adaptive auxiliary head structure is designed, and the network is obtained by adding the self-adaptive auxiliary head structure between a YOLO classical network structure neck and a detection head and replacing an original neck structure with a context aggregation bidirectional connection structure; a spatial perception attention module, convolution recalibration multi-scale feature fusion, a context aggregation bidirectional connection structure and a self-adaptive auxiliary head structure are designed; the network has good spatial feature aggregation capability to retain key feature information, an adaptive weight mechanism is integrated, information loss caused by fusion of different scales is reduced, feature refinement is performed on a to-be-detected image, and the accuracy and effectiveness of remote sensing target detection are improved.

The invention relates to the field of robot navigation, and particularly discloses a spatial perception observation information quality evaluation method and system, a terminal and a medium, and the method comprises the steps: converting the measurement information of each dimension into a normalization probability according to the measurement target of each dimension; according to the concept of entropy in the information theory, the normalized probability of each dimension is converted into a computable entropy value; a downstream task weight coefficient is introduced for the entropy value of each dimension, the multi-dimensional entropy value after the downstream task weight coefficient is introduced is mapped into a physical interpretable confidence coefficient in a [0, 1] interval through an exponential attenuation function, and the confidence coefficient explicitly represents the uncertainty of observation information of each dimension. According to the method, a space perception quality evaluation system with mathematics interpretability, scene adaptation and real-time calculation is formed, multi-source uncertainty is mapped into executable confidence, and multi-dimensional accurate evaluation of intelligent agent space perception observation information is achieved.