196 results about "Image perception" patented technology

The invention relates to the technical field of image processing, in particular to an image fusion driven anti-collision identification method and system. The method comprises the following steps: acquiring multi-source image data, and performing semantic guidance image fusion according to the multi-source image data to obtain image fusion data; performing scene segmentation according to the image fusion data to obtain scene segmentation data, and performing target detection on the scene segmentation data to obtain scene target data; performing confidence hierarchical space positioning according to the scene target data to obtain target positioning data, and performing motion trail prediction according to the target positioning data to obtain motion trail prediction data; and performing collision area propagation risk assessment according to the motion trail prediction data to obtain collision risk data. By fusing image perception depth, semantic understanding dimension and trajectory evolution modeling, the method has higher environmental adaptability, weak target recovery capability and system interpretability.

The invention discloses an intelligent car coupler identification method and system of a car dumper based on a picking and re-hooking robot, and relates to the technical field of intelligent identification, the method comprises the following steps: multi-modal data is collected and preprocessed, and the multi-modal data comprises laser radar point cloud data, camera images and infrared data; fusing the processed multi-modal data, identifying coupler information by adopting a convolutional neural network and an attitude estimation algorithm, and generating a coupler identification result; a path planning algorithm is adopted, a grabbing point path of the car coupler is calculated, interferents are avoided, the grabbing path is optimized, and an optimal grabbing path is generated; and the robot moves to the position of the car coupler according to the optimal grabbing path, the grabbing posture is corrected in real time through image perception, and a mechanical arm tail end executor is driven to clamp the car coupler for grabbing. Through multi-modal data fusion and path planning, the coupler is accurately recognized, the grabbing path is optimized, and the grabbing precision and stability of the robot in a complex environment are improved.

The invention discloses a complex water area garbage intelligent detection and collection method based on multi-modal perception, and the method comprises the steps: obtaining image perception data and semantic environment data of a complex water area; the method comprises the following steps: performing preprocessing, performing feature extraction and semantic coding, and then performing multi-modal feature alignment fusion, feature extraction and multi-task detection to obtain a garbage detection result, thereby effectively realizing deep fusion of semantic priori and visual features, and improving the accuracy and robustness of garbage recognition in a complex water area environment; then task importance evaluation is carried out to construct a garbage disposal priority queue, a path dynamic planning strategy oriented to energy consumption constraint and task priority collaborative optimization is constructed to carry out path generation, navigation energy consumption is reduced on the premise that task benefits are considered, and garbage detection and collection tasks in a complex water area are achieved; the system adapts to various intelligent devices such as unmanned ships, unmanned aerial vehicles and shore-based platforms, and has high autonomy, environmental adaptability and engineering deployability.

Embodiments of this disclosure can provide a system and method for training a perception model to perform an autonomous driving task. During operation, the system can obtain labeled training data comprising images captured by multiple cameras mounted at different locations on a vehicle, and the perception model can generate, in parallel, a prediction output associated with the task and a confidence score based on the labeled training data. The confidence score can indicate a level of uncertainty associated with the prediction output. The system can generate an uncertainty-weighted prediction based on ground truth indicated by the labeled training data, the prediction output, and the confidence score; compute a loss function based on the uncertainty-weighted prediction; and update the perception model based on the loss function.

The invention discloses a metal cutting tool wear monitoring method and system, and relates to the technical field of wear monitoring. The method comprises the following steps: monitoring the metal cutting tool in real time to obtain a multi-source sensing sequence; constructing a tool cutting monitoring matrix; inputting the tool cutting monitoring matrix into a tool wear detection channel to obtain a first tool wear coefficient; performing differential detection on the first tool wear coefficient according to a preset cutting scheme, and outputting a wear differential signal; performing image perception correction on the first tool wear coefficient to obtain a second tool wear coefficient; and obtaining a historical tool wear coefficient set of the metal cutting tool, performing wear trend prediction in combination with the second tool wear coefficient, constructing a tool wear trend chart, and performing health management on the metal cutting tool according to the tool wear trend chart. The technical problem of low tool wear monitoring precision in the prior art is solved, and the technical effect of improving the tool wear monitoring precision is achieved.

The invention relates to the technical field of rainstorm early warning, in particular to a deep learning-based rainstorm terminal early warning method and system, which introduces an image perception and semantic recognition mechanism to carry out consistency judgment on an overturning state of a rain gauge and image features such as a rain curtain and a water mark so as to realize effective elimination of unnatural trigger factors (such as manual water pouring and the like). And the reliability of rainfall sensing data is improved. A double-branch deep learning model capable of being deployed locally is adopted, multi-source data such as rainfall, temperature and humidity and topographic factors are fused, feature extraction and recurrence period prediction are performed based on a cross attention mechanism, and a threshold correction coefficient is dynamically generated in combination with historical events, so that self-adaptive adjustment of an early warning standard is realized, and misinformation and missing report risks are effectively reduced. The system supports low-power-consumption local operation, early warning decision is executed, and timeliness and accuracy of local early warning response are effectively improved.

The invention relates to the technical field of automatic driving, in particular to a multi-view-angle perception-driven automatic driving test scene twin reconstruction method, which comprises the following steps of: S1, acquiring and preprocessing multi-view-angle image perception data to form a multi-view-angle image sequence under a unified coordinate system; s2, spatial coding and modeling based on an orthogonal three-plane structure; s3, on the basis of a view perception-space interaction encoder of a Transform architecture, completing space coding processing represented by an orthogonal three-plane structure; s4, structure and semantics combined twinborn reconstruction decoding is carried out; and S5, generating a simulation compatible format and deploying a platform. According to the method, the terrain, facilities, traffic elements and other contents of a real road scene can be automatically restored, the storage and calculation cost is remarkably reduced while the spatial information integrity is ensured, various simulation platform support formats can be flexibly output, the method serves for closed-loop testing of automatic driving, and the reconstruction efficiency and the system suitability are improved.

The invention relates to the technical field of robot control, and discloses a robot motion control method, device, equipment and medium, and the method comprises the steps: constructing a simulation environment and configuring a robot model, obtaining a depth image of an area in front of the robot model through a depth sensor, carrying out the semantic segmentation of the depth image, generating a semantic mask of a passable area, and carrying out the recognition of the passable area. And generating three-dimensional terrain point cloud data, extracting a semantic feature vector of the point cloud data, inputting the semantic feature vector and the body state information of the robot model into the motion control strategy model to generate a joint control instruction, and driving the robot model to move according to the joint control instruction. According to the method, depth image perception, semantic segmentation, three-dimensional point cloud reconstruction and feature extraction are fused, robot body state information is combined, a self-adaptive joint control instruction is generated, dynamic perception, decision making and control of robot motion in various complex terrains can be achieved, and the autonomous motion energy of the robot in an unstructured environment is effectively improved.

The invention relates to the technical field of artificial intelligence, in particular to an art design style migration system based on deep learning, which comprises an image perception analysis module, a style constraint calculation module, a detail structure mapping module, a scale corresponding screening module and a region fusion regulation and control module. According to the method, the texture, the structure and the edge features of the image are accurately analyzed, key details and structure areas are identified, so that the flexibility of style migration is improved, style application is more accurate, the problem of excessive stylization or content imbalance is avoided, the spatial features of the content areas are effectively identified by analyzing the detail structure levels of the image, and the image quality is improved. The method comprises the following steps of: selecting and matching an optimal style domain according to a scale corresponding relation, enhancing multi-scale style adaptability, improving expressive force of an image under multi-style conversion, ensuring natural fusion of contents and styles, improving a visual effect and keeping harmony between the styles and original contents, and ensuring the accuracy of style mapping, screening and matching an optimal style domain according to a scale corresponding relation.

The present invention relates to a compaction monitoring device and method that integrates 3D scanning and image perception. The device comprises a sensing component comprised of a 3D road surface texture scanning module, a camera, a vibration damping module, and a locator; a data analysis component, which performs image positioning, image stitching, time synchronization, and calculation of a compaction index based on texture features; and a control strategy component, which controls the number of compaction attempts based on the surface distribution of the compaction index. Compared to existing technologies, this invention introduces a new information source and evaluation basis for road compaction, enabling dynamic, real-time monitoring and feedback control of compaction levels, improving the quality of road construction projects and enabling compaction monitoring based on 3D scanning and image perception.

The invention provides a bridge structure health diagnosis agent construction method based on a thinking chain and a domain tool set. According to the method, a language model with a causal reasoning capability is introduced, and a complete task chain process is constructed by combining target detection, segmentation and geometric analysis models for image processing, so that automation and intelligence from image perception to structure diagnosis are realized. The method supports multi-image input and multi-task cooperative processing, has good generalization ability and interpretability, and can effectively improve the efficiency and accuracy of structural health assessment.

The invention discloses a forged face image generation method and recognition method based on a diffusion model, and the method comprises the steps: collecting various watermark signatures used for a forged face image and a plurality of vanilla images, training an encoder and a decoder of a HiDDeN model, and employing the trained decoder as a watermark extractor to carry out the detection of a watermark in the forged face image; a plurality of vanilla images are collected to form a data set, an original watermark signature m is distributed to each training sample in the data set so as to train an LDM network model, a watermark extractor is adopted to carry out watermark extraction on output images of the LDM network model, and a watermark signature # imgabs0 # is obtained; and constructing a total loss function by using the watermark signature # imgabs1 #, the corresponding original watermark signature m and the image perception loss, and finally acting the total loss function on a back propagation process of a decoder of the LDM network model, so that the LDM network model learns the capability of generating the watermark image.

The invention relates to an insulator image segmentation method based on an infrared enhancement image of an unmanned aerial vehicle, and relates to the technical field of image processing, and the method comprises the steps: collecting an infrared image sequence through an infrared enhancement camera, obtaining a standard infrared image sequence, and carrying out the state recognition through an image perception prior engine, outputting a predicted insulator attention heat map and a predicted boundary confidence map; optimizing and adjusting the initial threshold segmentation algorithm and the initial region growing algorithm, and obtaining an adaptive threshold segmentation algorithm and an adaptive region growing algorithm; and carrying out image segmentation on the standard infrared image sequence to output an insulator image sequence, and carrying out early warning judgment on the insulator image sequence based on an adaptive early warning mechanism. The method solves the problems that a traditional insulator image segmentation method cannot effectively deal with the problems of low infrared image temperature contrast ratio, large noise interference and complex background, so that the insulator segmentation is easy to cause mistaken segmentation and missing segmentation, and the high-precision requirement of fault detection is difficult to meet.

The invention discloses a digital media content element accurate screening method based on artificial intelligence image recognition, and relates to the technical field of digital media, and the method comprises the steps: building a distributed capture network to form a dynamic content pool, and building a metadata index database; calling a multi-modal image perception engine to generate a double-layer characteristic spectrum containing dominant and recessive elements; constructing a distributed recognition model cluster based on federated learning; converting the user demand into a screening parameter set and generating a decision tree; screening and secondarily verifying an output result through a double-path matching mechanism; and constructing a reinforcement learning reward function based on user behaviors, and driving the model and the decision tree to co-evolve. Multi-source heterogeneous content full-dimension analysis is achieved, the recognition comprehensiveness and depth are improved, knowledge barriers and privacy risks are solved, the screening accuracy and flexibility are improved, the system is endowed with the continuous optimization capacity, and the method is suitable for efficient and accurate digital media content screening scenes.

The invention relates to a boiler combustion adjusting system and method based on a slag state, and the system comprises an image data sensing system which is used for detecting the contour of a slag block according to image information, matching the three-dimensional dimension of the slag block and analyzing the ash component of the slag block, so as to generate image sensing data of the slag block according to the contour, the three-dimensional dimension and the ash component; the edge-cloud collaborative computing architecture is used for constructing a combustion big data platform and calculating the theoretical slag quantity under the current combustion working condition by utilizing the combustion big data platform; and the combustion optimization decision execution system is used for determining the abnormal condition of combustion of the hearth on the basis of the difference value between the theoretical slag falling amount and the actual slag falling amount, and adjusting at least one combustion parameter of the primary air volume, the rotating speed of the coal mill, the air distribution mode and the soot blowing system. According to the embodiment of the invention, a perception-analysis-execution integrated framework is constructed, and targeted strategy optimization is carried out on the combustion abnormal working condition based on parameters such as the slag falling amount, the slag block falling position and components.

The invention provides an electromagnetic adsorption type drilling actuator capable of achieving automatic feeding and an automatic drilling method, and relates to the technical field of intelligent machining. The electromagnetic adsorption type drilling actuator comprises a drilling machine body, a connecting support, a feeding system, an electromagnetic adsorption device, an image sensing unit and a control system; the connecting support is slidably connected with the drilling machine body and fixedly connected with the tail end of the mechanical arm. The feeding system is fixedly arranged on the connecting bracket; a moving part of the feeding system is fixedly connected with the drilling machine body; the electromagnetic adsorption device is fixedly arranged on the connecting support, and the adsorption face of the electromagnetic adsorption device faces the feeding direction of the drilling machine body. The image sensing unit is fixedly arranged on the drilling machine body; and the control system is electrically connected with the drilling machine body, the feeding system, the electromagnetic adsorption device and the image sensing unit. A visual module is integrated on the aspect of the device, an executing mechanism with the real-time environment sensing capacity is constructed, and cooperative control of precise feeding transmission and self-adaptive adsorption is achieved through modular design.

The present invention includes systems and methods for optimizing perception of image data. Color vision impairment affects a user's ability to distinguish between certain colors, reducing the impact of color images. Mapping color image data to an expanded color gamut increases contrast between colors and the available number of distinct colors in a set of image data, which improves the ability of a user with color vision impairment to see differences between colors. The present invention includes systems and methods for determining a type of color vision impairment in order to provide customized optimization of image data for improved color perception.

The invention provides an active train sensing system and method based on thunder and vision fusion. Comprising an acquisition module used for acquiring multi-path image data containing different focal length images and three-dimensional point cloud data corresponding to the front of a train; the time synchronization module is used for generating synchronous image data and synchronous three-dimensional point cloud data; the coordinate transformation module is used for establishing a spatial transformation relation between the three-dimensional point cloud coordinate system and the image coordinate system; the image sensing module is used for inputting the synchronous image data and the three-dimensional point cloud data into the target segmentation network to obtain track coordinates; the point cloud sensing module is used for obtaining three-dimensional point cloud data in front of the train; and the thunder-vision fusion module is used for distributing an accurate center representative coordinate for each obstacle, generating structured obstacle information and outputting the structured obstacle information to a vehicle-mounted train control system. The multi-source sensing information utilization rate can be improved, the track obstacle recognition and three-dimensional positioning precision is improved, and the system sensing stability in a complex scene is enhanced.

The invention provides an alarm system and method for wild animal protection, and the method comprises the steps: carrying out the fitting of a motion track of an invading wild animal at the boundary of a protection region through historical infrared monitoring information, and obtaining a target motion model of the invading wild animal; according to the target motion model, determining the false alarm trajectory deviation of the motion trajectory of the invaded wild animals when the alarm device alarms mistakenly every time in the protection area boundary; imaging perception is carried out on the thermal imaging identification of the invading wild animal in the specified invading time period to obtain a target movement track of the invading wild animal; according to the target movement track and all the false alarm track deviations, the induction level of the alarm device when false alarm pre-judgment is carried out on the invading wild animals is determined; and finally, performing confidence coefficient calibration on an alarm instruction of the alarm device based on the sensing level, and further controlling the alarm device to complete verification type intrusion alarm of wild animal protection by the alarm instruction after confidence coefficient calibration. By adopting the scheme of the invention, the false alarm rate of the wild animal protection area boundary monitoring system can be reduced.

The invention relates to the technical field of laboratory information management, and discloses a laboratory information management system combining artificial intelligence perception and service-oriented construction, and the system comprises an AI data and image perception module which comprises a data and image collection unit, a model building unit and a processing unit based on a convolutional neural network; the service-oriented architecture service layer comprises a service bus and a plurality of service interfaces, and the service interfaces package the AI data and the image sensing module into independent services by adopting a RESTfulAPI standard; the core laboratory information management module comprises a database and a user interaction interface; and the resource management module comprises a resource monitoring unit and a distribution unit. In the invention, through a service bus of a service oriented architecture (SOA) and a priority scheduling algorithm, and in combination with an artificial intelligence image perception module, computing resources can be dynamically allocated according to a task emergency degree and resource availability, efficient optimization of laboratory data processing is realized, and continuity of laboratory workflow and real-time performance of data processing are improved.

The invention discloses an image generation and understanding unification method and system based on joint diffusion modeling, and relates to the technical field of image generation and understanding. According to the method, image generation and understanding tasks are unified through joint diffusion modeling, models do not need to be designed for the generation and understanding tasks respectively, and the efficiency is improved. Through improved DINOv2, Segmenter and DETR models, classification feature class cluster polymerizability, boundary detail segmentation and small target feature expression detection are enhanced. A random role allocation mechanism and a mask full attention mechanism flexibly process multi-domain information, domain invariant position coding assists cross-domain alignment, and the modeling precision is improved. Through optimization training, the model supports joint generation, controllable generation and image perception tasks at the same time, the performance is superior to that of an existing unified model, and even exceeds that of a proprietary model in tasks such as edge detection.

The invention discloses an extensible reconstruction method for a super-large scene based on 3D (three-dimensional) Gaussian Splitting. The balance of the number of sub-block images is ensured through image perception self-adaptive blocking; a low-contribution view is removed through SSIM pruning; a priori depth is obtained based on virtual stereo rendering, a confidence mask is generated through multi-view verification, boundary consistency loss is constructed, and Gaussian densification is driven; and finally, carrying out TSDF fusion to obtain a seamless grid. Experiments show that reconstruction is completed in a 2.7 km scene within 6 h, MatrixCity-Aerial F1 reaches 0.784 and is improved by 41% compared with City GaussianV2, precision, efficiency and boundary consistency are considered, and the method is suitable for being applied to oversized scenes such as digital twin cities and automatic driving.

The invention provides a multi-source heterogeneous traffic light data fusion optimization method based on vehicle and road cloud, and the method comprises the following steps: S01, traffic light data collection: carrying out the collection of the traffic light data in three modes, the method comprises the following steps: S02, carrying out image perception and identification on traffic lights through a camera, obtaining traffic light data of an intersection through V2X short-distance communication, and obtaining traffic light data through cloud traffic light data service, S02, carrying out data cleaning, S03, carrying out multi-source data fusion, and S04, carrying out HMI data display. More than 99.9% of service availability is realized; the average delay is controlled within 100ms; and city-level scale deployment is supported. Under the extreme condition, if the communication delay fluctuation is large, the phenomenon of transient display delay or second skipping may occur, but the system can still ensure the basic service continuity through a data compensation mechanism.

The invention discloses an obstacle three-dimensional space occupation analysis method based on all-round view image perception, which comprises the following steps of: acquiring multi-path image data from an all-round view camera, and converting all-round view data into an image feature map through a multi-stage image preprocessing and encoding link; then, a three-dimensional bilinear sampling technology is utilized to add spatial dimension information to the feature maps, so that deep fusion and interaction of semantic and position information among different scales are achieved, multiple feature maps of different sizes are generated, on one hand, the feature maps are used for multi-scale supervised learning in the training stage, and on the other hand, the feature maps are used for multi-scale supervised learning; on the other hand, the required regression quantity is output through a specific detection mechanism in the reasoning stage, and therefore an automatic driving environment sensing scheme which is economical, efficient and excellent in performance is achieved. According to the invention, high-precision three-dimensional environment perception is realized only by means of the look-around camera system, dynamic and static obstacles in the environment can be effectively distinguished and predicted, the real-time detection performance is ensured, and meanwhile, the robustness of the perception system in a complex environment is improved.

The embodiment of the invention provides an image processing method, electronic equipment and a computer readable storage medium. The method relates to the technical field of artificial intelligence, and comprises the steps: carrying out the down-sampling of a target image, and obtaining image information; performing information extraction on the target image to obtain semantic information; coding is carried out based on the image information and the semantic information, and a composite code stream is generated; the composite code stream is transmitted to a server side for processing, an image processing result of the target image is obtained, and the server side is used for executing an image processing task for the target image. According to the technical scheme provided by the embodiment of the invention, by combining image semantics, the requirement of image transmission bandwidth is reduced, the data volume of semantic information is far smaller than that of pixel-level information, the transmission data volume can be greatly reduced, the image perception effect under an extremely low bit rate is realized, the intelligent perception analysis capability of an image understanding task is better supported, and the image understanding efficiency is improved. According to the invention, the technical problem that the image perception capability is insufficient under the condition that the bandwidth is limited in the prior art is solved.

The invention relates to a visual language detection method for detecting insects and pests based on open vocabularies, and the method comprises the steps: taking a to-be-detected image and the text description of the to-be-detected image as input, and extracting visual features and text features; and constructing a multi-modal feature fusion module, mapping the visual features and the text features to a unified semantic space, generating language-perceived visual features and image-perceived text features, and aligning the language-perceived visual features and the image-perceived text features in the unified semantic space by using a comparative learning mechanism. The system is designed for effectively detecting pests in the real world, and the key requirement of an adaptive pest recognition system is met; the multi-modal feature fusion module effectively integrates a visual language mode, and the performance of open vocabulary insect pest detection is remarkably improved by enhancing feature representation; regional cues show excellent generalization ability in an open vocabulary scene, thereby enabling more robust and flexible pest detection in different environments.

The invention provides an endoscope image sensing method and system based on self-supervised learning, and the method comprises the steps: constructing and training an endoscope image sensing model, obtaining a real-time endoscope image sequence pair, inputting the real-time endoscope image sequence pair into the endoscope image sensing model for sensing, outputting a predicted retracement endoscope image sequence, and outputting a predicted retracement endoscope image sequence. An image sequence matching mechanism based on deep learning is adopted, the predicted retracement endoscope image sequence and the real-time retracement endoscope image sequence are matched, difference evaluation and screening are carried out on the predicted retracement endoscope image sequence and the real-time retracement endoscope image sequence after matching, and a corresponding uncertainty evaluation result is generated; and performing interpretability prompting based on the uncertainty evaluation result, and optimizing a corresponding module of the endoscope image sensing system according to a feedback result of the interpretability prompting, thereby realizing efficient and accurate identification of the abnormal region in the endoscope image.

A method of generating foveated rendering using temporal multiplexing includes generating a first spatial profile for an FOV by dividing the FOV into a first foveated zone and a first peripheral zone. The first foveated zone will be rendered at a first pixel resolution, and the first peripheral zone will be rendered at a second pixel resolution lower than the first pixel resolution. The method further includes generating a second spatial profile for the FOV by dividing the FOV into a second foveated zone and a second peripheral zone, the second foveated zone being spatially offset from the first foveated zone. The second foveated zone and the second peripheral zone will be rendered at the first pixel resolution and the second pixel resolution, respectively. The method further includes multiplexing the first spatial profile and the second spatial profile temporally in a sequence of frames.

The invention provides a vehicle-mounted visual perception system and electronic equipment, and the system comprises a data collection module, an algorithm integration module and a communication assembly, and also comprises at least one of a data preview module, a video processing module and a video flow pushing module. The data acquisition module is used for acquiring image data and releasing the image data and acquisition completion time; the algorithm integration module is used for perceiving the image data by using at least one image perception algorithm, obtaining a perception result and perception completion time of the image data and issuing the perception result and the perception completion time; the data previewing module is used for synchronizing the image data and the sensing result according to the acquisition completion time and the sensing completion time, and forwarding the image data and the sensing result to a to-be-previewed device for display; the video processing module is used for converting the image data into a video stream in a preset coding format according to the acquisition completion time and releasing the video stream; and the video stream pushing module is used for pushing the packaged video stream.

A system and a method for medical image processing are provided. The method includes: obtaining a first medical image; obtaining a trained image perception restoration model; the trained image perception restoration model includes an image quality perception sub-model and an image restoration sub-model; and inputting the first medical image into the trained image perception restoration model to obtain a second medical image. The image quality perception sub-model is configured to determine either or both of a first quality evaluation value of the first medical image and a second quality evaluation value of the second medical image, the image restoration sub-model is configured to determine the second medical image, and the quality of the second medical image is higher than that of the first medical image.