91 results about "Video sensors" patented technology

The present invention discloses a safety risk analysis system and method based on multimodal data processing; it relates to the field of industrial automation and information technology; the purpose of the present invention is to specifically target high-risk scenarios of hazardous chemical enterprises, and provide a safety risk analysis system and method based on multimodal data processing, which is based on deep learning technology, comprehensively considers the changes in spatiotemporal characteristics, can monitor and evaluate the safety risks of hazardous chemical enterprises in real time, and automatically integrates multimodal data such as monitored videos, sensor data, and operation logs. The spatial features are extracted through convolutional neural networks, and the time series data is processed using recurrent neural networks and long short-term memory networks, and further feature extraction and fusion are performed. The key risk factors are also identified through the attention mechanism, thereby achieving a comprehensive and accurate safety risk assessment, thereby providing real-time warnings for safety managers to ensure that potential safety hazards are discovered in a timely manner.

Video inspection systems are disclosed. In one embodiment a video camera for use in a pipe inspection system includes a camera head including an outer housing, a camera module assembly including video and non-video sensors, a processing circuit, and a data link receiver.

Disclosed in the present application are a method and apparatus for identifying a traffic behavior, and a storage medium and an electronic device. The method comprises: acquiring a radar image on the basis of a collected signal of a radar sensor, and acquiring a video image on the basis of a collected signal of a video sensor; performing feature extraction on the radar image, performing feature extraction on the video image, and performing feature fusion on a feature extraction result of the radar image and a feature extraction result of the video image, so as to obtain a fused feature; performing traffic target detection on the basis of the fused feature, so as to obtain a detection result in a real physical coordinate space; performing traffic target tracking on the basis of the detection result in the real physical coordinate space, so as to generate a target queue trajectory of the traffic target; and identifying a target traffic behavior on the basis of the target queue trajectory.

The invention relates to an intelligent inspection control system and method. The system comprises an environment sensing module, a situation recognition module, a task management and scheduling module, a path planning module, an execution control module and a communication linkage module. The system can collect various environmental parameters of an inspection area in real time, dynamically adjust the priority of an inspection task after identifying an abnormal state, and generate an optimal path to drive a robot to execute response operation; meanwhile, abnormal information, images, videos, sensor snapshots and other contents are uploaded to a background management platform through a communication linkage module, and sound-light alarm equipment, broadcast equipment and other equipment are linked to carry out field response. The control method has task preemption and path reconstruction capabilities, and supports distributed sensing deployment and whole-process execution state monitoring. Compared with the prior art, integrated fusion of environment situation awareness, task dynamic scheduling and response control is achieved, and the adaptive capacity, the response speed and the safety guarantee level of the system to complex scenes are improved.

The invention provides an intelligent identification and real-time alarm system for video monitoring. The intelligent identification and real-time alarm system comprises a video sensor, a real-time preprocessing module, a deep learning module, a risk assessment module and an early warning module, the video sensor collects video information in an environment and sends the video information to the real-time preprocessing module, and the video sensor adopts one of a network camera IPC, an analog camera AVC and an NVR video processor; the real-time preprocessing module preprocesses the video data, the preprocessing comprises clipping, size adjustment, enhancement and denoising, standard video image formation, resolution and frame rate unification and video noise reduction, and the standard video image obtained after preprocessing is sent to the deep learning module; and the deep learning module performs target identification, target detection and abnormal behavior detection on the standard video image through a deep learning model, and analyzes the video according to the output detection target and the detection abnormal result.

The invention relates to a non-contact sleep monitoring method based on cross-modal compensation. In order to overcome the defects that vital sign radar is single in physiological information and prone to being interfered, the method is achieved through multi-modal fusion of a radar sensor, a video sensor and an audio sensor. Specifically, a radar sensor obtains heartbeat, respiration and body movement physiological information through chest mechanical waves, a video sensor obtains heartbeat, respiration and body movement physiological information through a photoplethysmography (PPG), an audio sensor receives sound and the like, feature parameters are extracted, a ReliefF algorithm is used for feature optimization, a machine learning algorithm (Subspace KNN, Bagged Tres and the like) is adopted for classification, and the feature parameters are extracted. And finally, decision-level fusion is carried out through a naive Bayes classifier, and a sleep staging result is output. Experimental results show that the method is robust in a complex environment, the accuracy and reliability of sleep staging can be remarkably improved, cross-modal compensation is achieved, and measurement is more continuous and accurate.

The invention discloses a multi-target detection method based on a multi-source high-frequency track and a high-precision map, which realizes conversion of a video coordinate system, a millimeter wave radar coordinate system and a world coordinate system and fuses a radar target and a video target. The method comprises the following steps: automatically calibrating video and thunder vision; performing automatic deviation correction on a north corner and an equipment position based on multi-straight-line regression; transforming the video sensor into a world coordinate system; and carrying out decision association of the Leiyu tracking target. According to the method, the target trajectory and the high-precision map data are tracked by utilizing the thunder vision high frequency, the complexity of calibration between the sensors and between the sensors and a world coordinate system is reduced, meanwhile, the precision errors of coordinate transformation between the thunder vision sensors and thunder vision data world coordinate transformation are reduced, and compared with the prior art, the method has the advantages that in the application of complex traffic roadside vehicle detection scenes, the detection efficiency is improved. The precision is higher; and the data association has robustness.

The invention discloses a method for continuously improving the potential safety hazard identification capability of third-party construction, and belongs to the technical field of safety monitoring and artificial intelligence. The method comprises the following steps: acquiring multi-dimensional dynamic data such as a video and a sensor of a construction site, performing real-time hidden danger identification based on the data to determine a current hidden danger state, predicting a future risk upgrading probability through a risk evolution prediction model, and then fusing the current hidden danger state with the future risk upgrading probability to obtain a risk upgrading result. According to the method, the problems of alarm lag and low accuracy in the prior art are solved, the conversion from passive response to active early warning is realized by introducing risk evolution prediction and a dynamic alarm decision mechanism, and the risk evolution prediction and dynamic alarm decision mechanism is improved. And the system is endowed with a self-evolution capability through a data closed loop, so that the accuracy, the foresight and the intelligent level of safety management and control are remarkably improved.

The invention relates to a big data-driven public security situation awareness and evaluation system and model, which can comprehensively integrate various information such as social media, surveillance videos, sensors, government databases and the like through a multi-source data dynamic weighted fusion algorithm, and adopts an improved hidden Markov model (IHMM) and the like to construct a public security situation awareness model. The dynamic change process of the public security event can be effectively processed, and the occurrence probability and the possible development state of the public security event can be accurately predicted through learning of the state transition probability matrix and the observation probability matrix. Compared with a traditional method based on experience or simple statistics, the method has higher accuracy and adaptability in the face of complex and changeable public security situations.

The invention discloses a video-radar-radio frequency multi-source data fusion analysis system based on multi-modal perception and deep learning. A video stream preprocessing module adopts ACNN of an attention mechanism to carry out key frame extraction on a video stream and realize target detection and classification; the radar signal processing module completes radar echo signal feature learning and target trajectory prediction through fusion of VAE and LSTM; the radio frequency signal analysis module constructs an RFID tag association model based on GNN to extract identity information. The data quality of a video sensor, a radar sensor and a radio frequency sensor is dynamically evaluated through an AFM, the multi-source feature weight is autonomously adjusted to achieve high-robustness fusion, and space-time matching of a target track-identity is completed in combination with three Gaussian hybrid clustering and a double-dynamic mapping algorithm. According to the method, the problem of target mismatching caused by insufficient cross-modal feature interaction is effectively solved, the target recognition precision and identity binding continuity in a complex scene are improved, and the tracking stability is kept when the sensor data is dynamically degraded.

A method for automatically controlling an in-cabin environment for a passenger in an autonomous vehicle includes monitoring a state of the passenger via a video sensor; determining whether the state of the passenger corresponds to one of preset states of a predetermined number; and adjusting, when the state corresponds to the one of the preset states, any one or any combination of any two or more of a seat, an air conditioning system, a lighting system, a sound system, a variable window tinting, and a variable sunroof of the autonomous vehicle based on the state. The determining of the state of the passenger includes detecting a plurality of determination target objects from a video of the video sensor, cropping an image of each of the plurality of determination target objects, and determining whether the state of the passenger corresponds to the one of the preset states.

A smart home monitoring device is configured to plug into an electrical outlet and includes a multi-sensor array, excluding audio and video sensors, for monitoring environmental conditions, behavioral patterns, and odor-related indicators within a monitored environment. The device includes Bluetooth Low Energy communication with angle-of-arrival capability to enable sub-meter asset tracking, and supports home automation control through programmable automation frameworks such as IFTTT or Node-RED to initiate safety responses. The system further provides multi-tiered burglar detection through correlated event analysis across multiple sensor inputs, and generates health-related risk indicators based on detected patterns. Visual indicators, including multi-color LED illumination and blinking patterns, are used to communicate alert types and severity levels. The system enables non-intrusive monitoring while supporting proactive safety, security, and health management.

The invention discloses a phased array, TOFD and magnetic powder integrated detection system and method for medium and large diameter pipeline welded joints, the detection system comprises a control system, a liquid storage and supply system and a scanning system, the control system is connected with the liquid storage and supply system, the control system is connected with the scanning system, and the scanning system is connected with the control system. The liquid storage and supply system is connected with the scanning system through a flexible water pipeline and a flexible magnetic suspension pipeline; the liquid storage and supply system comprises a magnetic suspension tank and a water tank which are respectively used for storing magnetic suspension and water; the scanning system is a data acquisition center, is provided with a phased array probe, a TOFD probe, an electromagnetic yoke and a video sensor in a centralized mode, operates in a sub-control mode, is driven by a magnetic attraction wheel driving mechanism to achieve automatic scanning of a welding joint, and transmits detection data information to the control system. The device is compact in structure, convenient for later maintenance and low in manufacturing cost; according to the invention, the parameter control is accurate, the detection data can be analyzed in real time, the data interpretation is convenient, the detection precision is high, the detection speed is fast, and safety and reliability are realized.

An inside cabin sensor system, having an integrated, single hardware part comprising a camera sub-system and an integrated radar sub-system is proposed. A proposed system provides related information for covering the following applications inside a vehicle: Child Presence Detection (CPD), Driver Drowsiness & Fatigue (F), Driver Distraction (DD) as a safety-relevant function, complemented by Intrusion & Proximity Alert (IPA), Seat Occupancy Detection (SOD), Face Recognition (FR), and optional applications such as Driver Emotion (ES), Passenger Classification (PC), Airbag Suppression (AS), Airbag Activation (AA), Mobile Phone Detection (MP), Gesture Detection (GD) and Vital Signs Detection (VS). A proposed system utilizes Artificial Intelligence (AI)-related data processing for data processing, using radar point cloud data, calculated by said radar sensors. The proposed system utilizes Artificial Intelligence (AI)-related data processing for data processing, using video-captured data. The proposed system utilizes Artificial Intelligence (AI) -related data processing for sensor data fusion, using radar sensor and video sensor data.

Systems and methods for utility infrastructure condition monitoring, detection, and response are disclosed. One exemplary system includes a sensor package and a monitoring and control module. The sensor package includes a plurality of sensors such as, for example, an image sensor, a video sensor, and a LiDAR sensor. The sensors may each be configured to capture data indicative of one or more conditions (e.g., an environmental condition, a structural condition, etc.) in the vicinity of the utility infrastructure. The monitoring and control includes a detection module and an alert module. The detection module is configured to receive data captured by each sensor and, based on the captured data, determine one or more conditions in the vicinity of the utility infrastructure. The detection module may be configured to then, based on the determined conditions, provide an alert for the condition using the alert module.

The invention discloses a station building safety risk online assessment system based on an edge AI large model, which relates to the technical field of station building safety systems and comprises a data acquisition module, a large model reasoning module, an edge calculation module, a cloud collaboration module, a risk assessment module and a user interaction module. The multi-modal fusion module can perform fusion analysis on multi-modal data such as videos, sensors, texts and the like to form comprehensive risk assessment, the multi-modal fusion module adopts dynamic weighting, utilizes an attention mechanism and dynamically distributes weights according to the importance of modals, and through the design, the working personnel can perform multi-modal risk assessment according to the current time period, the working state or external factors. In addition, the multi-modal fusion module supports adaptive sampling and dynamic updating in the use process, and the data sampling frequency of the data acquisition module is dynamically adjusted according to the risk level, so that the flexibility of the system is further improved.

Systems and methods for utility infrastructure condition monitoring, detection, and response are disclosed. One exemplary system includes a sensor package and a monitoring and control module. The sensor package includes a plurality of sensors such as, for example, an image sensor, a video sensor, and a LiDAR sensor. The sensors may each be configured to capture data indicative of one or more conditions (e.g., an environmental condition, a structural condition, etc.) in the vicinity of the utility infrastructure. The monitoring and control includes a detection module and an alert module. The detection module is configured to receive data captured by each sensor and, based on the captured data, determine one or more conditions in the vicinity of the utility infrastructure. The detection module may be configured to then, based on the determined conditions, provide an alert for the condition using the alert module.

Source: FR2832733 A1 Video sensors are used for controlling melting of aluminum in a furnace by producing images of the bath surface or the inner surface of the furnace walls. Melting of aluminum in a furnace comprises: (a) introducing solid aluminum into the furnace; (b) melting the aluminum to form a molten bath; (c) production of images of the bath surface or of the inner surface of the furnace (2) walls; (d) treating these images to identify the presence of solids or the formation of oxides on the bath surface or for identifying the presence of hot spots or hot zones or zones having a certain minimal temperature on the fumace walls; and (e) modifying at least one fumace operating condition as a function of this treatment of the images. Independent claims are also included for the following: (a) a device for detecting the surface state of a bath of aluminum or the state of the furnace walls in an aluminum melting furnace; and (b) an aluminum melting furnace incorporating this detection device.

The invention relates to a device and a method for detecting the pressure-bearing strength of a brazed plate heat exchanger, and relates to the technical field of detection of the pressure-bearing strength of the brazed plate heat exchanger. The device comprises a supporting frame, a protection frame movably arranged on the supporting frame, a video sensor fixed in the protection frame, a clamping piece used for clamping the heat exchanger, an inflation assembly used for inflating four air inlet pipes on the heat exchanger and a paint spraying assembly used for conducting paint spraying treatment on the outer side of the heat exchanger. The inflation assembly comprises three inflation pipes fixed to the protection frame, four rotating discs rotationally arranged in the protection frame, three sealing pipes fixed to the four rotating discs correspondingly, an inflation piece used for inflating the sealing pipes, and a switching piece used for switching and opening the three inflation pipes. And the rotating piece is used for synchronously rotating the four rotating discs. The detection gas, the sealing pipe and the paint spraying amount can be rapidly switched according to different types of heat exchangers, and the operation complexity is reduced.

Disclosed are systems and methods for determining target characteristics during a laser procedure, comprising (i) obtaining a relationship between (i) a number of pixels associated with a light beam reflected from a target or an object located in proximity to the target on an endoscopic image obtained from a video sensor coupled to an endoscope and (ii) a distance of the target from a tip of the endoscope. The method further comprising (ii) measuring the number of pixels associated with the light beam reflected from the target or the object located in proximity to the target during a procedure, and (iii) based at least in part on the relationship obtained in step (i) and the measured number of pixels in step (ii), determining at least one of a size of the target or a distance of the target from the tip of the endoscope.

Operation of a solid fuel burning device can be monitored using a video sensor located remote from the solid fuel burning device and at least another sensor. The video sensor can record video data that monitors actions and behavior related to the solid fuel burning device. The other can record at least temperature data related to the solid fuel burning device. A controller can: detect an occurrence related to an object in at least the video data; create an identified occurrence; determine a context of the identified occurrence based on a stage of a fire cycle of the solid fuel burning device; and characterize the identified occurrence in the context as satisfactory or sub-optimal, such that: when the identified occurrence in the context is satisfactory, continue normal operation; and when the identified occurrence in the context is sub-optimal, send a message to a user of the solid fuel burning device.

The invention provides a self-adaptive remote communication system of a long-distance heat supply network inspection robot, which comprises a multi-mode communication module, a pipeline special antenna, an intelligent relay unit and a self-adaptive control module, and is characterized in that the multi-mode communication module is connected with a main controller of the inspection robot; the special pipeline antenna is connected with the multi-mode communication module; the special pipeline antenna is externally connected with a monitoring center through the intelligent relay unit; the self-adaptive control module is connected with the multi-mode communication module and is used for acquiring the signal strength of each communication system and executing a communication system strategy; according to the invention, high-speed and stable transmission of data is realized, real-time return of key information such as inspection videos, sensor data and the like and timely issuing of instructions are guaranteed, and the overall inspection efficiency and the system response speed are improved.

The invention discloses an automatic calibration method for thunder-vision fusion data, and relates to the field of intelligent traffic and automatic driving, and the method comprises the steps: obtaining radar data containing a plurality of moving targets and video data corresponding to the radar data, respectively extracting the trajectory data of the plurality of moving targets, and obtaining a radar trajectory set and a video trajectory set; track feature points are extracted based on the radar track set and the video track set, and a radar inflection point set and a video inflection point set are obtained; matching the radar inflection point set with the video inflection point set to obtain an association point pair set; and determining a coordinate conversion relation and a time relation between the radar data and the video data according to the association point pair set, and completing automatic calibration of the thunder-vision fusion data. According to the method, full-automatic space-time calibration of the millimeter wave radar and the video sensor can be realized, manual field operation and road closing are not needed, the calibration precision is improved through track feature matching, and long-term adaptive optimization is supported to maintain the fusion precision.

The invention provides a ship collision early warning method and device and a storage medium, and belongs to the technical field of ship early warning, and the method comprises the steps: importing the dynamic information of a moving target around a ship, obtaining an original ship environment image from a multi-channel video sensor disposed on the periphery of the ship, and obtaining original distance data from a laser radar; performing fusion analysis on each original ship environment image and each original distance data to obtain a target panoramic video and preprocessed distance data; and performing collision analysis on the target panoramic video, the preprocessed distance data and the dynamic information to obtain collision data. According to the invention, the perception, detection and collision avoidance early warning capabilities of the periphery of the ship are greatly improved, full-view coverage is realized, the problem of existence of a monitoring blind area is solved, meanwhile, fusion of multi-source data is realized, the target identification precision is improved, the probability of misjudgment and missing judgment is reduced, and the intelligence of collision early warning is realized.

The invention relates to light-weight cabin shell casting equipment and a casting process, and relates to the technical field of casting equipment. The device comprises a casting tank, a defoaming tank fixed to the casting tank, a casting pipe arranged on the casting tank, a video sensor used for detecting the model of a mold, a distance sensor used for detecting the amount of molten metal in the casting tank, a heat preservation assembly used for conducting heat preservation on the molten metal in the casting tank and a casting assembly used for conducting quantitative casting on the molten metal. The casting assembly comprises three liquid outlet pipes communicating with the casting tank, three agent outlet pipes communicating with the defoaming tank, liquid outlet plates movably arranged on the three liquid outlet pipes correspondingly, and control valves fixed to the three agent outlet pipes correspondingly. The casting tank has the advantages that the molten metal is defoamed and optimized, the discharging speed of the molten metal and the defoaming agent can be controlled according to the model of the mold, the liquid discharging speed of the molten metal and the defoaming agent is prevented from being affected by the static pressure head as much as possible, and the casting effect of the casting tank on multiple molds of different models is guaranteed.

The application discloses a vehicle holographic perception and risk behavior identification system based on deep fusion of laser vision multi-source data, which comprises a laser vision fusion vehicle information perception layer integrated with a roadside laser radar, a video sensor and a high-definition snapshot camera; an edge computing layer of vehicle holographic identification and risk behavior identification based on an edge computing server; a cloud center processing server layer based on intelligent gantry cooperation and fusion operation; and an application software layer integrated with vehicle holographic perception, high-precision vehicle trajectory tracking, vehicle risk identification and statistical analysis. The application has the advantages that laser radar information and video image information can be fused, vehicle risk behavior identification in a complex road scene can be realized, corresponding vehicle holographic perception data can be obtained according to the risk behavior identification result, risk behavior area statistical analysis and risk vehicle tracking can be realized, and the road safety monitoring system can be provided with high-precision and high-reliability vehicle information perception and risk behavior identification functions.

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for automatic creation of work charts.

The application discloses a kind of dynamic video remote monitoring management methods, it is related to image processing technical field, including: establishing monitoring edge device history transmission database, the historical resource utilization state of each monitoring edge device is analyzed, the resource demand of each edge device is predicted;Determine the maximum carrying capacity of the resource of each edge device, the resource load state of each edge device is calculated;Based on the resource demand of each edge device, the resource load state of each edge device and the video stream of historical transmission data, initialize the video transmission code rate of each edge device;Real-time transmission data of each video sensor is obtained, according to the resource demand and resource load state of each edge device, calculate video stream processing cost index;Based on video transmission code rate and video stream processing cost index, update the video transmission code rate of each edge device;The application has the advantages that: optimize video transmission quality, improve system monitoring overall performance.

The present invention relates to the technical field of coal mine belt conveyor operation monitoring, and in particular to a method and system for detecting the status of a conveyor belt of a coal mine belt conveyor based on image recognition. The method comprises: installing a video sensor on a gantry of the conveyor; first, using the video sensor to capture a video of the belt surface area of the conveyor belt in real time; and capturing the video frame by frame to obtain multiple frames of continuous images of the coal mine conveyor belt; then, the video sensor analyzes and processes the coal mine conveyor belt images; and finally, the video sensor displays the processed coal mine conveyor belt images, wherein the processed coal mine conveyor belt images include information on whether there is coal on the conveyor belt and whether the conveyor belt is operating. The present invention provides a method for detecting the status of a coal mine belt conveyor based on image recognition, which is reliable, has good real-time detection performance, is easy to maintain, is low in cost, and is convenient for practical application in coal mine scenarios.

The present disclosure relates to systems and methods for operating one or more self-checkout (SCO) terminals in a self-checkout (SCO) environment. The system includes one or more video sensors configured to capture a plurality of video frames. The video frame is processed by a processing unit to detect a primary object of interest and to detect a secondary object of interest after detecting the primary object of interest. Further, changes in occurrence locations and occurrence times of the primary object of interest and the secondary object of interest are determined, thereby generating a motion trigger. Based on the motion trigger, transaction data is received, which is compared to the detected secondary object of interest. A non-scan event alert is generated based on a mismatch in the comparison of the transaction data to the detected one or more secondary objects of interest.