50 results about "Smoking behavior" patented technology

The application discloses a smoking behavior detection model construction method based on a double-main network. The model is composed of five parts: a ROI main network, a space constraint network, a cigarette main network, a cigarette neck network and a cigarette detection head. Through migration learning, the ROI main network is used to obtain smoking posture features of various scales; the space constraint network is used to face the smoking posture feature maps of various scales and extract smoking posture masks; the smoking posture masks of different scales are used to perform spatial filtering on the cigarette features of various scales obtained by the cigarette main network; and the filtered cigarette features are output to the neck network and the detection head to output the coordinates and confidence of the cigarette target. By adopting the method, posture detection, image segmentation and target detection are fused; through the spatial semantic constraint of the context, the false detection rate of similar cigarette objects in the image is effectively reduced, and the detection accuracy is improved.

The invention relates to the technical field of indoor behavior detection, and discloses an indoor smoking behavior detection method and system based on transient mutation characteristics. The system comprises a transient sensing module which captures multi-source sensing data streams such as temperature, humidity, air quality and images in a target space in real time and accurately captures environment changes related to smoking; the environment modeling module constructs a dynamic behavior response model based on multi-source data, converts smoking detection into an environment state evolution process, can adapt to a complex indoor environment, and avoids false detection and missing detection; the behavior decision-making module takes a transient feature analysis algorithm as an engine, generates a sudden change sensing agent, identifies a smoking behavior by analyzing a data trend and feature combination, and reduces a misjudgment rate; the execution verification module rapidly executes intervention instructions such as ventilation equipment starting and alarm triggering according to the judgment result, seamless joint of detection and intervention is achieved, the harm of smoking is reduced, and an efficient scheme is provided for indoor smoke control.

The invention provides a smoking behavior detection system based on an edge device, and belongs to the field of computer vision, the smoking behavior detection system is deployed on a Jetson Nano edge computing device, and the smoking behavior detection system is used for detecting the smoking behavior through video frame acquisition processing, a lightweight spatial-temporal feature extraction and recognition model and a high-speed reasoning module using a TensorRT reasoning engine. And the real-time detection of the smoking behavior in the monitoring video is realized. The system firstly preprocesses a video frame collected by a camera, and performs feature extraction on a target area and judges a smoking behavior by using a time-space behavior recognition network improved by pruning redundant Token of input data. When a smoking behavior is detected, the system may generate alarm information and alarm voice locally at the terminal. The system has the advantages of convenient deployment, high real-time performance, local reasoning and the like, and is suitable for firework safety management in various public scenes.

The invention relates to the technical field of electronic cigarettes, in particular to a safety control method and system for an electronic cigarette. The method comprises the steps of obtaining a pre-stored matching template and multi-modal data input during user verification; the multi-modal data comprises smoking behavior data, and the matching template comprises a smoking behavior template; performing identity verification on the user according to the multi-modal data and the matching template, and if the verification is passed, sending a starting instruction to the electronic cigarette; after the electronic cigarette is started, whether the current smoking behavior data are matched with the smoking behavior template or not is judged, and if the current smoking behavior data are not matched with the smoking behavior template, a locking instruction or an instruction for requiring a user to perform verification again is sent to the electronic cigarette. According to the method, the use safety of the electronic cigarette can be ensured in the whole process.

This invention relates to the field of indoor behavior detection technology, and discloses a method and system for detecting indoor smoking behavior based on transient change characteristics. The system includes a transient sensing module that captures multi-source sensor data streams such as temperature, humidity, air quality, and images in the target space in real time, accurately capturing smoking-related environmental changes; an environment modeling module that constructs a dynamic behavior response model based on multi-source data, transforming smoking detection into an environmental state evolution process, adaptable to complex indoor environments, and avoiding false positives and false negatives; a behavior decision module that uses a transient feature analysis algorithm as its engine to generate a change-sensing intelligent agent, identifying smoking behavior by analyzing data trends and feature combinations, reducing the false positive rate; and an execution verification module that, based on the judgment results, quickly executes intervention commands such as starting ventilation equipment and triggering alarms, achieving seamless integration of detection and intervention, reducing the harm of smoking, and providing an efficient solution for indoor smoking control.

The invention discloses a second-hand smoke monitoring and controlling device in a closed space. The device comprises a smoke source intelligent identification sub-module, and an infrared human body detection sub-module, a particulate matter concentration detection sub-module, a multimedia intervention sub-module, an air purification sub-module and an air quality announcement sub-module which are respectively in communication connection with the smoke source intelligent identification sub-module, the smoke source intelligent identification sub-module adopts an algorithm based on smoke source fingerprint identification, accurately distinguishes different sources of tobacco smoke and non-tobacco smoke by analyzing particle size distribution of particulate matters generated by different smoke sources or a change trend of chemical components along with time, and performs time-space fusion on smoke source data and infrared data to confirm a smoking behavior; and generating a co-processing instruction, and associating the co-processing instruction to the multimedia intervention sub-module, the air purification sub-module and the air quality announcement sub-module. The invention further discloses a method for monitoring and controlling the second-hand smoke in the closed space by using the device.

The invention discloses a smoking behavior recognition method based on multi-source perception and particle size fingerprint analysis, which comprises the following steps: arranging a particulate matter sensor module, continuously collecting a multi-channel particle size spectrum in a target space in a non-optical mode, and generating a high-resolution particle size distribution sequence according to time; an infrared sensor module is arranged to monitor entering and leaving states of personnel in real time; the short-term change of the concentration distribution of the particulate matters with different particle sizes is calculated through a parameterization formula, and the accurate judgment of the smoking event is realized in combination with the personnel state and the PM2.5 speed increase in the same period. According to the invention, the infrared sensing data stream and the particulate matter concentration data stream are deeply analyzed, and the on-site state of personnel and the specific fluctuation mode of cigarette smoke are accurately identified; through cross cooperation verification of multi-dimensional features, interference of water vapor, mosquito-repellent incense and non-artificial smoke sources can be effectively eliminated, and unification of high detection precision and low false alarm rate is achieved. According to the method, imaging equipment is completely abandoned, and the risk of privacy disclosure is avoided from the source.

The invention provides a bimodal-based pedestrian smoking state recognition system and method. The system comprises an image acquisition module, a data preprocessing module, a feature extraction and fusion module and a smoking detection module. According to the method, the high-precision detection and judgment of the smoking behavior are realized by fusing the complementary sensing characteristics of the visible light and the infrared image. According to the method, a double-flow collaborative deep neural network structure is adopted, semantic features of a visible light image and temperature features of an infrared image are extracted respectively, a cross-modal channel attention mechanism is introduced into a feature layer, and self-adaptive fusion and space focusing of multi-modal information are achieved. According to the method, accurate geometric calibration is not needed, and end-to-end smoking detection and identification can be realized only by ensuring collection time synchronization.

The invention discloses a smoking habit data acquisition device. The smoking habit data acquisition device comprises a smoking set body, and a honeycomb-shaped ceramic heating body, a heating control module, a main control module and a lithium battery which are arranged in the smoking set body, the heating control module comprises a voltage control module and a current detection module, and the voltage control module applies a constant voltage to the ceramic heating body to enable the ceramic heating body to reach heat balance at the set temperature of 50-80 DEG C; the current detection module periodically collects the working current of the ceramic heating body and sends the working current to the main control module; the master control module comprises a microprocessor, a storage unit and a wireless connection module and is used for data processing, data storage and communication with external equipment. By means of the device, a relation curve of working current and time of the ceramic heating body during smoking can be obtained, and quantitative data analysis is provided for smoking behavior analysis.

The invention discloses a smoking behavior detection method and device, equipment and a storage medium, and relates to the field of image recognition, and the method comprises the steps: obtaining target video frames corresponding to a to-be-detected target event, extracting posture features based on each target video frame, and determining a target posture corresponding to each target video frame based on the posture features; performing time sequence mode matching on the target video frames based on the timestamps and the target postures corresponding to the target video frames to obtain corresponding mode matching results, and determining a target start frame and a target end frame when the mode matching results represent that the target event is a suspected smoking event; and determining a to-be-verified video frame and a corresponding dynamic region of interest based on the target attitude, and performing object detection on the dynamic region of interest by using a target object detection model to obtain a corresponding target detection result, so as to generate a smoking behavior detection result corresponding to the target event based on the target start frame, the target end frame and the target detection result. According to the invention, the misjudgment rate of smoking behaviors is reduced.

The application provides a smoking behavior recognition method and system, and relates to the technical field of computers.The method comprises the following steps: S1, acquiring a real-time video stream, calculating the video stream by using a deep learning target detection model efficientDet, and acquiring a region of interest of a person, wherein the region of interest comprises the position of a hand and the position of a mouth; S2, acquiring the position of a joint in the region of interest by using a hand joint model, and calculating joint position distribution information; and S3, comparing the joint position distribution information with existing standard hand-held cigarette palm joint distribution, judging whether the current palm action conforms to the palm joint position distribution when smoking, and if yes, recognizing the action as a smoking behavior; the region of interest of a person is acquired by using target detection, thereby reducing unnecessary detection, enabling better utilization of computing resources, and having stronger robustness and being suitable for various complex scenes.

The smoking behavior monitoring and early warning method comprises the following steps: pulling a monitoring video stream, and inputting the monitoring video stream into a monitoring model of a cascade architecture including lightweight face detection and cigarette detection; face detection is carried out on the monitoring video stream by using a lightweight face detection model, and the lightweight face detection model takes RetinaFace as a reference and adopts MobileNetV1-0. 25 as a backbone network; the obtained face area is input into a cigarette detection model, and the cigarette detection model takes YOLOv5s as a benchmark; and if the cigarette detection model detects a target cigarette, generating early warning information. Through a cascade architecture, a detection range is reduced from a whole image to a human face surrounding area, and accurate judgment and positioning of smoking behaviors in a high-density crowd place are realized.

The invention discloses a smoking target identification method based on context learning. The method comprises the following steps: S0, performing coarse screening by using skeleton extraction; the method comprises the following steps: S1, inputting an image into a pre-trained ResNet-101 network for feature extraction; s2, constructing a feature pyramid network, and fusing multi-scale features through a top-down path; s3, splicing to form a global feature map; s4, key features are enhanced through an scSE module; s5, candidate areas are generated and screened; s6, performing context alignment on the candidate areas; and S7, performing classification and regression. The false detection rate and the omission rate in a complex scene can be reduced, and high-precision real-time smoking behavior recognition can be realized as far as possible.

The invention relates to the technical field of civil engineering safety monitoring, and particularly discloses a civil engineering site fire risk intelligent assessment system and method. The system comprises an image module, a smoke module and an AI module, the image module comprises a first camera and a second camera which are used for collecting image information of a construction site from different angles, and the smoke module comprises a plurality of smoke sensors which are used for sensing smoke signals in a specific area of the construction site. And the AI module comprises a position identification sub-module, a behavior identification sub-module and a smoke feature analysis sub-module, and is used for performing multi-dimensional fusion processing on the acquired images and smoke signals, and performing intelligent identification and risk assessment on the smoking behavior of the construction personnel based on a set risk scoring algorithm. According to the method, sensing signal triggering, image behavior analysis and multi-source information matching are combined, a set of three-dimensional and real-time smoking recognition and early warning mechanism is constructed, and the intelligent management level of construction site fire safety can be effectively improved.

This application provides a method, system, device, and storage medium for identifying smoking behavior in industrial parks. The method includes: acquiring video stream data from park surveillance; decoding the video stream data to obtain image frames; performing human target detection on the image frames to determine the location of each human body within the image frames; cropping sub-images containing the human bodies from the image frames based on their locations; performing smoking behavior recognition on the sub-images to determine whether the human body in the sub-image is smoking; and outputting the smoking behavior recognition results for each human body in the image frames. This method, through a two-stage processing architecture of first human detection and then region cropping, transforms the problem of identifying small-target smoking behavior in a large-scale park surveillance scenario into a refined behavior discrimination problem at a normalized scale. This effectively alleviates the contradiction between sparse features of small targets and background interference, improving the balance between recognition accuracy and computational efficiency.

The invention provides a smoking hypertension patient health monitoring and control method, device and equipment, a medium and a program product, and relates to the technical field of health data processing, the method is applied to an intelligent mobile terminal, and the method comprises the following steps: converting user complaint information into structured data including life time, symptoms and mood feeling; based on the structured data and physiological data including blood pressure data collected by the wearable device, integrated health data displayed by a time axis is generated; determining smoking habit data based on the time and the place corresponding to the structured data; inputting the smoking habit data and the integrated health data into a natural language generation model to generate a chief complaint record; and performing classification and grading health management strategies through an AI algorithm based on the physiological data. Smoking behavior analysis and hypertension health monitoring are fused, personalized and dynamic health management of smoking hypertension patients is achieved, the accuracy of health state monitoring results of the patients is improved, and effective medical assistance is achieved for chronic diseases.

The utility model discloses a wrist wearing type smoking cessation instrument, and relates to the technical field of smoking cessation instruments. A display screen, a setting button and an alarm are arranged on the front side wall of the smoking cessation instrument main body, a smoke detection sensor and a thermal infrared imager are arranged at the left side end of the smoking cessation instrument main body, a charging socket is formed in the right side end of the smoking cessation instrument main body, and a control module, a GPS positioning module and an equipment power supply are arranged on the inner side of the smoking cessation instrument main body. The smoke detection sensor can monitor the smoke concentration around the user in real time, and when smoke is detected, the user is reminded through the alarm, so that the user can timely realize the smoking behavior and correct the smoking behavior; and the thermal infrared imager can monitor the change of local temperature at the hand position of the user and assists in judging whether the user is smoking or not, so that the smoking cessation effect is further enhanced. The electrode slice on the rear side wall of the smoking cessation instrument body is matched with the conductive gel slice, the wrist of the user can be slightly electrically stimulated through the electrical stimulation module, and reminding and intervention are given when the user smokes.

The present application belongs to the technical field of smoking monitoring, and particularly relates to an indoor smoking management method based on infrared monitoring. The indoor smoking management method based on infrared monitoring comprises at least the following steps: a monitoring step of monitoring the ambient temperature in a monitoring range in real time; a smoking judgment step of regarding a smoking event as occurring when a high temperature point higher than a preset smoking temperature point is judged to exist; a judgment confirmation step of continuously performing the real-time monitoring step to obtain the motion trail of the high temperature point after the high temperature point occurs, and performing reconfirmation of the smoking behavior; and a notification step of performing alarm and recording the smoking event after the smoking event is judged and confirmed to occur. The indoor smoking management method based on infrared monitoring can accurately judge the occurrence of the smoking behavior through infrared heat detection, image analysis and behavior judgment, and timely perform alarm processing.

The application discloses a smoking behavior detection method and device, equipment and storage medium, and relates to the technical field of image recognition. The method comprises the following steps: acquiring a pedestrian area in a to-be-detected image; performing smoking detection on the pedestrian area by using a fusion model obtained by fusing multiple models; the fusion model comprises a pedestrian posture recognition submodel, a cigarette recognition submodel and a pedestrian gesture and mouth shape recognition submodel; and whether there is a smoking behavior in the to-be-detected image is judged according to the recognition result of the submodel in the fusion model. By using the detection mode of multiple model fusion, comprehensive judgment is performed on the image features of the cigarette, the posture and action of the person and the gesture and mouth shape of smoking. Compared with a single detection model, the accuracy of smoking detection is greatly improved, and the object of smoking detection is the pedestrian area in the to-be-detected image, that is, the cigarette detection is performed only in the area where the pedestrian is located, so that background interference is avoided, and the situation of missing report or false report is avoided.

The application relates to a smoking identification method and a related device, which comprises the following steps: obtaining a target image to be processed, and extracting a candidate region image from the target image based on preset region parameters; performing image correction processing on the candidate region image to obtain a target region image; inputting the target region image into a pre-trained target detection model to obtain at least one candidate detection frame and a confidence corresponding to the candidate detection frame; judging whether a target detection frame meeting a target constraint condition exists in the candidate detection frame, and if the target detection frame exists, determining that a smoking behavior exists in the target image to be processed, and generating an effective region image. The scheme provided by the application can pre-process the image to be detected, and then input the image into a target detection model for detection, so as to judge whether a smoking behavior exists in the image to be detected, improve the identification precision and efficiency of the smoking behavior, and reduce the false detection and missed detection.

The present disclosure provides a smoking behavior recognition method, a smoking detection model, a device, a vehicle and a medium, and relates to the technical field of image recognition. The method comprises the following steps: determining a fusion weight of a person image in a position of a driver in a vehicle cabin according to an image display parameter, determining an actual distance between a mouth of the person in the person image and a cigarette, generating a fusion image feature according to the fusion weight and the actual distance, classifying a smoking behavior of the driver according to the fusion image feature, and obtaining a recognition result of whether the smoking behavior exists. The present disclosure can improve the detection accuracy of whether the smoking behavior of the driver exists, and is beneficial to reduce the false detection rate of the smoking behavior.

The invention discloses a smoking behavior identification method and device, a smoking set and a storage medium. The method comprises the following steps: acquiring output information of a smoking set in each unit time, and determining first reference information corresponding to each unit time; based on the first reference information corresponding to each unit time, determining a first identification condition for the output information of each unit time; determining second reference information based on the output information of the plurality of first unit times under the condition that the output information of the plurality of continuous first unit times meets a first identification condition; and based on the second reference information, judging the output information of each unit time after the first unit time according to a second identification condition, and under the condition that the output information of the second unit time meets the second identification condition, determining that an effective smoking behavior of the smoking set is detected. The accuracy of effective suction behavior recognition is improved by judging the rising feature and the falling feature of the output information of each unit time.

The invention discloses a smoking behavior detection method based on YOLOv5s and a robot, and belongs to the field of target detection.The smoking behavior detection method comprises the steps that a smoking behavior data set is collected, and the collected data set is labeled through labmeli; dividing the labeled data set to generate a training set, a verification set and a test set; a data set optimization strategy is adopted, and a Yolov5s recognition model is trained according to the generated training set, verification set and test set; the method comprises the following steps of: deploying a trained Yov5s identification model on robot equipment, deploying TensorRT in the robot equipment, acquiring field image data through a camera on a robot, performing real-time reasoning detection by using the deployed YOLOv5s-TensorRT, and transmitting a processing signal to an MCU (Microprogrammed Control Unit) through a UART (Universal Asynchronous Receiver / Transmitter) when a smoking behavior is detected so as to trigger a buzzer to give an alarm. The problems that existing smoking monitoring is poor in flexibility, cloud computing performance is bottleneck, and a complex dynamic environment cannot be automatically adapted are solved.

This invention belongs to the field of heated cigarette technology, specifically relating to a control method for heated cigarettes. The adaptive temperature control method for the pre-smoking stage includes: determining the user's required smoke volume based on the user's smoking depth and duration during the smoking stage; simultaneously determining the input power of the temperature controller for the smoking stage based on the user's required smoke volume; and adaptively calculating the output power of the temperature controller for the pre-smoking stage, so that the required smoke volume can be rapidly generated according to the user's smoking behavior. The adaptive temperature control method for the smoking stage includes: adjusting the output power of the heating element based on the user's required smoke volume calculated by the data analyzer using differential pressure data. This invention transforms consumer smoking behavior characteristics into a time pointer in the control method, which is beneficial for the targeted design of heating programs such as preheating programs, smoking process heating programs, and smoking interval heating programs, and also provides technical support for personalized customization of heated cigarettes.

This application provides a behavior recognition method and electronic device to address the problem of inaccurate recognition of smoking behavior in existing technologies, which poses a safety hazard. In this application embodiment, after acquiring an image to be recognized, the electronic device determines the target distance between the target in the image and the acquisition device, detects targets related to the target behavior, and adjusts the focal length of the acquisition device according to the target distance. This results in a clearer image of the target, with its enlarged area within the image. Behavior recognition is then performed based on this target image, accurately identifying the presence of the corresponding behavior and reducing safety risks.

The utility model belongs to the field of intelligent monitoring, and provides a multifunctional smoking behavior monitoring device which comprises an installation support, a machine shell with an image acquisition window and a smoke detection window is arranged on the installation support, and an image acquisition system, a smoke acquisition module and a main control module are arranged on the machine shell. The image acquisition system comprises a thermal imaging module and a visible light camera module; through cooperative work of the smoke collection module, the thermal imaging module and the motion capture module, monitoring is performed from multiple dimensions of smoke, temperature and motion, compared with a traditional smoke alarm, the accuracy of smoking behavior monitoring is improved, the false alarm rate is reduced, the face image of a smoker can be captured through the face recognition module, and the smoke alarm is more intelligent. The method can accurately position the identity of the smoker, and mainly monitors the smoker with the smoking history, thereby facilitating the afterward tracing and management, and effectively restraining the illegal smoking behavior.

The invention discloses a smoking hidden danger identification method, device and system, and the method comprises the steps: obtaining multi-source image data, carrying out the preprocessing, obtaining a training data set, inputting the training data set into a deep learning model containing a path aggregation network, generating a multi-level feature map, executing the sparse anchor point screening, completing the model training, and obtaining a smoking hidden danger identification result. Obtaining an initial smoking hidden danger identification model; regularly collecting real-time smoking behavior images as incremental data, fusing the incremental data with the training data set to obtain an iterative data set, and performing lightweight fine tuning on the small target detection head of the initial smoking hidden danger recognition model to obtain an updated smoking hidden danger recognition model; and inputting a real-time image stream of an industrial site into the updated smoking hidden danger recognition model, executing reasoning analysis, and outputting a smoking hidden danger recognition result. According to the method, the small target detection rate is improved through the multi-level feature map, and the accuracy and stability of smoking hidden danger recognition are improved.

The application discloses a one-way series YOLOV5 network small target detection method. The method adopts two series YOLOV5 as a detector to obtain the target detection result in a video scene, and detects the smoking behavior of a video stream through two layers of YOLOV5 network. The first YOLOV5 network is responsible for preliminary screening, and detects the input video frame in real time to screen out the class smoking behavior target. The second YOLOV5 network is responsible for fine screening, and detects the input class smoking behavior target again to accurately detect the smoking behavior target existing in the video frame. The application improves the network detection accuracy and detection speed while reducing the volume and parameter quantity of the network, realizes higher target detection accuracy and accuracy through the series YOLOV5 network, improves the false detection and missed detection problems caused by the insufficient target feature extraction capability in the existing network, and improves the detection effect.

This invention discloses an intelligent detection method for smoking behavior in no-smoking areas based on target detection and image segmentation. The method involves scaling up the image / video frames of the no-smoking area to be detected, and then preprocessing the scaled images using a standardized approach. A YOLO11 target detection model is then constructed to detect smoking-related targets in the preprocessed no-smoking area images, generating detection images. Rectangular regions containing suspected cigarettes and related human hands are cropped from the detection images and input into a SAM image segmentation model for optimization. Simultaneously, the center coordinates of the suspected cigarettes are also input into the model, achieving pixel-level precise segmentation of the cigarettes, hands, and smoke, extracting accurate target contour features. Finally, by combining spatial relationships and feature judgment rules, the final identification and determination of smoking behavior is completed.