105results about How to "Real-time recognition" patented technology

The invention provides a plate structure impact load positioning method based on a distributed fiber bragg grating sensing network, and belongs to the field of impact monitoring of structure firmness monitoring. The method includes the steps of firstly, arranging the distributed fiber bragg grating sensing network; secondly, collecting an impact response signal; thirdly, extracting feature information and analyzing sensitive characteristics of an FBG sensor; fourthly, positioning the position of an impact point through the association algorithm of the maximum peak amplitude ratio of the impact response signal and position information. The positioning algorithm is simple, rapid, convenient and reliable and is high in instantaneity.

The invention belongs to the technical field of agriculture, in particular relating to a DSP (Digital Signal Processor)-based rice weed recognition system and method. The DSP-based rice weed recognition system consists of an image acquiring module, a rice weed recognition module and an image displaying module, wherein the image acquiring module and the image displaying module are connected with the rice weed recognition module respectively by virtue of an I2C (inter-integrated circuit) bus and a BNC (Bayonet Nut Connector) data bus; the image acquiring module consists of a CCD (charge-coupled device) camera supporting high-definition image and an SAA7115 decoding chip of Philips, the image displaying module consists of a Gadmei PL7006 liquid crystal television used for displaying a television signal, and the rice weed recognition module consists of a DM642DSP image processing chip, and expanding SDRAM (synchronous dynamic random access memory) and SAA7121 (system application architecture 7121) decoding chips. According to the invention, the real-time recognition of weeds in rice fields can be realized, and the system miniaturization and portability can lead the system to be capable of being flexibly arranged on rice planting field equipment, and improving the practical application range of the system.

The invention provides a food material freshness identification method and device based on deep learning, a refrigerator and a medium. The method comprises the following steps that food material imageinformation is collected; according to the food material image information and a pre-trained food material freshness identification model, food material freshness identification result information isdetermined; and on the basis of deep learning, the food material freshness identification model is trained and updated by taking the food material image information collected in the food material freshness identification process as a sample according to a preset period. According to the technical scheme, the identification accuracy is relatively high, real-time identification can be achieved, optimization and improvement are carried out in the application of the food material freshness identification model, so that the application range of food material freshness identification is improved, and the method and the device can be applied to complex scenes.

The invention discloses an outdoor tomato identification method based on subsection threshold image segmentation and light spot identification. The method includes: after normalized color difference Cd and color component ratio rGB are calculated, subsection threshold image segmentation is utilized to realize tomato non-light-spot area segmentation Rr; a light spot area segmentation algorithm is used to extract a candidate tomato light spot area Rb, and Rr and Rb form an initial tomato area Rt; area marking and denoising are performed to Rt; calculating area At of the initial tomato area and area Ab of the candidate tomato light spot area; in the same tomato area, if Ab is smaller than one third of At, Rb is reserved at the tomato area, otherwise Rb is reckoned as a background light spot area and removed, and only Rr is reserved; and area marking and denoising are performed to the finally obtained tomato area. Subsection threshold image segmentation and light spot identification are combined to overcome influences of natural light variation, and real-time fruit identification under different lighting conditions is achieved.

The invention discloses an on-line identification method for steering characteristics of drivers. The on-line identification method comprises the following steps: S1, collecting steering data of cars when a plurality of drivers under test drive the cars and steer; S2, analyzing dependency among the steering data collected in the step 1 to obtain characteristic item data capable of describing the steering characteristics of the drivers; S3, clustering the characteristic item data by using a clustering algorithm, and enabling each characteristic item data class to be corresponding to a drive steering type; S4, establishing an off-line identification model by a BP (back propagation) neural network, extracting a weight value and a threshold value, and further establishing an on-line identification model; S5, collecting target steering data of the cars when the drivers under test drive the cars and steer, and identifying the steering characteristics of the drivers under test by the on-line identification model. According to the on-line identification method for the steering characteristics of the drivers, disclosed by the invention, in allusion to the problem that the operations of the drivers are not matched with car responses in the steering process of the cars, the on-line identification model is established, therefore the types of the steering characteristics of the drivers can be accurately identified in real time.

The invention discloses a fast face angle recognition method based on deep learning. Face angle recognition relates to rotation angles of a head around three axes in a three-dimensional vertical coordinate system, and a face angle change can cause partial absence of facial feature information. The method comprises the following steps: (1) labeling face images at times of rotation of 0 degree, plus and minus 15 degrees, plus and minus 30 degrees and plus and minus 45 degrees to establish a database; (2) using angle labels to carry out feature extraction through five layers of a convolution module, transforming features into 2048-dimensional feature vectors through two layers of full connection, using the feature vectors as inputs of a softmax classification layer, carrying out classification, and thus establishing a convolutional neural network structure; and (3) based on an ubuntu 16.04 operating system, training the convolutional neural network under a GPU of 1080 and a deep learning framework of a CAFFE to obtain a face angle recognition model. The method is used for deep learning-based real-time recognition of three-dimensional face angles.

The invention provides a monitoring method and device based on human body characteristics, and the method comprises the steps: obtaining the human body characteristics of a target object, and receiving a video image which is uploaded by monitoring equipment, screened out from a monitoring video, and provided with a monitoring object; extracting human body characteristics of at least one monitoredobject contained in the video image; calculating a first feature similarity between the human body feature of the target object and the human body feature of each monitoring object;, determining the monitoring object with the first feature similarity greater than the first preset similarity as a target monitoring object, and sending warning information for the target monitoring object, the warninginformation comprising uploading identification information of a video image monitoring device comprising the target monitoring object. Through the method, the monitoring efficiency can be improved.

The invention provides a laser particle image speed measuring data processing method based on moving boundary self-recognition, and belongs to the technical field of hydromechanics experiments. A PIVimage containing laser particles is shot by a PIV camera, image gray values are acquired, and a data matrix is established by the gray values of the PIV image; boundary pixel points in the PIV image are judged and recognized according to a boundary judgment criterion, the boundary pixel points are connected, and a boundary of a measured object in a moving state is acquired; a new SN coordinate system is established according to the boundary and the normal perpendicular to the boundary; the PIV image is subjected to coordinate conversion, data obtained after coordinate transformation of the PIVimage is acquired, and a capture window when the data obtained after coordinate transformation is subjected to correlation calculation always clings to the moving boundary, then a speed vector is acquired by the data obtained after coordinate transformation through a standard correlation calculation algorithm, the speed vector is subjected to coordinate inverse transformation, and a speed flow field of the measured object is acquired. The measuring precision of the speed flow field of the moving boundary is improved.

The present invention provides a human behavior real-time recognition method, and the method comprises the steps of: firstly, collecting triaxial acceleration data of an acceleration sensor by utilizing mobile equipment and uploading the triaxial acceleration to a PC (personal computer) machine; secondly, preprocessing the triaxial acceleration data by utilizing a preprocessing module on the PC machine, wherein the preprocessing includes window length cutting and feature extracting; then training a Gaussian processes classifier model with relatively high precision by utilizing the pre-processed data; transplanting and installing the Gaussian processes classifier model and the preprocessing module to an Android platform; and then installing the platform to the mobile equipment; and finally performing real-time testing and observation on human behaviors. The method can be effectively applied to various actual environments such as emergency rescue environments, medical healthcare environments and the like; by solving the congestion problem caused by too large data volume during remote data transmission in a conventional method, the data volume processed by a mobile phone is reduced; the energy consumption is lowered; and efficient, accurate and real-time remote human behavior monitoring is realized.

The invention provides a small and medium-sized navigation channel bridge monitoring device characterized in that the device is arranged on a small and medium-sized bridge and directly facing a navigation channel so as to monitor a ship target in the navigation channel, is arranged on the bridge and comprises a camera which is arranged on the small and medium-sized bridge, directly faces the center line of the navigation channel and is used for shooting the navigation channel so as to obtain a corresponding real-time monitoring video; a radar which is arranged on small and medium-sized bridgesand is at least used for continuously scanning the navigation channel and obtaining a real-time scanning position when a ship target is scanned; a warning module which is used for sending an acousto-optic warning to a ship target in the navigation channel; and a central processing unit in communication connection with the camera, the radar and the warning module, wherein the central processing unit comprises a neural network model storage part, a monitoring information acquisition cache part, a ship target identification part, a video coordinate conversion part, a radar coordinate conversionpart, a data fusion part, a track information identification part, a track prediction part and an early warning analysis output part.

The invention provides a student classroom learning state online evaluation method and system. The student classroom learning state online evaluation method comprises the following steps: synchronously collecting video images and sound signals of students; correspondingly inputting the video image and the sound signal into the trained expression recognition model and the trained emotion state recognition model, and outputting an expression category, an emotion state category and a corresponding probability corresponding to the student, wherein the categories of the expressions, the emotional states and the classroom learning states are the same; and inputting the expression recognition result and the emotional state recognition result into a student state evaluation model, and outputting an evaluated student classroom learning state category.

The invention relates to the field of big data and discloses a black product gang identification method, device and equipment and a storage medium. The method comprises the following steps of within apreset time range, after receiving request information sent by the same user account each time, extracting a plurality of node features in the request information; based on each node feature, constructing a risk feature graph of the user account, and segmenting the risk feature graph into a plurality of feature sub-graphs; respectively calculating the shortest coding length of each feature sub-graph by adopting a preset coding measurement algorithm, and respectively splitting each feature sub-graph into a plurality of feature communities according to the shortest coding length; and inputtingeach characteristic community into a preset black product gang identification model for identification, and outputting a black product gang identification result. The invention also relates to a blockchain technology, and the request information is stored in the blockchain. According to the method, real-time identification of the black gang is realized, and the defense capability of the black gangis improved.

The invention discloses a civil aircraft test flight vibration fault prediction model which is established through the following steps: 1, establishing a database which is composed of a characteristicparameter set and states corresponding to characteristics; wherein the characteristic parameter set comprises time domain characteristics extracted from the vibration parameters of each part of the civil aircraft, frequency domain characteristics extracted from the vibration parameters of each part of the civil aircraft, and flight parameters of which the correlation with the vibration parametersis greater than 0.6; wherein the time domain characteristics comprise the maximum value, the minimum value, the peak-to-peak value and the amplitude change rate of the vibration amplitude; wherein the frequency domain characteristics comprise frequency composition, frequency size and a change trend of the frequency along with time; and 2, establishing a civil aircraft test flight vibration faultprediction model by using the binary support vector machine model to obtain the binary support vector machine model so as to determine the early warning boundary of the test flight data, and taking the early warning boundary as the basis for judging the normal and abnormal single states of the data. The invention also discloses a civil aircraft test flight vibration fault prediction system.

The invention relates to a method and system for identifying a salient-point mould number of a revolution-solid glass bottle based on images. The method comprises the following steps of: 1) rotating the to-be-identified glass bottle on a turn table, and triggering a camera to collect a panoramic image containing a pitting mould number area by virtue of a photoelectric sensor and a rotary encoder;2) preprocessing the collected image to improve the quality of the image; 3) on the basis of smoothening the image rapidly by virtue of an integral image, segmenting the image by virtue of a dynamic threshold, and carrying out morphologic processing on the image to improve the segmentation effect, screening salient points of a mould according to the features (such as shapes, area and coordinates and the like) of the salient points of the mould, and extracting the coordinates of each salient point; 4) according to the distance relationships among the salient points, calculating corresponding symbolic expressions; and 5) inquiring a glass bottle mould code table, and determining the mould number according to the symbolic expressions obtained in the step 4). According to the invention, lightpaths are transformed by a mirror, thereby satisfying the needs on the space layout and adjustment of equipment; the collection of the high-definition salient-point images of the mould is ensured; and the identification system is simple in structure, low in cost, high in precision, high in speed, and easy to popularize and use.

The invention relates to a road water accumulation point identification system, which comprises a vehicle speed evaluation value setting unit, a vehicle flow evaluation value setting unit, a rainfallwater accumulation probability correction coefficient setting unit and a water accumulation identification unit, wherein the vehicle speed evaluation value setting unit sets the vehicle speed evaluation value of a detected road section according to the current average vehicle speed of floating vehicles on the detected road section and a road section water accumulation vehicle speed threshold; thevehicle flow evaluation value setting unit sets the vehicle flow evaluation value of the detected road section according to the current floating vehicle flow on the detected road section and a road section water accumulation floating vehicle flow threshold; the rainfall water accumulation probability correction coefficient setting unit sets a rainfall water accumulation probability correction coefficient according to the current hourly rainfall on the detected road section and multiple rainfall thresholds; and the water accumulation identification unit enables the vehicle speed evaluation value of the detected road section, the vehicle flow evaluation value and the rainfall water accumulation probability correction coefficient to be multiplied to obtain a water accumulation integrated evaluation value of the detected road section, and according to the water accumulation integrated evaluation value, whether the detected road section is a water accumulation road section is judged. Detection of water accumulation without device can be realized, and the device layout and maintenance cost is greatly reduced.

The invention provides a distraction driving detection method based on face posture estimation and sight deviation. The distraction driving detection method comprises the following steps: a face detection step, a face tracking step, a face key point detection step, a face pose estimation step and a distraction driving detection step. According to the invention, a depth infrared camera and other sensor equipment are not needed, and only RGB three-channel color images collected by a common camera are needed to be used for vehicle-mounted video image identification and distracted driving detection.

The embodiment of the invention provides a tire wear degree identification method and device. The method comprises the steps that the circumferential acceleration sequence and the radial accelerationsequence of a tire in the driving process are acquired, and the sequences are normalized according to the vehicle speed square of the same moment; the normalized circumferential acceleration sequenceand radial acceleration sequence form a sample to be predicted in the form of a two-dimensional matrix; and the sample to be predicted is inputted to a preset convolutional neural network model, and the wear degree of the tire is determined according to the output result of the convolutional neural network model. According to the method, modeling of the complex mechanism of tire wear can be avoided, and wear degree identification can be realized by analyzing the circumferential and radial acceleration response of the tire so that the method is simpler and more convenient and higher in precision and online real-time prediction can be realized. End-to-end wear degree prediction is performed on the recombined acceleration signal matrix by using the powerful feature extraction function of theconvolutional neural network without too much early pretreatment so as to have robustness for the change of the driving condition.

The invention provides an empty parking space identification method. The empty parking space identification method comprises the following steps: 1, acquiring image information of a parking space by using a camera mounted above the parking space; 2, for the image information, determining a first confidence coefficient that the parking space is an empty parking space by using a first identificationalgorithm; 3, for the image information, determining a second confidence coefficient that the parking space is an empty parking space by using a second identification algorithm; 4, judging whether the parking space is an empty parking space or not based on the first confidence coefficient and the second confidence coefficient.