227 results about "Machine vision system" patented technology

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

A system including: a robotic frame including at least an actuated arm, a machine-vision system including at least a camera, the machine-vision system communicatively connected to a computing device, the computing device including at least a processor and a memory communicatively connected to the at least a processor, the memory storing instructions configuring the at least a processor to: receive image data from the at least a camera, the image data including at least two captured viewpoints of a produce item, extract at least an object-level visual feature from the image data using a machine vision model, assign a defect classification label to the produce item as a function of the at least an object-level visual feature, and control the at least an actuated arm to perform a handling operation on the produce item as a function of the defect classification label.

The invention discloses a double-arm citrus picking robot and a picking method, and belongs to the technical field of agricultural automation equipment. The robot comprises a crawler-type moving chassis, a symmetrically-arranged double-mechanical-arm system, a machine vision system, a citrus collecting bin and a control system. The double-mechanical-arm system adopts a main and auxiliary collaborative framework, the tail end of a main mechanical arm is integrated with a clamping jaw and vacuum suction disc parallel mechanism, an auxiliary mechanical arm is provided with a rotary circular saw blade, and lossless clamping of fruits and precise cutting of fruit stems are achieved. The machine vision system realizes high-precision citrus identification under complex illumination by improving a YOLOv5 network and fusing an HSV dynamic threshold value and Focal Loss optimization; graded path planning is combined with an A * algorithm and an RRT * algorithm, and motion safety of the mechanical arm is ensured through bounding box collision detection. The picking method comprises the steps of three-dimensional semantic map construction, collaborative grabbing and cutting, lossless transfer and the like. The problems that a single-arm robot is low in efficiency, fruits are prone to being damaged, fruit stems are not thoroughly treated and the like are solved, and the robot is suitable for automatic orchard harvesting in hilly areas.

The invention relates to a flexible material cutting robot with a leveling function and a cutting method thereof, and belongs to the technical field of flexible material cutting, the flexible material cutting robot comprises a master control layer, a sensing layer, an execution layer and an interaction layer, the master control layer adopts a central control module, the sensing layer adopts a data acquisition unit, the execution layer is a hardware action unit, and the interaction layer adopts a man-machine interface. The central control module is provided with a digital twin engine and a multi-physics field collaborative optimization algorithm library, the data acquisition unit comprises a machine vision system and a material characteristic self-identification unit, and the hardware action unit comprises a multi-stage tension collaborative leveling module, a vacuum adsorption type dynamic cutting platform and a multi-axis linkage cutting head group. According to the invention, through closed-loop control of material characteristic sensing, dynamic leveling, self-adaptive adsorption and energy matching cutting, zero-deformation precise cutting of high-end flexible materials is realized, and the system is particularly suitable for high-value material processing in the fields of aerospace, medical equipment and the like.

The invention provides a belt type dried beancurd stick microwave drying device and method based on the expansion degree, the drying device is mainly divided into four heating areas, each heating area only depends on microwave heating, each heating area is provided with a moisture exhaust fan and a fresh air valve, and the drying device is provided with a machine vision system used for controlling equipment operation. Whether the dried beancurd sticks exist in the corresponding area of each magnetron group is judged through the machine vision system, the on-off of the magnetrons is automatically controlled, and the no-load phenomenon of the magnetrons is avoided. On the premise of ensuring safe production, the working number of magnetrons in different areas, starting and stopping of a moisture removal fan and opening and closing of a fresh air valve are controlled through the expansion degrees of the different areas of the dried beancurd sticks in combination with appearance colors, so that the local charring phenomenon caused by over-high microwave drying power, untimely evaporation of moisture on the surfaces of the dried beancurd sticks or over-high temperature is avoided; the expansion degree consistency of different batches of dried beancurd sticks is ensured, and the automation degree of equipment is improved.

The invention discloses a target detection method and device based on visual identification. The method comprises the following steps: acquiring relative coordinates between an image acquisition part and a detection surface of a detection object; when the relative coordinate is not consistent with the reference coordinate, determining a first coordinate difference value; when the relative coordinate is not within the coordinate error range, adjusting the image acquisition component until the relative coordinate is consistent with the reference coordinate; when the relative coordinates are consistent with the reference coordinates, controlling an image acquisition component to perform image acquisition on the detection object to obtain a plurality of images; determining second relative coordinate differences between the plurality of images and the reference image, and selecting a target image; and performing target detection according to the target image. The technical problem that in the prior art, a machine vision system adopts depth-of-field limitation of a fixed camera, so that all targets cannot be ensured to be within the depth-of-field range of the camera when the heights of different models of recognized targets are changed, and recognition cannot be possibly caused is solved.

A Kaleidoscopic Geometric Vision Platform (KGVP) that transforms three-dimensional (3D) machine vision through an optical system that creates multiple virtual laser projectors from a single moving component. A spindle mirror mechanism (SMM) rotates a reflective surface to sweep collimated laser beams in circular patterns. These beams strike a series of kaleidoscopic mirror facets (KMFs) arranged concentrically around the SMM in a concave configuration. Each KMF redirects the rotating beam, creating a virtual projector with a distinct origin point and sweep direction. As the SMM completes one rotation, it generates N distinct laser trajectories (where N equals the number of KMFs), each sweeping from a different virtual origin point. Event-based cameras / sensors positioned strategically around the KMFs detect laser light reflected from object surfaces with microsecond precision. The KGVP triangulates 3D surface coordinates by determining correspondence between detected light and specific virtual projectors based on precise timing information. This approach eliminates the computational complexity typically required for multi-view feature matching, as the KGVP can unambiguously identify which virtual projector created each detected event. With calibration, the KGVP can establish the exact 3D positions of all virtual projector origins and their beam trajectories and build look-up tables that optimize triangulation accuracy. The KGVP enables real-time 3D surface reconstruction with high precision while using far fewer mechanical components than conventional machine vision systems.

The invention relates to the technical field of brake component detection, and discloses a brake spring piece flatness intelligent detection system and method based on machine vision. The method comprises the steps of obtaining historical brake spring piece image data, and generating a standard flatness curve according to the historical brake spring piece image data; real-time brake spring piece image data are collected through a machine vision system, and a real-time flatness curve is generated; calculating a flatness deviation curve according to the real-time and standard flatness curve; analyzing the matching degree of the deviation curve and a reference defect model, and determining the flatness state of the brake spring piece; and finally, dynamically adjusting detection parameters based on the flatness state, and generating an optimized detection scheme. According to the method, non-contact detection is realized by taking historical data as a reference and depending on machine vision, flatness deviation can be accurately presented, defect properties corresponding to the deviation can be determined, a detection scheme can be dynamically optimized, detection consistency, efficiency and adaptability are improved, manual dependence and part abrasion are reduced, and the method is suitable for batch detection scenes of the brake spring pieces.

The invention relates to the technical field of commercial vehicles, in particular to a vehicle shock absorber damping force dynamic adjustment method based on road feature recognition, and introduces a machine vision technology to detect road features in the vehicle driving direction. Through the machine vision system, the vehicle can identify the unevenness features of the road in advance, including the feature types and the spatial positions thereof, and feed back the information to the control system in real time. And the control system adjusts the damping force of the shock absorber in advance according to the previewed road characteristics. Specifically, training and optimization are carried out based on an improved YOLOV8 model, road feature classification information and detection frame position information from the trained model are received in real time through a semi-active controller, and a preset control algorithm is executed according to information returned by the model to adjust the damping force of the shock absorber in advance. According to the method, preview control is carried out in the semi-active suspension by recognizing road excitation, and the problem that the vibration reduction effect of the semi-active suspension is not ideal due to response time lag of the vibration damper is solved.

A waste stream is analyzed and sorted to segregate different items for recycling. Certain features of the technology improve the accuracy with which waste stream items are diverted to collection repositories. Other features concern adaptation of neural networks in accordance with context information sensed from the waste. Still other features serve to automate and simplify maintenance of machine vision systems used in waste sorting. Yet other aspects of the technology concern marking 2D machine readable code data on items having complex surfaces (e.g., food containers with integral ribbing for structural strength or juice pooling), to mitigate issues that such surfaces can introduce in code reading. Still other aspects of the technology concern prioritizing certain blocks of conveyor belt imagery for analysis. Yet other aspects of the technology concern joint use of near infrared spectroscopy, artificial intelligence, digital watermarking, and / or other techniques, for waste sorting. A variety of further features and arrangements are also detailed.

The invention relates to the technical field of single crystal preparation, in particular to a crystal seeding stage monitoring method and system, a storage medium, a single crystal product manufacturing method and a single crystal product. Image changes in the seeding process are judged in combination with a computer machine vision system or an image recognition technology, statistics and analysis of abnormal conditions are carried out by taking each monitoring image as a unit through an image judgment model, and the accumulated abnormal conditions are reasonably analyzed, so that warning worthy of being noticed is output, and the seeding process is automatically monitored; by accurately identifying and outputting the crystal structure change signal instead of manual automatic execution of the monitoring process, the abnormal condition identification accuracy of the single crystal production process can be effectively improved, and the process inspection waste and subjective non-uniformity of personnel are reduced or eliminated.

FIELD: underwater equipment.SUBSTANCE: invention is intended for precise automatic docking and fixation of autonomous unmanned underwater vehicles (AUVs) using bottom mooring devices (BMDs). For automatic docking and fixation of an autonomous unmanned underwater vehicle on bottom mooring devices, an underwater vehicle is used with the ability to detect bottom mooring devices using a hydroacoustic navigation system installed on it. Using the onboard control system, the underwater vehicle stabilizes its position in the working area of the manipulator with a given orientation to its grip; simultaneously with the last command, a signal is sent using the hydroacoustic navigation system of the underwater vehicle about its arrival in the area of the bottom mooring device. After receiving this hydroacoustic signal from the hydroacoustic navigation system, a machine vision system installed near the manipulator's grip is turned on, and with its help, the spatial location of the clamp of the gripping device located in the bow part of the underwater vehicle’s hull is detected, and then, with the help of the technical vision system and the manipulator control system, its open grip is smoothly moved towards the clamp until the contact sensor, located inside the gripper jaws, is triggered; upon the signal from the contact sensor, the gripper jaws are closed, fixing the underwater vehicle in the water space. Using the manipulator control system, the corresponding control signals are sent to the manipulator drives, smoothly moving the underwater vehicle to the bottom mooring device platform; during this movement, the underwater vehicle’s thrusters maintain its horizontal position; said movement of the underwater vehicle by the manipulator is completed after the activation of the locking devices located, respectively, on the underwater vehicle and the bottom mooring device. After said fixation, using contactless devices located inside the fixing devices, the underwater vehicle's batteries are recharged via a cable-rope, information is exchanged between this underwater vehicle and the accompanying vessel, or the underwater vehicle is quickly lifted onto this vessel using a winch.EFFECT: precise and rapid docking of the AUV with the charging device or with the device for its rapid ascent to the surface, as well as rigid fixation of the AUV at a given point in the water space when the onboard equipment of this apparatus performs arbitrary working operations.1 cl, 1 dwg

The invention discloses a method for automatically adjusting the sand blasting pressure of a spray gun based on BSD equipment, and particularly relates to the technical field of semiconductor device manufacturing. S2, reference pressure matching; s3, carrying out real-time multi-dimensional monitoring; s4, dynamic pressure adjustment; and S5, performing closed-loop verification and correction. According to the method for automatically adjusting the sand blasting pressure of the spray gun based on the BSD equipment, the initial flatness, the edge thickness difference and the target damage layer depth of the back face of a silicon wafer are collected through a high-precision laser contourgraph, and a silicon wafer parameter-sand blasting pressure mapping model trained through 1000 or more sets of data is called to determine adaptive reference pressure; and during sand blasting, a micro pressure sensor, a machine vision system and an abrasive flow sensor are combined to realize multi-dimensional real-time monitoring, so that the problems of insufficient depth and incomplete coverage of a damaged layer or edge breakage and cracks of a silicon wafer are effectively avoided.

The application discloses a kind of tea rolling degree classification methods based on machine vision system, comprising the following steps: step 1: based on machine vision system acquisition different rolling time under tea rolling leaf sample image;Step 2: extract and analyze the color feature information of tea rolling leaf sample image;Step 3: based on portable color difference instrument acquisition different rolling time under tea rolling leaf sample color appearance index;Step 4: based on the color appearance index under different rolling time, obtain the variation law of color appearance index with rolling time;Step 5: based on color feature information and the variation law of color appearance index with rolling time, the classification model of tea rolling degree is established by CNN-SVM algorithm, the application based on machine vision system establishes objective quantization tea rolling degree classification method, realizes the quality quality of product and the quality quality of product of precision control tea processing process.

1. The name of the design product: automatic focusing module (RK-200D33-AF). 2. The use of the design product: used for precise detection of machine vision system. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: design 1 perspective view 1. 5. Design 1 is designated as the basic design.

An apparatus for adjusting a display of a graphical user interface (GUI) based on image data. The apparatus includes a machine vision system configured to capture images of real-world objects within an environment, at least a processor; and a memory coupled to the processor. The memory contains instructions configuring the processor to receive input data objects, comprising image data and location data, using the machine vision system, process the image data using the machine vision system to extract and classify components of the visual inputs, generate structured environmental data based on the processed image data, and modify the GUI based on the input data objects and the structured environmental data. The image data is cross-referenced with applicable regulatory mandates by performing real-time safety assessments and compliance checks based on visual inputs of the image data.

The utility model discloses a Mark point structure, which adopts at least two rectangular parts which are arranged in a crossed manner to form a bonding pad area, and compared with a traditional circular structure, the composite geometric shape of the bonding pad area can provide higher feature vector dimension in a machine vision system, the HOG feature value is improved by at least 30%, the algorithm identification accuracy is greatly improved, and the reliability of the structure is improved. Especially in a high-density BGA packaging area, the positioning precision of the center coordinates of the cross points can reach + / -0.01 mm, the identification degree of the Mark points on the PCB is greatly improved, and the problem that the Mark points are difficult to distinguish due to the low identification degree is effectively solved.

To provide cable wire insertion depth monitoring.SOLUTION: A method (200) for cable wire insertion monitoring includes receiving, from a camera system (312), an inspection image (314) of a connector housing (302) held in a housing retainer (306) of a housing inspection system (300). Insertion of a cable wire (324) into a corresponding cable cavity (304) of the connector housing (302) is detected via an insertion monitoring machine vision system (402). Based at least in part on the inspection image (314), an insertion depth of the cable wire (324) into the corresponding cable cavity (304) is estimated. An indication (416) of the insertion depth of the cable wire (324) is output.SELECTED DRAWING: Figure 3B

The application discloses a kind of interframe image coding rate distortion optimization method and system for machine vision, it is related to image processing technical field, including steps: extracting the feature map of current video frame original image and corresponding reconstruction image under each scale;According to the feature map of original image and reconstruction image, obtain the distortion measure based on feature similarity;According to the point coordinate pixel value of original image and reconstruction image, obtain the distortion measure based on square error;By mixing respectively based on feature similarity, based on square error distortion measure, rate distortion optimization under the control of mixed distortion measure is carried out.The application can better meet the sensitivity of machine vision system to video content features by mapping video frames to feature space and extracting multi-scale features using residual deep neural network, which not only improves the coding efficiency, but also ensures that the key features of the encoded video content are preserved during machine processing, thereby improving the performance and task completion of the algorithm.

A method, computer program product, and computing system for encoding audio encounter information of a reference audio acquisition device of a plurality of audio acquisition devices of an audio recording system, thus defining encoded reference audio encounter information. Location information may be estimated, via a machine vision system, for an acoustic source within an acoustic environment. One or more acoustic relative transfer functions may be selected from a plurality of acoustic relative transfer functions for the plurality of audio acquisition devices of the audio recording system based upon, at least in part, the location information. The encoded reference audio encounter information and a representation of the selected one or more acoustic relative transfer function may be transmitted.

Methods of identifying a defect in a machine vision system. Embodiments of the method include providing a first imaging device having a first field of view; moving a reflective tool through the first field of view; capturing a plurality of images of the reflective tool at different locations in the first field of view using the first imaging device; and analyzing at least one of the plurality of images to identify one or more defects in the machine vision system. Systems and apparatus configured to carry out the methods are provided, as are other aspects.

Surgical tracking, methods and software for assisting in determining whether a tool is properly installed on a surgical device. A protective packaging retains the tool and has a trackable feature. The trackable feature has a predetermined state defined relative to the tool and the trackable feature is detectable by a machine vision system. One or more controllers acquire the actual state of the tool based on the detected trackable feature and compare the actual state of the tool with an expected state of the tool, which is based on an expected condition in which the tool is properly mounted to the surgical device. Based on the comparison, the one or more controllers can determine whether the tool is properly mounted to the surgical device and generate an alert.

The invention provides a product anti-counterfeiting traceability detection system based on machine vision and a working process, belongs to the technical field of product anti-counterfeiting traceability detection, and aims to provide the product anti-counterfeiting traceability detection system based on machine vision and the working process. The product anti-counterfeiting traceability detection device can be installed on an assembly line or any stable cross beam, on one hand, the first industrial camera is used for collecting product labels for anti-counterfeiting traceability detection, on the other hand, the second industrial camera can shoot label images at different angles to enrich a data set, and the accuracy of watermark extraction is improved. Meanwhile, the detection system is matched with a printing-shooting-resistant image watermark embedding and extracting method based on deep learning, so that a machine vision system has strong image recognition and analysis capabilities, and powerful technical support is provided for anti-counterfeiting traceability of products.

In an aspect, an apparatus for procedural training is presented. An apparatus includes a processor and a memory communicatively connected to at least a processor. The memory contains instructions configuring at least a processor to receive optical data from a sensor, wherein a machine vision system is configured to identify at least one object within optical data. At least a processor is configured to determine a procedural performance parameter as a function of optical data wherein determining the procedural performance parameter comprises classifying the at least one object within optical data to an optical data category using an optical data classifier. At least a processor is configured to compare a procedural performance parameter to a procedural performance threshold. At least a processor is configured to display procedural training feedback through a display unit as a function of a comparison.

The disclosure discloses a method and a system for generating image adversarial examples based on an acoustic wave. The method includes: acquiring an image containing a target object or a target scene; generating simulated image examples for the acquired image, wherein the simulated image examples have adversarial effects on a deep learning algorithm in a target machine vision system; optimizing the generated simulated image examples to obtain an optimal adversarial example and corresponding adversarial parameters; and injecting the adversarial parameters into an inertial sensor of the target machine vision system in a manner of an acoustic wave, such that the adversarial parameters are used as sensor readings that will cause an image stabilization module in the target machine vision system to operate to generate particular blurry patterns in a generated real-world image so as to generate an image adversarial example in a physical world.

An automated golf ball sorting apparatus and method of use is provided. A machine vision system and neural network classification sort golf balls by brand, model, or condition. The system includes a chain-driven elevator with inclined ball platforms that rotate each ball in two planes along a high-friction roller. A camera captures images of each rotating ball, which are classified using a trained convolutional neural network. A microcontroller tracks each ball's progress using infrared sensor interrupts and dynamically assigns it to a solenoid-actuated gate for diversion into a corresponding sort bin. A user loads mixed balls into a hopper; the system lifts, scans, classifies, and sorts them in real time, processing one ball per second with over 90% accuracy. A training mode enables collection of annotated image data using single-type ball loads to refine the neural network.

The invention discloses an experimental device for simulating surface dirt accumulation and detection of an intelligent coal caving camera, and relates to the technical field of coal mine engineering, the experimental device comprises an air duct model group, a dust generator, an air draft mechanism, a power supply, a controller, a sensor assembly, a haze meter and a plurality of camera type dust collectors; the air duct model group with the transparent side wall is provided with a simulation air duct for simulating a coal caving space at the rear part of an underground fully mechanized caving face; the air draft opening of the air draft mechanism is connected with the outlet of the simulation air duct and has adjustable power; the sensor assembly comprises a wind speed sensor and at least one concentration sensor; the camera type dust collectors are respectively arranged at different positions of a dust flowing path; the haze meter is used for detecting the haze of each simulation lens for at least three times, the controller is in communication connection with the dust generator, the air draft mechanism, each sensor and the haze meter, and the power supply is used for powering on each component. The system can simulate the coal dust attachment characteristics on the surface of the camera, provides support for the research of reducing the dust attachment of the camera, and improves the precision of a visual system of an underground intelligent coal caving machine.

The invention provides a machine vision system and device for identifying and guiding a robot to perform automobile toe-in adjustment, and the system comprises a user interface, an image collection module, a deep learning target identification module, a three-dimensional coordinate calculation module, a coordinate conversion module, and a robot communication guiding module. Real-time processing can be carried out on a color image and a point cloud image shot by an industrial camera, pixel positions of a nut and a screw can be accurately recognized, and three-dimensional space coordinates of the nut and the screw in a robot coordinate system are calculated; the deep learning target recognition module can efficiently and accurately position a target from a complex background based on a pre-trained model; the coordinate conversion module converts the visual information into a position instruction which can be executed by the robot; and the robot communication guiding module is responsible for sending the coordinate instruction to the robot controller to realize accurate motion guiding. The problems that traditional automobile toe-in adjustment depends on manpower, efficiency is low and precision is unstable are effectively solved, and time and economic cost can be saved.

The invention discloses water-fertilizer collaborative irrigation equipment for selenium-rich rape straw mushroom planting, and relates to the technical field of water-fertilizer collaborative irrigation, the water-fertilizer collaborative irrigation equipment comprises a clear water storage mechanism for storing clear water, and one side of the clear water storage mechanism is provided with a straw storage mechanism for storing crushed selenium-rich rape straw; the lower side of the straw storage mechanism is provided with a fermentation storage mechanism for fermenting and storing selenium-rich rape straw and water and fertilizer, and one side of the fermentation storage mechanism is provided with a supply mechanism for performing different irrigation modes according to different types of mushrooms; through cooperative work of a machine vision system-camera integrated in the supply mechanism and a judgment module, the system can recognize mushroom types (such as oyster mushrooms, shiitake mushrooms and stropharia rugoso-annulata) in a cultivation area in real time, irrigation parameters are intelligently adjusted according to the mushroom types, and due to the differential irrigation strategy based on crop physiological characteristics, the irrigation efficiency is improved. The problems of necrosis, incomplete nutrient absorption and the like caused by an improper irrigation mode in traditional mushroom cultivation are effectively solved.

The utility model provides an identification device based on chess and card data reading, and belongs to the technical field of card arranging machines. Comprising a recognition device used for reading chess and card data, a chess and card sensing area used for containing chess and cards to be recognized and a control module. The recognition device is arranged opposite to the detection face of a to-be-recognized chess and card, the output end of the recognition device is connected with the input end of the control module, and the recognition device conducts recognition based on the distance signal difference between the concave-convex structure of the chess and card character mark face and the plane structure of the chess and card back face. The identification device is a contact type identification device or a non-contact type identification device; the contact type recognition device is directly in physical contact with the chess and card to be recognized; the non-contact recognition device does not make physical contact with the chess and card to be recognized. The contact type recognition device is an inductive sensor, a capacitive sensor or a linear displacement sensor. The non-contact identification device is a laser range finder, an ultrasonic detector, a photoelectric sensor or a machine vision system. The technical problem that in the prior art, color recognition card face precision is poor is solved.