399 results about "Machine vision system" patented technology

The invention discloses an aluminum alloy surface defect detection method and system using deep learning, and relates to the related field of detection performed in combination with deep learning, and the method comprises the following steps: carrying out image acquisition on an aluminum alloy finished product, constructing a surface image data set to extract multi-scale surface features, and constructing a feature enhanced image set to carry out defect detection, and a defect detection result is obtained to dynamically optimize aluminum alloy production process parameters, and a process adjustment instruction is generated and fed back to a production system. The technical problems that traditional detection mostly depends on manual visual inspection or a machine vision system based on rules, imaging distortion, small defect missing detection and process adjustment are caused are solved, and the technical effects that deep learning is conducted through the double-branch network, the defect detection efficiency and the production yield are improved, and control over the aluminum alloy manufacturing quality is met are achieved.

The invention discloses an AOI optical scheme automatic optimization method based on reinforcement learning, and belongs to the technical field of automatic optical detection. According to the method, a reinforcement learning technology is applied to automatic adjustment of optical schemes in a machine vision system, wafer images under different optical schemes are collected by using an AOI system, and scoring is performed by using a plurality of semantic segmentation models; constructing and training a non-reference image quality evaluation network, evaluating the optical imaging quality in real time, and designing a reward function on the basis; by using the Actor-Critic algorithm, the intelligent agent can autonomously learn an optimal parameter adjustment strategy, quickly adapt to different scenes, realize dynamic optimization of light source parameters and remarkably improve image quality, and an efficient and intelligent solution is provided for efficient application of a machine vision system in a complex industrial environment.

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 machine vision system may include a meta-imager including a meta-optic and a polarization-sensitive photodetector. A machine vision system may further include and at least one processor operably coupled to the polarization-sensitive photodetector, and at least one memory operably coupled to the at least one processor. A machine vision system may be configured to: receive, from the polarization-sensitive photodetector, a plurality of feature maps; input, into a trained artificial neural network, the plurality of feature maps; and process, using the trained artificial neural network, the plurality of feature maps to recognize an object.

A measurement and positioning system based on machine vision and laser triangulation, which includes a machine vision system, a laser ranging system, and a standard positioning target configured to be installed on a positioned workpiece, where the laser ranging system is configured to project laser light onto the standard positioning target so as to perform a three-dimensional height measurement, and the machine vision system is configured to capture and image a positioning target and a laser image projected on the standard positioning target and to measure a two-dimensional coordinate position. The measurement system of the present application can realize a rapid three-dimensional measurement and precise positioning of a spatial position of the positioned object by means of the standard positioning target installed on the positioned object.

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 relates to the technical field of information, in particular to a forming process and system of a high-precision injection hook in the technical field of injection hook forming. The forming process includes the steps that computer aided design and finite element analysis are applied, the forming process of the injection hook in a mold is simulated, the mold structure is optimized, and the mold testing frequency is reduced; a high-precision mold is integrated with a cooling channel of a preset ejection hook arrow-shaped structure, a temperature sensor is arranged in the cooling channel, the cooling state is monitored in real time, cooling parameters are adjusted based on monitoring data, and precise structure forming is achieved. And the machine vision system detects the integrity and direction consistency of the arrow-shaped structure of the molded ejection hook on line, identifies unqualified products, automatically removes the unqualified products, records defect types, and sends defect type data and corresponding product batch information to the production data acquisition module.

The invention discloses a two-dimensional and three-dimensional fused fabric defect detection method, and belongs to the technical field of machine vision recognition. The technical problem of limitation of a traditional two-dimensional machine vision system is solved. According to the technical scheme, the method is characterized in that multi-angle light source imaging is carried out, a detected fabric is polished through light sources at different angles, and two-dimensional images of illumination at different angles are obtained through shooting of a linear array camera; and two-dimensional and three-dimensional information fusion: carrying out pixel-level fusion on the shot two-dimensional grey-scale map and the reconstructed gradient map and depth map with the three-dimensional information, carrying out high and low frequency decomposition and fusion on the two-dimensional information and the three-dimensional information through non-subsampled contourlet transformation, and fusing the two-dimensional defect feature information and the three-dimensional defect feature information into one map. After the non-subsampled contourlet transform decomposes the image, the frequency domain space is divided into wedge-shaped directional sub-bands; defects are identified based on the deep learning model. The method has the effect of improving the accuracy of textile quality detection.

A kaleidoscopic laser beam projector (KLBP) system that employs a single moving structure to project patterns of sweeping laser beams with diverse sweep directions and emission apertures. The system comprises a collimated light source, a spinning mirror deflector (“spindle / flutter mirror”) at the center, and a stationary kaleidoscopic mirror containing multiple facet mirrors arranged in a concave fashion around the spindle mirror. As the spindle mirror rotates, it directs laser beams in a radially whirling movement toward the facet mirrors, which then subdivide these beams into multiple short strokes and redirect them outward in different directions. Various embodiments include configurations with diffractive optical elements (DOEs) for beam splitting, different kaleidoscopic mirror geometries (hexagonal, octagonal, etc.), and innovative flutter mirror mechanisms that create controlled angular variations during rotation. The system enables a “4×1” configuration that replaces traditional “4×4” systems requiring eight motors and multiple moving structures. The KLBP system offers significant advantages including mechanical simplicity, reduced size and weight, improved energy efficiency, enhanced reliability, cost-effectiveness, and modular configurability. The diverse beam incidence angles enable superior illumination of complex three-dimensional objects for applications in machine vision systems, precision metrology, gap and flushness inspection, and robot-assisted assembly of complex structures.

The invention provides a weld quality evaluation system and method based on cooperation of ultrasonic waves and machine vision. The system comprises an ultrasonic detection system used for preliminary quality division and defect judgment of a weld and a machine vision system used for quality detection and quality evaluation of the weld. The ultrasonic detection system comprises an ultrasonic probe, an ultrasonic detector and a signal processing and analyzing module; the machine vision system comprises an image acquisition module, a data processing system and a user interface. The invention further provides a weld defect detection and evaluation method of the system. According to the method, ultrasonic detection and machine vision technologies are combined, ultrasonic waves are used for preliminarily detecting the defects of the welding seam, the defect detection process is gradually optimized, the machine vision system is combined for deep learning recognition of the surface defects of the welding seam, and finally independent operation of the machine vision system is achieved. Therefore, steel structure weld defect detection and weld quality evaluation are realized.

The invention discloses a building solid waste road performance index and multi-path utilization scene intelligent evaluation method. The method comprises the steps that the mass content of red bricks, the mass content of needle-sheet-shaped particles and the particle gradation in building solid waste particles are obtained through a machine vision system; the pavement performance indexes, namely CBR value, water absorption rate and crushing value, of the building solid waste are obtained through tests; according to the influence rules of the mass content of the red bricks, the mass content of the needle-sheet-shaped particles and the grain composition in the building solid waste particles on the building solid waste road performance indexes, an estimation model of the building solid waste road performance indexes is established. According to the method, the mass content of the red bricks, the mass content of the needle-sheet-shaped particles and the grain composition in the building solid waste particles are obtained in real time on the basis of machine vision, the road performance indexes of the building solid waste are predicted through the prediction model of the road performance indexes of the building solid waste, and the detection efficiency and accuracy are improved.

The invention relates to the technical field of surface defect detection, and discloses an aluminum profile surface defect analysis method based on machine vision, and the method comprises the steps: obtaining equipment operation parameters, environment parameters, aluminum bar processing procedure parameters and aluminum profile surface snapshots of processing equipment of a target aluminum profile in real time; obtaining a corresponding equipment operation risk value according to the equipment operation parameter; according to the aluminum bar machining procedure parameters, the equipment operation risk values and the environment parameters, procedure score values of the corresponding aluminum bar machining key procedures are obtained; the aluminum profile surface snapshots, obtained from multiple angles, of the target aluminum profile are combined and then sent to the pre-trained surface recognition model for evaluation, the quality inspection defect value output by the surface recognition model is obtained, and the surface quality defects can be continuously, accurately and rapidly found in a non-contact detection mode. The aluminum profile production efficiency and quality are fully improved, and compared with manual detection, a machine vision system has lower cost and higher efficiency.

The invention discloses a cigarette end surface defect detection method based on visual target detection, and the main design concept of the invention is that the method mainly comprises the steps: collecting original data of a cigarette end surface, carrying out the preprocessing of an original image, and segmenting the original image to obtain a cigarette end surface image; in combination with the scene characteristics of actually shooting the cigarette end face, expanding and reinforcing the cigarette end face image to obtain diversified cigarette end face visual samples; marking the cigarette end face visual sample and then training a target detection model based on deep learning; the trained target detection model is used for detecting a cigarette end face real-shot picture collected in the production process in real time, and the detected end face defects are subjected to quantitative characterization and then are subjected to score estimation. Compared with a traditional image processing method and a machine vision system, a new solution is provided for cigarette end face defect detection, and the dual requirements of a high-speed production line for detection precision and real-time performance can be met.

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 present invention relates to an integrated vision acquisition module for tunnel apparent defects, mainly used for solving the problems in existing detection devices of low integration level, complex debugging, low acquisition accuracy, poor environmental adaptability and the like. The vision acquisition module consists of a mounting back plate, a housing, a machine vision system, an integrated control system, a heat dissipation mechanism, a protective mechanism, and a wire arrangement mechanism. The machine vision system is encapsulated inside the mounting back plate and the housing, the heat dissipation mechanism is mounted on the back of the mounting back plate, the protective mechanism functions among the mechanisms of the vision acquisition module, and the integrated control system and the wire arrangement mechanism are mounted in the center of the front of the mounting back plate. The vision acquisition module uses a modular design, can implement accurate digital imaging of tunnel full-sections, has an overall protection grade of IP65 and good heat dissipation performance while maintaining a high degree of modularity, is adapted to different tunnel profile sections, and can complete rapid and non-contact detection of apparent defects in tunnel lining such as cracks, water leakage, and exfoliation and defects in ancillary facilities such as signal mechanisms, optical cables, and communication cables.

The invention relates to a quick locking method and structure for an automobile part mold. The device is characterized by comprising a fixed base, a locking cylinder and a locking unit, in the motion control of the locking mechanism, a motion smoothing algorithm and a trajectory optimization algorithm are introduced, the motion trajectory of the locking mechanism is smoothed, the speed change and the acceleration change in the motion process are reduced, the vibration and impact of the locking mechanism are reduced, and the stability of the locking process is improved, so that the consistency of the locking position of the automobile part mold is ensured; and after the motion control is optimized, transmitting the optimized motion track data to a machine vision system for monitoring the subsequent locking process.

The invention discloses an appearance, flavor and taste coupled edible mushroom grading and sorting device and method. The device comprises a hardware part and a software part. Hardware comprises an airflow setting device, a conveying belt, an industrial camera, a light source, a pneumatic electromagnetic valve, an industrial personal computer and other main components. And software functions: while controlling the conveyor belt to rotate, acquiring images of the dried edible mushrooms by using the industrial camera, processing the images, obtaining the appearance quality of the edible mushrooms in real time, obtaining the content of main flavor components and the rehydration ratio of the edible mushrooms according to the appearance quality by using a built-in database, and outputting the grade of the dried edible mushrooms. And the corresponding electromagnetic valves of the corresponding grades are controlled to act, and the dried edible mushrooms are blown into the frames of the corresponding grades. According to the device, the machine vision system and the air-blowing type executing mechanism are used for grading the dried edible mushrooms, the appearance, the smell and the taste are combined, a multi-index fusion grading method is formed, and the grading accuracy and consistency are improved.

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.

Systems and methods are disclosed herein for identifying cable damage using machine vision, such as on a cable being fed down a wellbore from a cable spool in an oil and gas operation. An example method can include providing a camera directed toward a cable that is winding upon, or unwinding from. a cable spool. The method can also include capturing a plurality of frames of images of the cable by the camera. such as by capturing video. The captured frames can be cropped to remove portions of the frames that do not include the cable. The method can further include processing the cropped frames using a machine-learning model. The machine-learning model can be trained using images of known cable damage as inputs so it can identify new instances of damage. The machine-learning model can further classify each cropped frame as including damage or not including damage.

The present disclosure provides a system, apparatus and method for controlling welding based on artificial intelligence. The artificial intelligence-based welding control system according to an embodiment of the present disclosure may include a machine vision system configured to photograph at least a portion of a welding object through a camera, and transmit the photographed image (hereinafter, “photography image”) to a relay system; the relay system configured to receive the photography image from the machine vision system, recognize a welding position of the welding object from the received photography image based on a pre-trained first artificial intelligence model, determine whether the recognized welding position is included in a designated position range, and when the recognized welding position is included in the designated position range, transmit the recognized welding position to a welding system; and the welding system configured to receive the welding position from the relay system, and perform welding at the received welding position.

Various example embodiments are presented herein for supporting object detection in a machine vision system. Various example embodiments directed to supporting object detection in a machine vision system may be configured to support range extension for object detection in a machine vision system. Various example embodiments directed to supporting object detection in a machine vision system may be configured to support flexible and efficient range extension for object detection in a machine vision system. Various example embodiments for flexible and efficient range extension for supporting object detection in a machine vision system may be configured to support flexible and efficient range extension for object detection in a machine vision system based on use of image slices.

Various example embodiments for supporting object detection in machine vision systems are presented herein. Various example embodiments for supporting object detection in machine vision systems may be configured to support range extension for object detection in machine vision systems. Various example embodiments for supporting object detection in machine vision systems may be configured to support elastic and efficient range extension for object detection in machine vision systems. Various example embodiments for supporting elastic and efficient range extension for object detection in machine vision systems may be configured to supporting elastic and efficient range extension for object detection in machine vision systems based on use of image slicing.

The invention relates to the technical field of insulator manufacturing, and discloses an insulator production system and method, a production preparation stage is composed of physical equipment for insulator production, the equipment executes specific production operation and is hardware support of the equipment, a full-process automatic and intelligent management and control system is constructed, and the production efficiency is improved. The links of production preparation, raw material stirring, forming, firing, surface treatment, quality detection and the like are covered, all the links are closely coordinated and follow strict standard specifications, the defects of unsmooth connection, efficiency loss and the like in a traditional production mode are effectively overcome, the production efficiency is greatly improved, the modern large-scale and high-efficiency production demands are met, ordered and efficient production promotion is guaranteed, and the production cost is reduced. Advanced means such as a machine vision system are adopted, insulator surface defects are recognized in a multi-dimensional mode, whether products are qualified or not is judged according to an accurate calculation formula through image collection, preprocessing, feature extraction and database comparison, the quality is strictly controlled, and the defective product outflow risk is reduced.

The invention provides automatic particle image velocity measurement equipment and a control method. The automatic particle image velocity measurement equipment comprises a three-axis lead screw open T-shaped sliding table, a four-axis lead screw closed gantry sliding table, a machine vision system, a PIV system, a controller and an automatic equipment control system, the machine vision system collects an image of the experimental model, the controller compares the image of the experimental model with a database, when the matching rate is larger than or equal to a preset percentage, feature matching is completed, a measurement result and a decision instruction are generated, and the automatic equipment control system controls the sliding table to move according to a given motion path. And meanwhile, the high-speed camera is controlled to trigger double-frame exposure according to given exposure parameters, and real-time cross-correlation analysis is carried out to obtain velocity field data, so that automatic PIV measurement is realized. According to the method, the problems of long time consumption, low efficiency and high operation repeatability caused by frequent manual calibration of PIV equipment in the experiment process are solved, a large amount of manpower and time cost is saved, and the research speed is increased.

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 fresh pepper sorting device based on machine vision. The fresh pepper sorting device is characterized by comprising a vibration feeding system, a conveying system, a machine vision recognition system and a sorting system. Vibration feeding movement of the vibration feeding system is achieved through an electromagnet, recognition of the machine vision system is achieved through a CCD industrial camera, and conveying movement of the conveying system is achieved through a motor. The invention is applied to the technical field of sorting machines. The automatic sorting device is designed for solving the problems that a traditional manual sorting mode is high in labor intensity, severe in working environment, low in sorting efficiency and poor in sorting quality. The whole device has the advantages of full-automatic intelligent equipment, rapid, efficient and accurate sorting of the fresh chilies and intelligent and integrated sorting work are achieved, the production efficiency is improved, the sorting period of the fresh chilies is shortened, and the fresh chilies sorting device can be widely applied to the related fields of the sorting industry and is efficient.

The invention discloses a barrier gate deviation correction method and system based on machine vision. The method comprises the steps that barrier gate rod position deviation data and acceleration are obtained in real time through a machine vision system; when the deviation data is greater than a smoothness threshold value, performing calculation to obtain an initial deviation correction speed; carrying out barrier gate motion state analysis operation to obtain a deviation correction speed; carrying out adaptive adjustment operation by adopting an adaptive adjustment algorithm based on PID (Proportion Integration Differentiation) control to obtain a dynamic deviation correction speed; inputting the dynamic deviation correction speed and the deviation data into a pre-trained barrier gate motion control model to obtain execution time and execution amplitude; based on the correction efficiency optimization target, carrying out optimal correction speed judgment operation to obtain an optimal correction speed; and performing correction process completion condition judgment operation to obtain a judgment result, and ending the correction process or re-executing calculation according to the judgment result to obtain an initial correction speed. According to the method, the deviation correction speed can be dynamically and stably optimized according to the real-time deviation data and the motion state.

The invention discloses a double-layer composite quartz crucible surface defect detection method based on machine vision, and relates to the technical field of crucible surface defect detection, and the method comprises the following steps: a conveyor belt conveys double-layer composite quartz crucibles to a detection area at a preset speed to ensure that each crucible can move in sequence on a production line; and a detection visual field of a machine vision system is stably entered. According to the method, key features such as the microstructure change index and the crack interval variability index are extracted, so that a machine vision system can identify complex defects more accurately, and misjudgment and missing detection are avoided. According to the method, the detection precision of complex defects is improved by enhancing image processing. And in combination with a machine learning model, the system intelligently evaluates the defect complexity and dynamically adjusts the conveyor belt speed. For simple defects, the high speed is kept, so that the production rhythm is not influenced; for complex defects, the speed of the conveyor belt is reduced, and time is provided for detailed analysis, so that the defect recognition rate is improved, and the product quality is ensured while efficient production is ensured.

The invention discloses a transform domain high-order tensor video denoising method for 3D machine vision inspection and measurement, and the method comprises the steps: carrying out the modeling of collected multi-channel multi-frame video data into a fourth-order tensor, carrying out the normalization processing of the fourth-order tensor, decomposing the fourth-order tensor into a third-order depth tensor and a fourth-order RGB tensor, and maintaining the image space, time and channel information. For different tensors, a transform domain high-order tensor denoising model is constructed, and mixed noise is removed by using a tube rank and constraint conditions, including suppression of low rank and sparse noise. An optimization problem is converted through an augmented Lagrange expression, and alternating optimization is carried out by adopting an alternating direction multiplier method frame until convergence is carried out to obtain denoised tensor data. And finally, splicing the de-noised depth and RGB information on a channel dimension through tensor inverse remodeling, and recovering a complete RGBD video data structure. According to the method, mixed noise is effectively removed, the data definition and reducibility are improved, high-quality input data are provided for a 3D machine vision system, and the detection and measurement precision and robustness are remarkably improved.

At least some embodiments described herein are directed to a machine vision method, which involves capturing both a two-dimensional (2D) image of an object to identify a barcode and determine 3D features, and a three-dimensional (3D) image of the environment. The 3D image data is examined for the presence of the 3D object features. If a feature is missing, a digital fault detection signal is provided. If a feature is present, at least one parameter of the machine vision system is adjusted. The method also includes decoding the barcode and determining the object's orientation based on the barcode location in the 2D image.