14 results about "High speed vision" patented technology

The utility model relates to visual inspection technical field, and disclose copper wire film surface quality high -speed vision detection system, including device frame, the right side of device frame is equipped with the detection assembly for to copper wire surface detection, the inside of device frame is equipped with the cleaning assembly for to copper wire cleaning, the detection assembly includes mounting piece, mounting piece is equipped with two, two mounting piece all weld in the right side of device frame, the utility model discloses through vision sensor collection image, after gauss filter denoising, histogram equalization enhances image details, the image after processing can accurately identify scratch, bubble, wrinkle, film thickness uneven etc. defect type based on the semantic segmentation model U of depth learning Net, MaskR CNN, and the detection precision reaches 0.01mm 2 , and further effectively detect the defect of copper wire, and further reduce the subsequent processing of the copper wire with defect.

This invention proposes a spatiotemporally synchronized visualization measurement and analysis method, system, and device for flow-heat-phase. The method includes: acquiring a multimodal sensing dataset and performing spatial calibration and alignment; processing the aligned high-speed visual image to obtain a binary phase diagram, combining temporal information to identify and segment it, dividing and merging the liquid phase region, gas phase region, contact line region, and rewetting region to obtain a partitioned result map; identifying and fusing the infrared temperature image, binary phase diagram, and partitioned result map to output a spatiotemporally synchronized visualization measurement phase diagram for flow-heat-phase. By acquiring data to form a dataset and calibrating and aligning it, this invention achieves, for the first time, a precise spatiotemporally synchronized correlation of gas-liquid distribution, temperature field, and heat flow information; combining binary phase diagram generation, intelligent segmentation, and multimodal data fusion technology, it overcomes the limitations of traditional methods and can quantitatively analyze key physical processes under the interaction of multiple bubbles on complex surfaces; the final output phase diagram can reveal the coupling mechanism between the temperature field of the micro-region of the heated surface and the dynamic behavior of bubbles.

This invention discloses a saw blade detection method and system based on multi-source data fusion, specifically relating to the field of saw blade detection technology. By simultaneously acquiring high-speed visual images of the saw blade's front and side views and spindle current signals, the initial tooth profile reference features of the saw blade are extracted. Furthermore, the system monitors in real time changes in the straightness of the projection line, the amount of cutting edge retraction, and abnormal changes in the current signal during the saw blade cutting process. By fusing visual and current data analysis, the normal wear and degradation trend of the saw blade and abnormal tooth loss characteristics are observed, thereby achieving accurate identification of saw blade wear or tooth loss conditions. A comprehensive evaluation is then performed based on the defect rate of sawn products, ultimately outputting an accurate and reliable saw blade replacement control signal. This invention effectively improves the accuracy of saw blade condition detection and the timeliness of replacement decisions, avoiding processing quality problems and ensuring production efficiency and processing safety.

The present application relates to a kind of PCB drill bit micro-chipping online detection and early warning method.For the problem of difficult tool state identification under different materials and working conditions, a kind of high-speed vision, current, vibration and displacement signal is synchronously collected, a high-resolution multi-modal data set covering typical micro-chipping evolution stage is constructed, and based on normalized feature fusion, lightweight time coding and fingerprint database establishment, high-fidelity representation and online dynamic matching of micro-chipping state are realized.The system accurately identifies and predicts the evolution trend through dynamic time warping and Bayesian inference, and realizes adaptive early warning feedback loop by linking machining control system, while supporting incremental optimization of fingerprint database based on artificial review.The scheme effectively improves the sensitivity and discrimination accuracy of drill bit health state identification, and provides technical support for efficient and intelligent machining process monitoring.

The application discloses a kind of intelligent swing laser welding systems and methods of stator and rotor core, it is related to laser welding field, including swing laser welding module, multi-modal online detection module, intelligent control and feedback module and motion execution module, by optical coherence tomography, high-speed vision, multispectral sensing and infrared thermal imaging real-time acquisition melt depth, molten pool shape, splash, spectrum and temperature and so on Multidimensional characteristic signal.Based on the real-time identification of defect by CNN-LSTM hybrid architecture deep learning model, and through millisecond level feedback control dynamic adjustment laser power, swing parameters and protective gas flow, realize the closed-loop quality control of " perception-decision-execution ".Solved the welding defect frequent, thermal deformation difficult to control and quality detection lag and other problems in the prior art, significantly improve the welding yield and consistency of stator and rotor core, meet the mass production demand.

The utility model discloses book high -speed visual inspection equipment, include: area array detection section, including first detection line, be equipped with the area array camera for gathering book top surface, first side and second side image on first detection line top and both sides, line sweep detection section, including the interval area formed by second detection line and third detection line, be equipped with the line sweep camera for gathering book bottom surface, third side and fourth side image below interval area and both sides, the utility model discloses the combination of area array detection section and line sweep detection section carries out the detection to the six -sided appearance of book, can identify common appearance defect, also can add up the verification of specified two -sided appearance feature image such as high -grade defect of internal defect folding.

This invention discloses an adaptive layered collaborative optimization method for metal additive 3D printing, relating to the field of 3D printing. The method includes: simultaneously acquiring real-time morphology, temperature field distribution, and dynamic fluctuation parameters of the molten pool during the metal additive 3D printing process using a high-speed vision acquisition module and an infrared temperature measurement module; performing noise reduction preprocessing on the acquired multi-dimensional raw data of the molten pool, simultaneously extracting core features, and performing standardized quantization on the core features to generate a molten pool state vector. This invention can accurately perceive the molten pool state, quantify core features, adaptively generate layer thickness and matching process parameters, select the optimal solution through adaptation judgment, and dynamically adjust parameters throughout the process, thereby improving deposition stability and forming consistency, reducing printing defects, adapting to various metal materials, improving part precision and quality, reducing scrap rate, and optimizing printing efficiency.

This invention relates to the field of marine firefighting technology and discloses an adaptive control method and system for the trajectory of a marine fire monitor jet. The invention employs a triple closed-loop control architecture of "prediction-correction-re-prediction," establishing a dynamic jet trajectory model coupled with multiple physics fields. It comprehensively considers influencing factors such as the six-degree-of-freedom motion of the hull, three-dimensional wind speed and direction, fire-induced thermal flow, characteristics of the extinguishing medium, and pipeline pressure fluctuations. Initial jet state parameters are acquired in real-time using a high-speed visual sensor at the muzzle, and trajectory pre-correction is performed in advance by combining this with hull attitude prediction information. An adaptive parameter optimization algorithm based on transfer learning is used to continuously update model parameters to adapt to different working conditions. This achieves intelligent scheduling and trajectory planning for multi-monitor coordination, avoiding overlapping coverage and blind spots in firefighting. This invention solves the problems of jet control lag, low accuracy, and poor adaptability in existing technologies, significantly improving the speed and effectiveness of marine firefighting.

This invention belongs to the field of safety monitoring and fault diagnosis technology for mining equipment. Specifically, it discloses a laser and vision-based system and method for detecting misalignment in coal mine vertical shaft cages. The system includes a linear laser emitter, a high-speed industrial camera, an adaptive lighting unit, an inertial measurement unit, and an edge computing server. The edge computing server receives the image stream acquired by the high-speed industrial camera and the cage attitude information obtained by the inertial measurement unit. It uses the data from the inertial measurement unit to perform spatial consistency correction on the visual geometric features and outputs the cage misalignment amount after decoupling from cage vibration interference. The system adopts a technical path of establishing an absolute geometric benchmark through active laser projection, capturing dynamic scenes with high-speed vision, and intelligent decoupling and decision-making through a deep learning model. This effectively isolates cage vibration interference, improves the imaging quality of the joint area, and integrates multimodal features for accurate judgment. Ultimately, it achieves stable and accurate identification of 2mm-level cage misalignment in a strong vibration environment.

The application belongs to the technical field of high-speed vision, and discloses a vision system and method fusing events and RGB. The application obtains spatially aligned RGB frame data and event stream data by using an optical acquisition subsystem; time-aligns the RGB frame data and the event stream data by using a heterogeneous logic control subsystem, and generates a heterogeneous data packet with a unified timestamp; receives the heterogeneous data packet by using a fusion operation subsystem, and performs fusion calculation to obtain vision processing information. The application can improve the spatiotemporal alignment accuracy of the imaging scheme of the fusion event camera and the RGB camera.

The application discloses a production line packaging flaw intelligent diagnosis system based on high-speed vision and edge calculation, which comprises the following modules: a time domain feature construction module, which calculates original pixel gray extremum to construct a time domain feature vector; a high-frequency extremum monitoring module, which performs extremum checking on the vector to output a physical trigger level; a local material interception module, which intercepts local breakpoint pixel material flow in response to the trigger level; a microscopic slice generation module, which generates a microscopic image slice by two-dimensionally recombining the material flow; a physical space reconstruction module, which extracts an abnormal pulse count value and converts it into a position embedding code array; an input tensor fusion module, which fuses the slice and the embedding code array to construct a three-dimensional input tensor; a spatial bias diagnosis calculation module, which outputs a defective flaw probability vector through the tensor; and a production line intervention module, which generates a control instruction according to the probability vector to feed back a rejection mechanism. The application solves the problems of real-time response deficiency and resource redundancy caused by centralized transmission and processing delay in high-speed production line vision detection.

The present application relates to the technical field of wind power operation and maintenance and unmanned aerial vehicle inspection, in particular to a wind turbine shutdown and non-shutdown dual-mode intelligent inspection system and method, the present application obtains visible light, identifies the outline and spatial angle of the wind turbine components through fusion neural network, determines the windward surface and the leeward surface, calculates the reference hovering waypoint, uses the rotating target detection network to identify the pitch angle, roll angle and three-dimensional coordinates of the wind turbine blade, and matches with the shutdown posture template, if it is a shape, then marks the dynamic no-fly zone, when not shutting down, generates a dynamic follow-up waypoint sequence within the safety gap based on the reference hovering waypoint and the wind turbine blade motion trajectory, when shutting down, calculates the waypoints of the windward surface, the leeward surface, the leading edge and the trailing edge according to the angle, length and hub diameter of the wind turbine blade, and avoids the dynamic no-fly zone, the unmanned aerial vehicle flies along the planned route and adaptively adjusts the gimbal angle, adopts high-speed visual triggering or fixed-point shooting to collect the wind turbine blade image, and generates an inspection report through intelligent defect analysis.

This invention provides a high-speed visual online inspection system for bottle cap appearance defects and dimensional deviations, comprising: a bottle cap pose module for acquiring input data packets and detection formulas, and conveying bottle caps in a single row with equal spacing; an image acquisition module for acquiring a multi-source image set of bottle caps; a standardization module for performing geometric normalization, illumination normalization, and reflection decomposition based on the multi-source image set to obtain a standardized multi-view image package; a ROI mask module for performing structure-aware partitioning on the standardized multi-view image package to obtain a structured ROI mask; a size vector module for extracting backlight contours, size components, height components, and thread anomaly indicators, and outputting a size vector; an appearance defect detection module for performing geometric detection and supervised learning detection to form a final defect location map; and a constraint judgment module for risk judgment and process parameter optimization. This invention enables high-precision, low-fluctuation, traceable, and process-optimizable automatic detection of bottle cap dimensions and appearance defects.