88results about How to "Improve defect detection efficiency" patented technology

The invention relates to the technical field of surface defect detection, in particular to a photovoltaic aluminum frame surface defect detection system which comprises a surface defect detection platform, a surface information unit, an image processing unit, a progressive division unit, a defect positioning unit, a defect classification unit and a management display unit. According to the method, the appearance feature image of the photovoltaic aluminum frame to be detected is preliminarily analyzed, so that whether the surface of the photovoltaic aluminum frame to be detected has defect risks or not is known from the perspective of image difference, light source detection defect positioning analysis is carried out in a progressive mode, meanwhile, detection is carried out in a light source detection mode, and the detection accuracy is improved. According to the method, the defect position can be further determined, defect multi-level verification analysis is carried out in an information feedback mode so as to improve the accuracy and effectiveness of the detection result, and the to-be-detected photovoltaic aluminum frame with the defect is divided and managed in the information feedback mode so as to improve the defect processing effect of the subsequent to-be-detected photovoltaic aluminum frame.

The invention relates to an unmanned aerial vehicle defect detection method based on channel space fusion cross attention. The method comprises the following steps: planning a waypoint sampling route of a tower pole in a power transmission line; a high-resolution visible light image shot by the unmanned aerial vehicle at a low altitude is obtained and preprocessed; improving the YOLOv8 network model to obtain a YOLOv8-based unmanned aerial vehicle defect detection model, and training and verifying the YOLOv8-based unmanned aerial vehicle defect detection model; and identifying the defect positions and types of the copper-aluminum transition wire clamp and the pin missing in the tower. According to the invention, a multi-scale feature extraction network in a YOLOv8 network model is replaced by an ESSF module to solve the problem of severe scale change, shallow information and deep information are fully fused, more small target information can be captured, and a small target detection layer is added to enhance the perception ability of the model to a small object; a CSFCA attention mechanism module is integrated, global and local information can be fully utilized, background noise can be filtered, key information can be highlighted, and the efficiency of defect detection is improved.

The invention relates to the technical field of image recognition, in particular to a circuit board defect detection method and device, equipment and a storage medium, and the method comprises the steps: obtaining a to-be-detected image, and carrying out the preprocessing of the to-be-detected image, and obtaining a preprocessed image; inputting the preprocessed image into a pre-constructed defect detection model, performing feature extraction processing on the preprocessed image based on a pre-confirmed convolution weight, performing fusion processing on a plurality of feature maps of different scales based on a pre-confirmed fusion weight, and performing defect detection on the fused feature map by adopting a detection head to obtain a defect detection result; the defect detection method disclosed by the invention aims to solve the problems of high calculation complexity, poor real-time performance and insensitivity to small target detection in the existing PCB defect detection method. The convolution weight is adjusted by introducing a dynamic convolution selection mechanism, and it is ensured that the model has high precision when processing small defects; and the fusion weight is adjusted by introducing a weight adaptive mechanism, so that the calculation of redundant information can be reduced, and the detection efficiency is greatly improved.

The invention relates to the technical field of image recognition, in particular to a printed circuit board defect detection method and system based on image recognition. The method comprises the following steps: obtaining a printed circuit board image set and enhancing the printed circuit board image set to obtain a printed circuit board enhanced image set; obtaining circuit board image shooting environment data, and performing identification according to the circuit board image shooting environment data to obtain a circuit board material region image set; performing solder overflow detection according to the circuit board material area image set to obtain a solder overflow area image set; and performing heat dissipation defect detection on the circuit board material compensation image set according to the solder overflow area image set so as to obtain a heat dissipation defect image set, and performing heat dissipation defect feature integration on the heat dissipation defect image set so as to obtain heat dissipation defect feature data. The circuit board defect detection efficiency can be improved.

The invention provides a ceramic defect intelligent detection method, system and device based on deep learning and a storage medium. The method comprises the steps that a ceramic surface image is collected and preprocessed; performing feature extraction and classification on the preprocessed ceramic surface image by using a deep convolutional neural network to obtain a preliminary classification result; according to the preliminary classification result, performing defect positioning on the ceramic surface image by using a multi-scale feature fusion technology to generate a defect candidate area; performing secondary classification on the defect candidate area by applying a support vector machine to obtain a secondary classification result; and outputting a ceramic defect detection report according to the secondary classification result. According to the technical scheme provided by the invention, the efficiency, accuracy and robustness of ceramic defect detection are improved.

The invention discloses a medicine bottle defect detection method and system based on machine vision and deep learning, belongs to the technical field of defect detection, and solves the problems that a twin defect classification network is sensitive to a highlight area caused by light reflection in appearance image information and the defect detection accuracy of the twin defect classification network is interfered in the existing method. The method comprises the following steps: constructing a defect detection model based on deep learning in combination with a YOLOV5 model, carrying out conjoint analysis detection on a time sequence image set by the defect detection model in combination with a medicine bottle simulation model, carrying out defect identification on the time sequence image set by the defect detection model, positioning a medicine bottle defect area, and carrying out defect detection on the medicine bottle. Classifying and marking defect types of the defect areas of the medicine bottles in a short circuit and random vector machine combined mode; according to the invention, the mean filter is introduced into the defect detection model, so that background filtering can be carried out on the image acquired under machine vision under the condition of light reflection of the medicine bottle, effective information of a small-size target image is effectively reserved, and the defect classification and recognition accuracy is ensured.

The invention relates to a woolen sweater defect detection method. The method is characterized by comprising the following four main steps: a data preprocessing step, a model construction step, a model training step and a defect detection step. Wherein in the model construction step, strategies such as an attention mechanism and a network structure optimization are introduced, so that the identification capability of the model on woolen sweater defects is improved; in the detection process, automatic positioning and classification of defect areas are realized through a series of key functions. The method is high in detection efficiency, low in comprehensive detection cost and high in detection accuracy.

The invention relates to an X-ray-based defect identification method, belongs to the technical field of X-ray nondestructive testing, and solves the problems of low accuracy and efficiency of casting defect identification in the prior art. The method comprises the following steps: acquiring an X-ray image of a sample casting, and preprocessing the X-ray image to acquire an edge image of the sample casting; marking whether the contour has defects and defect types according to the area and defect features of each contour in the edge image of the sample casting; training a defect identification model by adopting the marked edge image of the sample casting; obtaining an X-ray image of a target casting, and preprocessing the X-ray image to obtain an edge image of the target casting; and inputting the edge image of the target casting into the trained defect identification model so as to identify whether the target casting has a defect and identify the type of the defect. According to the method, through combination of an X-ray imaging technology, an image processing technology and a machine learning technology, rapid and accurate identification of the internal defects of the casting is realized.

The invention discloses a method and system for detecting petrochemical equipment defects, and the method comprises the steps: dividing to-be-evaluated petrochemical equipment into a plurality of detection regions, and obtaining defect point cloud data of each detection region through three-dimensional shape measurement; according to the defect point cloud data of each inspection area, utilizing a defect identification model to identify defects of each inspection area and obtain vertex features of each defect; according to the vertex features and the defect point cloud of each defect, defect evaluation features of each inspection area are calculated, and the defect evaluation features comprise a defect opening area coefficient, a defect depth coefficient and a defect metal loss coefficient; and according to the defect evaluation features of each inspection area, obtaining a defect risk coefficient for comprehensively representing the severity of the overall defect features of the petrochemical equipment to be evaluated. According to the invention, the defect detection precision and the automatic detection efficiency can be effectively improved.

The invention relates to the technical field of pavement detection, in particular to a pavement defect detection method, pavement defect detection equipment and a storage medium. The pavement defect detection method comprises the following steps: firstly, obtaining a to-be-detected pavement image; inputting the to-be-detected pavement image into the trained pavement defect detection model to obtain a defect detection result of the to-be-detected pavement; the defect detection result comprises defect-free and existing defect types and defect areas, and the defect types comprise crack, net crack and asphalt strip crack. Wherein the pavement defect detection model comprises a teacher network, a reconstructed student network and a segmentation network, and meanwhile, a multi-scale feature fusion module is introduced in front of a decoder of the student network, so that feature information of different scales can be better restored. It can be seen that the adaptive pavement defect detection model is designed for pavement defect detection, the pavement defect detection efficiency is improved, and the method can be suitable for detection of various defect types.

The invention relates to an FPGA (Field Programmable Gate Array)-based surfacing workpiece surface defect detection method and device. The method comprises the following steps: acquiring a magnetic induction intensity data matrix; performing phase-locked amplification on the magnetic induction intensity data matrix to obtain an original feature data matrix; performing convolution filtering processing on the original feature data matrix to obtain a smooth feature data matrix; performing binarization processing on the smooth feature data matrix to obtain a binarized data matrix; whether magnetic field distortion exists on the surface of the surfacing workpiece or not is determined according to the binary data matrix; and when the magnetic field distortion exists on the surface of the surfacing workpiece, judging whether the magnetic field distortion is caused by surface defects of the workpiece or not, and outputting a detection report according to a judgment result. Defect detection is realized by utilizing hardware, the advantage of high operational capability of the FPGA is fully utilized, and the efficiency of defect detection is remarkably improved.

The utility model discloses a glass fabric warp knitting machine flaw detection device which comprises a base and a detection box arranged in the center of the top end of the base, the detection box is provided with supports which are symmetrically arranged, feeding rollers which are symmetrically arranged up and down are arranged in any one set of supports, ports for glass fabric to enter and exit are formed in the two sides of the detection box, and the two ends of the detection box are connected with the feeding rollers. And a control panel is arranged on one end surface of the detection box. The glass fabric defect detection device has the beneficial effects that the glass fabric can be smoothed through the electric push plate, the phenomenon that the glass fabric is wrinkled when entering or leaving the detection box is avoided, meanwhile, a camera module and related parts can move in the horizontal direction through cooperation of a threaded rod and a threaded sleeve, and then the glass fabric defect detection efficiency is improved; meanwhile, through cooperation of the first guide roller, the second guide roller and related parts, overall lifting or partial lifting of the bearing conveying belt can be achieved, and the detection accuracy is improved.

The invention discloses an indexable blade defect detection method, electronic equipment and a medium. The indexable blade defect detection method comprises the following steps: acquiring normal and defect samples of an indexable blade; constructing an indexable blade defect detection model, and taking indexable blade normal samples as a training set; the training process comprises the following steps of: inputting a normal sample of the indexable blade into a teacher encoder through an input layer, obtaining a first feature vector and a second feature vector through a feature compression module, and respectively inputting the first feature vector and the second feature vector into a first student decoder and a second student decoder; respectively calculating a first similarity between the ith teacher coding vector and the ith vector of the first student decoder and a second similarity between the ith teacher coding vector and the ith vector of the second student decoder; constructing a loss function based on the first and second similarities so as to train a model; inputting the indexable blade defect sample into the model obtained by each round of training, and screening out an optimal model; and obtaining a to-be-detected indexable blade image, inputting the to-be-detected indexable blade image to the optimal indexable blade defect detection model, and judging whether a defect exists or not.

The embodiment of the invention relates to the technical field of industrial defect detection, in particular to an image anomaly detection model training method, an image anomaly detection method and an image anomaly detection device. A specific embodiment of the method comprises the steps of obtaining a training sample set, a normal category prompt text, an abnormal category prompt text, a learnable normal category prompt template, a learnable abnormal category prompt template and an initial image anomaly detection model; model training is executed, wherein the normal category spliced text vector and the abnormal category spliced text vector are input into a text encoder, and normal category text features and abnormal category text features are obtained; performing image embedding processing on the sample image; inputting the image block embedded vector sequence into an image encoder to obtain a block feature sequence; inputting the block feature sequence, the normal category text features and the abnormal category text features into a segmentation decoder to obtain an abnormal segmentation prediction map; and determining an image anomaly detection model. According to the embodiment, the industrial product defect detection efficiency can be improved.

The invention relates to a wafer back defect detection method, device and equipment and a storage medium, and is applied to the field of wafer defect detection, and the method comprises the steps: determining a target focal length sequence of a microscope lens at a to-be-detected point according to the position of the to-be-detected point, and obtaining a target image sequence corresponding to the to-be-detected point according to the target focal length sequence; performing image fusion processing on each image in the target image sequence to obtain a target clear image of the to-be-detected point; and obtaining target clear images of all the to-be-detected points on the back surface of the to-be-detected wafer, and obtaining a defect detection result of the back surface of the to-be-detected wafer according to the color gradation difference of the target clear images of all the to-be-detected points. The wafer back defect detection method has the technical effects that the wafer image fusion time is saved, and the wafer back defect detection efficiency is improved.

The utility model discloses a flaw detection device for furniture plates, which comprises a portal frame and a rack, a translation component is arranged at the top of the portal frame, a vacuum chuck machine is arranged on the translation component, a movable scanning component is arranged at one end of the portal frame, the rack is arranged at the lower part in the portal frame, and a belt type conveying mechanism is arranged in the rack. According to the flaw detection device for the furniture plate, the bottom end of the plate adsorbed by the vacuum chuck machine is subjected to flaw detection through the camera on the scanning plate, and then the scanning plate is driven to rotate through the first motor, so that the top end of the plate placed on the belt type conveying mechanism is subjected to flaw detection; therefore, flaw detection can be carried out on the two surfaces of the plate in a plate blanking process, naked eye recognition by workers is not needed, the efficiency of plate surface flaw detection is improved, and meanwhile, the labor intensity of the workers is reduced.

The invention relates to an insulator material defect detection method, system, device, medium and program, and the method comprises the steps: carrying out the denoising filtering and image standardization operation of an insulator image obtained in advance, and obtaining a standard insulator image; performing multi-channel convolution and residual connection on the standard insulator image to obtain an insulator image feature set; performing global attention coding on the insulator image feature set to obtain an attention image feature set; extracting global channel image features from the insulator image feature set, and performing channel fusion and spatial fusion on the global channel image features to obtain perception image features; and performing layer-by-layer feature decoding on the attention image feature set by using the perception image features to obtain a defect detection result, thereby improving the insulator defect detection efficiency.

The invention relates to a defect detection method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: performing unified view angle processing on a plurality of acquired acquisition images on the surface of a product to obtain a plurality of projection images; wherein each collected image is an image obtained based on multi-view multi-light-source shooting; splicing the plurality of projection images to obtain a target image; and inputting the target image into a trained defect detection model for processing, and outputting a defect detection result of the product surface. By adopting the method and the device, the implementation cost of defect detection of various scenes can be reduced.

The invention relates to a multi-angle annular uniform lighting device and a design method. The device comprises a camera, an imaging lens, a displacement mechanism, a motor, an annular light source mounting base, an angle adjusting mechanism, an LED light source mounting base and a dimming LED light source. The design method comprises the following steps: determining key parameters such as a working distance and an illumination area of a light source according to an actual application scene; according to a preset result parameter, an aspheric plano-convex dimming lens generatrix is calculated, and a three-dimensional dimming lens is constructed; the dimming lens and the LED are combined to serve as a dimming LED light source; a plurality of dimming LED light sources are arranged on an annular light source mounting base at equal azimuth angles, and the illumination of the centrosymmetric dimming LED light sources is superposed and compensated to form an annular uniform illumination light source; a displacement mechanism and an angle adjusting mechanism are additionally arranged to achieve the annular light source with the adjustable working distance and the adjustable multiple angles. The invention has the advantages of small light source divergence angle, high illumination uniformity and strong adaptability to detection elements, and can reduce the interference of strong reflection in defect detection.

The invention discloses a bolt defect detection device and a bolt defect detection test block. The device comprises a detection rod, at least two detection probes, a connecting assembly, a sleeve and an auxiliary assembly, the connecting assembly comprises a common end and at least two connecting ends, the common end of the connecting assembly is connected with the first end of the detection rod, and the connecting ends of the connecting assembly are respectively connected with the detection probe; the sleeve is arranged at the end part of the first end of the detection rod in a sleeving manner, and the sleeve is used for adjusting the opening and closing angle between the connecting ends of the connecting assembly, so that the detection probe is attached to the inner wall of the to-be-detected bolt; the second end of the detection rod is connected with the auxiliary assembly; the auxiliary assembly is connected with the end part of the to-be-detected bolt; the auxiliary assembly is used for adjusting lifting and rotation of the detection rod so as to adjust the detection position of the probe. According to the technical scheme provided by the embodiment of the invention, defect detection can be carried out on the bolt under the condition that the bolt is not disassembled, and the bolt defect detection efficiency is remarkably improved while the bolt defect detection cost is reduced.

The present application relates to the field of semiconductor equipment technology, and provides an insulating coating defect detection method and detection system, the method comprising: contacting the conductive surface of the substrate to be detected that is opposite to the coating surface with the lower electrode to form a flat electrode; arranging the coating surface of the substrate to be detected relative to the upper electrode according to a preset spacing; applying a preset voltage to the upper electrode so that the upper electrode can form a tip discharge; starting a motion control module, clamping the upper electrode to move according to a preset detection path, and being able to cover the coating surface of the substrate to be detected, wherein the detection path is generated according to the shape of the substrate to be detected and the characteristics of the coating surface; checking whether there is a position on the coating surface where a tip discharge is generated, and the position where a tip discharge is generated indicates that the porosity of this position exceeds the set standard, and the position where a tip discharge is not generated indicates that the porosity of this position meets the set standard. The present application performs defect detection through tip discharge without damaging the coating, thereby improving the defect detection efficiency.

The invention relates to the field of carpet detection, in particular to a carpet surface flaw detection device which comprises a carpet body, light source assemblies arranged at the front end and the rear end of the carpet body and first suction assemblies connected to the light source assemblies. The suction structures are arranged at the upper end and the lower end of the straight carpet respectively, the same attraction force is applied to the upper end face and the lower end face of the carpet through the suction structures, yarn stretching out of the suede face can be influenced by the attraction force applied by the suction structures, meanwhile, the oblique light sources are arranged on one sides of the industrial cameras, and therefore the yarn stretching out of the suede face can be influenced. A light source is arranged on the carpet, a pattern lampshade is arranged on the light source, the light source penetrates through the pattern lampshade to irradiate a refractor installed at the front end of the carpet, the refracted light source stretches across the carpet and is projected to a light sensing plate arranged at the rear end of the carpet, and then a pattern projected on the light sensing plate is shot through a second industrial camera. And whether the pattern on the photosensitive plate is in a complete state is compared to detect whether the carpet surface has a wrong weaving phenomenon.

The invention relates to an optical measurement technology, and discloses a batch chip multi-angle defect detection method, control equipment and AOI equipment, and the method comprises the steps: placing a chip array after gold wire bonding into a detection area of the AOI equipment; controlling the camera array to shoot panoramic images of the chip array in multiple directions; carrying out image segmentation on the panoramic image in multiple directions to obtain a multi-angle image corresponding to each chip; carrying out defect detection on the chip without the unqualified image; an image supplementary shooting path is intelligently planned according to the chip position and direction to which the unqualified image belongs; controlling the motion camera to carry out supplementary shooting on the unqualified image along the image supplementary shooting path; carrying out defect detection on the chip after image supplementary shooting; and generating a defect detection result of the chip. The invention further discloses a computer readable storage medium. The invention aims to improve the efficiency of defect detection on batch chips while ensuring the quality of defect detection on the batch chips.

The invention relates to a wafer surface defect detection method, system and device and a storage medium, and relates to the technical field of semiconductors. The method comprises the following steps: acquiring point cloud data for the surface of a to-be-detected wafer, wherein the point cloud data comprises three-dimensional coordinates of all points on the surface of the to-be-detected wafer; according to the point cloud data, a first normal direction corresponding to each point is calculated, and each first normal direction represents the direction of a normal corresponding to the position where any point in the point cloud data is located; and for each first normal direction, calculating an included angle between the first normal direction and a preset contrast normal direction, and judging whether a defect exists in an area where a position corresponding to the first normal direction is located according to the included angle to obtain a surface defect detection result. The method has low requirements on ambient light, and can effectively improve the accuracy of defect detection.

The invention provides a semiconductor structure and a manufacturing method thereof, and a bubble defect detection method of the semiconductor structure, the semiconductor structure comprises a plurality of chip regions and cutting channel regions connected between the adjacent chip regions, and a bubble mark is formed in the cutting channel region at the periphery of each chip region; detecting the semiconductor structure to obtain a detection image containing the bubble mark; according to a design distance between the bubble mark and the chip area, disassembling the detection image to obtain an image of each chip area; and identifying the image of each chip area so as to judge whether the chip area has a bubble defect or not. According to the technical scheme, the bubble defect detection efficiency is improved, and the omission ratio is reduced.

The utility model relates to a brake master cylinder piston compensation hole defect detection mechanism, which comprises a placing table, an image acquisition assembly and a detection assembly, and is characterized in that the image acquisition assembly is arranged on the placing table and is used for carrying out image acquisition on a compensation hole of a to-be-detected piston to obtain a detection image of the compensation hole of the to-be-detected piston; the brake master cylinder piston compensation hole defect detection device has the advantages that semi-automatic defect detection processing of the brake master cylinder piston compensation hole is achieved, the compensation hole defect detection efficiency is improved, the detection precision is improved, and the detection precision is improved. And meanwhile, the situation of missing inspection or not-in-place inspection existing in manual visual inspection is avoided.

The utility model relates to the field of PCBA (Printed Circuit Board Assembly) detection, in particular to a high-resolution transmission type PCBA defect detection device. The technical problem to be solved by the utility model is to provide the high-resolution transmission-type PCBA defect detection device which can be used for automatically loading, unloading and placing PCBA, reducing the trouble of manual operation and improving the PCBA defect detection efficiency. A high-resolution transmission type PCBA defect detection device comprises a bottom plate, a conveying device, a placement block, a lifting device, a displacement device, a shooting mechanism, an irradiation device, a feeding and discharging device, a discharging device, a fixing rod, a fixing plate, a mounting frame, a first motor, a first guide rail, a first lead screw, a first screw block, a first sliding block, a lifting air cylinder, a fixing frame and a pneumatic suction cup. The PCBA automatic feeding and discharging device achieves the effects of automatically feeding, discharging and placing PCBA, reducing manual operation troubles and improving PCBA defect detection efficiency.

The present application relates to a kind of drug bottle body defect detection equipment and detection method, and it is related to the technical field of medical instrument detection;Including detection platform, medicine bottle conveying part and angle adjusting component;Medicine bottle conveying part includes two clamping conveyors, and clamping conveyor is equipped with clamping block at equal intervals;Angle adjusting component includes two telescopic skeletons, and limit rotating shaft is rotatably arranged at the corner of two telescopic skeletons, and limit rotating shaft on telescopic skeleton is equipped with angle adjusting belt;The present application can solve the following problems existing in the prior art:First, the prior art mainly shoots the image of the whole bottle body, and a large number of pixel points and mosaic will appear in the image when the image is enlarged and detected, which will affect the accuracy of bottle body defect detection, so that some small scratches on the bottle body cannot be effectively identified;Second, the prior art has certain singleness, and it can only detect defects for single size of drug bottle body.

This invention discloses an automatic imaging and defect detection method for capacitor wire mesh applicable to multiple sizes, belonging to the field of industrial automation inspection technology. The method includes the following steps: generating a corresponding imaging path based on the parameter information of the capacitor wire mesh frame to be inspected; controlling the imaging unit to acquire images of the capacitor wire mesh according to the imaging path; identifying regular rectangular wire mesh regions based on region positioning images, and constructing multiple region masks based on these regions; switching between foreground detection regions and background suppression regions between different region masks based on predefined defect types to be detected, and performing defect detection and classification within the corresponding foreground detection regions; generating an inspection report based on structured data; achieving refined partitioning detection through multi-region masks, effectively improving defect detection efficiency and avoiding human subjective errors, reducing the probability of missed and false detections, and accurately identifying various defects such as wire mesh blockage, expansion and contraction, latex peeling, and dirt.