19results about How to "Improve defect detection accuracy" patented technology

The invention relates to the technical field of image processing, in particular to a method and system for detecting surface defects of an insulating layer of a polyethylene insulated cable, and the method comprises the steps: obtaining a surface image of the insulating layer; the defect response index of each pixel in the surface image is calculated, and the defect response index is in positive correlation with the product of the gradient amplitude of each pixel in the corresponding local window and is in positive correlation with the absolute value of the difference value of the gray average values of all the pixels in the local window. According to the method, a defect response index is constructed by introducing a gradient direction angle variance, the defect response index and the defect response index are distinguished by utilizing the physical characteristics that the gradient direction of a highlight region is consistent and a defect region is disordered, and a defect significance enhancement factor is constructed according to the physical characteristics to dynamically adjust the Gaussian kernel scale. According to the method, large-kernel smooth interference is automatically adopted in a highlight area, small-kernel reserved details are adopted in a defect area, the limitation of a traditional fixed parameter algorithm is effectively overcome, and the defect detection precision under the complex reflective background is remarkably improved.

The invention relates to the technical field of image data processing, in particular to a hub surface defect detection method and system based on image processing, and the method comprises the steps: shooting the surface of a hub, obtaining an enhanced grayscale image, and carrying out the superpixel segmentation of the enhanced grayscale image, so as to obtain a connected domain; constructing a connected domain set of a target connected domain of an enhanced grayscale image shot over against the surface of the hub; respectively inputting the connected domains in the connected domain set into the trained SVM model, and outputting the defect probability that each connected domain belongs to each defect type; and performing weighted fusion on the defect probability of each connected domain belonging to the target defect type to obtain a comprehensive probability of the target connected domain belonging to the target defect type, and when the comprehensive probability is greater than a defect threshold, judging that the target connected domain has the defect of the target defect type. According to the method, the adaptive measurement distance is constructed, so that the superpixel segmentation process is more fit with the hub surface physical structure and the defect real boundary, and the defect detection accuracy is improved.

This application relates to the field of pipeline network detection technology, and discloses a visual detection method and system for pipeline network node defects based on a detection robot. The method includes: acquiring node images of a sewer network using a detection robot, and performing circle detection and sector segmentation on the node images to obtain multiple sector region blocks; extracting the block feature vector of each sector region block; calculating angular attenuation weights and radial attenuation weights, and constructing a node connection relationship matrix based on the angular attenuation weights and radial attenuation weights; combining the block feature vectors and the node connection relationship matrix to perform feature aggregation and illumination compensation to obtain a global feature vector; calculating angular deviation index, radial mutation rate index, and leakage index, and generating defect visual detection results, thereby realizing comprehensive diagnosis and spatial positioning of pipeline network node connection defects, and improving the defect detection accuracy of sewer detection robots in complex node environments.

The invention provides boxing and stacking equipment and a working method thereof.The boxing and stacking equipment comprises a rack and a box body defect detection mechanism, a box body stacking mechanism, an opening expanding and boxing mechanism and a mechanical arm which are arranged on the rack, the box body defect detection mechanism comprises a detection camera assembly, a sensor assembly, a conveying belt and a defect removing assembly, and the conveying belt is arranged on the rack; the conveying belt can drive boxes placed on the conveying belt to move in the conveying direction, the sensor assembly is arranged at the inlet end of the conveying belt, the sensor assembly, the detection camera assembly and the defect removing assembly are sequentially arranged in the conveying direction, the box stacking mechanism comprises a stacking plate and an adsorption head, and the opening and boxing mechanism comprises a push plate assembly and a supporting plate assembly. The pushing plate assembly and the supporting plate assembly are arranged on the two sides of the stacking plate respectively, the box sealing mechanism comprises a cover plate folding assembly, a linear sealing machine core, a corner edge sealing machine core and an adhesive tape supporting and pasting assembly, the cover plate folding assembly is arranged at one end of the linear sealing machine core, and the adhesive tape supporting and pasting assembly is arranged at one end of the corner edge sealing machine core.

The invention discloses a high-speed rail station building railway passing area structure health monitoring system and method and a medium, and the system comprises a concrete structure self-sensing monitoring subsystem which is used for collecting sensing data; the image acquisition and recognition subsystem is used for acquiring surface images of the concrete structure and the steel structure, inputting a surface defect recognition model and outputting a structured surface defect recognition result; the data acquisition and preprocessing unit subsystem is used for receiving the sensing data and the structured surface defect recognition result, and respectively performing preprocessing and feature extraction to obtain a sensing data feature vector and an image feature matrix; and the data fusion and analysis subsystem is used for receiving the sensing data feature vector and the image feature matrix, inputting the sensing data feature vector and the image feature matrix into a structure defect recognition model and outputting a structure defect recognition result. According to the invention, all-around, accurate and real-time monitoring of the concrete structure and the steel structure of the railway passing area of the high-speed rail station building from inside to outside is realized.

The present application relates to the technical field of power transmission line detection, in particular to a power transmission line fastener defect detection method based on image processing, comprising: 1, collecting multiple power transmission line fastener images, and using the multiple power transmission line fastener images to construct a power transmission line fastener dataset; 2, labeling the power transmission line fastener dataset, and dividing the labeled power transmission line fastener dataset into a training set, a validation set and a test set according to a specified proportion; 3, building an improved target detection model; 4, training the improved target detection model using the training set, and verifying using the validation set; 5, testing the trained target detection model using the test set. The present application overcomes the problems of small target size of power transmission line fasteners, complex environment background, large contrast of aerial light, and diversified angles, and can improve the precision of power transmission line fastener defect detection, providing technical reference for maintenance personnel to carry out power transmission line fastener defect detection work.

The application discloses a kind of multi-channel optical imaging system and device of wafer defect detection, it is related to semiconductor detection field.The system includes the objective lens, DIC prism and independent bright field, dark field, fluorescence PL imaging channel arranged in sequence.Wherein, after dark field and PL light are collected by objective lens, single pass through DIC prism is split into vertical o light and e light, and is transmitted along the same light path.The core is that polaroid is arranged between the DIC prism of dark field or PL channel and camera, so that its optical axis is collinear with DIC prism, and the light entrance surface is perpendicular to the light path.Rotating polaroid can only allow o light or e light single beam to be transmitted to camera, effectively eliminates ghosting, solves the problem of over-checking of particle defects, and improves the imaging clarity and detection accuracy of subsurface defects.

The invention provides a tool defect detection model construction method and a tool defect detection method, and relates to the technical field of image processing, and the tool defect detection model construction method comprises the steps: obtaining a training data set; for each first cutter image, performing feature extraction on each first cutter image by using a pre-constructed first unsupervised network to obtain a first feature map corresponding to the first cutter image; adjusting the channel dimension of the first cutter image according to a first feature map corresponding to the first cutter image to obtain a second cutter image corresponding to the first cutter image; and training the first supervised network according to the second cutter images corresponding to the plurality of first cutter images to obtain a second supervised network, and taking the second supervised network as a cutter defect detection model. Compared with the prior art, the accuracy of tool defect detection can be enhanced.

This application relates to a method, apparatus, computer equipment, and medium for detecting defects in compressed hardware fittings. The method includes: performing image enhancement processing on an acquired original hardware fitting image to obtain an enhanced image; extracting the hardware fitting contour from the enhanced image to obtain a contour image; extracting features from the contour image using a region localization model; obtaining candidate region images based on the extracted features; performing deduplication processing on the candidate region images to obtain a target region image; performing size correction on the target region image to obtain a crimped area image; performing a first defect detection on the crimped area image using a defect detection network model to obtain a first candidate defect image; performing a second defect detection on the crimped area image to obtain a second candidate defect image; and fusing the first and second candidate defect images to obtain a defect detection result. This method can improve the accuracy of defect detection.

The control device includes an acquisition unit and a determination unit. The acquisition unit is capable of acquiring a first estimated pixel value corresponding to a post-processing pixel group of a first region and a second estimated pixel value corresponding to a post-processing pixel group of a second region on the basis of a first image estimation model, a second image estimation model, and a pre-processing captured image of a substrate to be inspected. The first image estimation model corresponds to a pixel group of a first region in the pre-processing captured image and the post-processing captured image. The second image estimation model corresponds to a pixel group of a second region in the pre-processing captured image and the post-processing captured image. The determination unit is capable of performing an abnormality determination on the substrate to be inspected on the basis of a result of comparison between the first captured image of the substrate to be inspected and the first estimated pixel value and a result of comparison between the second captured image and the second estimated pixel value.

The application relates to the technical field of image processing, and particularly discloses a glass bottle defect recognition method and system, which is characterized in that the mouth, bottom and body of a glass bottle are photographed and imaged to obtain corresponding three views for preprocessing, so as to weaken and inhibit the noise of the collected three views and obtain a preprocessed image; the preprocessed image of a single channel is further fused, the pictures in three directions are subjected to feature extraction, and the pictures are fused into a three-channel feature map for defect detection according to different weights, so that the defect detection precision can be improved, and the detection task can be completed at a relatively fast speed; then, the calculation capacity of a convolutional neural network is utilized to collect and process image features of different degrees, and the features are subjected to convolution, pooling and feature fusion of different degrees, and then convolution up-sampling and the like, so that a target feature image is obtained, the image features can be fully extracted, and the defect recognition precision can be improved.

The present application relates to the field of industrial detection, and specifically discloses a general defect detection method for industrial manufacturing, comprising the following steps: S1, reference image construction and to-be-detected region calibration; S2, positive and negative sample storage and to-be-detected region extraction; S3, to-be-detected image acquisition; S4, similarity calculation and result output. The present application also discloses a general defect detection system for industrial manufacturing, comprising a reference image calibration module, a sample processing and storage module, an image registration alignment module, a similarity calculation and result output module. In actual use, the present application requires few samples, only 20-30 positive samples and 10-20 negative samples are needed for deployment, greatly reducing the cost of sample collection and labeling and solving the problem of scarce defect samples. In addition, the detection accuracy is high, the core registration algorithm has small error, the similarity calculation model improves the accuracy, and the adaptability is strong. When the product model is updated, the reference image and sample library can be replaced, meeting the flexible production demand.

The present application relates to the technical field of Mini-LED chip detection, and a Mini-LED repair method and device based on automatic optical detection, comprising: performing Gaussian filtering on an optical chip image to obtain a filtered chip image, training a neural network based on an original chip image set to obtain a defect area detection model, detecting the filtered chip image according to the defect area detection model to obtain a defect image area group, if the defect image area group is not an empty set, recording the product to be repaired as a repairable product, performing local contrast enhancement on the filtered chip image to obtain a target chip image, performing single chip recognition on the target chip image to obtain a single chip area set, identifying a chip area group to be repaired according to the single chip area set, and generating a repair opinion report based on the chip area group to be repaired. The present application can improve the accuracy of Mini-LED product defect detection and reduce the labor consumption of Mini-LED products.

This invention discloses a lightweight asphalt road defect detection method and system with partial multi-scale attention, belonging to the field of computer vision and intelligent transportation technology. This invention is applicable to the real-time detection and precise localization of defects such as cracks and potholes. It employs PMA modules, SCConv modules, and the inner-AIoU loss function to construct the required asphalt road detection model, achieving accurate detection of the road image to be detected. The model constructed in this invention, while maintaining its lightweight advantage, achieves significant improvements in speed, substantial increases in defect detection accuracy, and diversification of detected defect categories, fully meeting the needs of real-time detection. Furthermore, the detection model constructed in this invention also possesses good portability and scalability, making its integration with hardware devices more convenient.

This invention belongs to the field of road defect detection technology, specifically relating to a method for detecting road defects in UAV remote sensing images based on SCDLW-YOLO. The method includes the following steps: acquiring UAV road remote sensing images, performing data augmentation processing, constructing an expanded road defect dataset and dividing it proportionally into training, validation, and test sets; constructing an SCDLW-YOLO road defect detection model based on the YOLOv8s detection framework; inputting the training set into the SCDLW-YOLO road defect detection model for training; using the validation set to supervise and iteratively optimize the model training process; using the test set to further test and confirm the performance of the converged model; finally, obtaining the optimal SCDLW-YOLO road defect detection model and deploying it on a UAV or embedded device. This invention can effectively improve the detection accuracy and real-time performance of road defects such as cracks, potholes, and repair areas in complex scenarios, while also maintaining a lightweight model, making it suitable for road inspection and intelligent defect recognition using UAVs and edge devices.

This invention discloses a method and system for detecting defects in metal pipes based on eddy current induction. It solves the problems of poor detection accuracy, inability to locate defects, and inability to measure defect dimensions in existing metal pipe defect detection methods. The method includes the following steps: establishing a two-dimensional axisymmetric model of the metal pipe with an external circumferential defect using a CFD algorithm, and fitting a linear function of the magnetic field amplitude and defect geometric parameters; applying an alternating excitation magnetic field to the metal pipe to obtain the induced magnetic field generated on the pipe surface; obtaining the resultant magnetic field amplitude and acquiring a scanning curve showing the change of the resultant magnetic field amplitude with the scanning position through axial scanning; determining whether a defect exists on the metal pipe surface based on the scanning curve, and if a defect exists, locating the defect center based on the scanning curve, and obtaining the defect depth and width using an inversion algorithm. This achieves simultaneous quantitative measurement of defect depth and width, as well as precise location of metal pipe defects.

This invention relates to the field of image data processing technology, and more particularly to a method and system for visually monitoring the operating status of an energy-feedback elevator. The method includes: acquiring operating images of key components in the energy-feedback elevator; determining the local structural orientation angle of each pixel; determining the structural anomaly value of each pixel to obtain a structural anomaly feature map; constructing a weighted gray-level co-occurrence matrix in all directions at a preset distance, and determining the frequency of all gray-level pairs in the weighted gray-level co-occurrence matrix; determining whether there are surface defects in the key components of the energy-feedback elevator, classifying and evaluating the surface defects of the key components, and visually displaying the evaluation results on a monitoring interface. This invention analyzes the structural orientation angle based on pixel gradients, and combines the gradient magnitude with the neighborhood angle to evaluate anomalies, effectively distinguishing between normal textures and defects, and improving the accuracy of monitoring defects in periodic texture components.

This application relates to the field of image analysis technology, and provides a method, apparatus, and electronic device for detecting defects on the surface of a cavity, including: acquiring a target image to be detected and corresponding text prompt information to construct an image-text modal input pair; extracting image feature vectors and text feature vectors from the target image; and introducing HOPLORA pairs. n img and n txt Perform domain transformation and semantic alignment to obtain the transformed image feature vector. n img’ and text feature vectors n txt’ Calculate the image-text similarity score s corresponding to the target image and generate an anomaly scoring map. A To identify defect areas, a visual language model is used to enhance the model's understanding of the valve body structure semantics through image-text modal fusion; the HOPLoRA module performs efficient low-rank parameter adaptation, improving cross-scene detection performance; and pixel-level anomaly scoring maps are output based on image-text similarity to achieve fine-grained defect localization. This method significantly improves the accuracy of defect detection on the valve body's internal cavity surface, adapts to complex working conditions, and enhances the model's scalability.

This invention discloses a wafer defect detection method, system, and storage medium. The method includes: acquiring an image of a sawtooth region of a target to be detected in a wafer; extracting corner points from the image and calculating the angle value of each corner point; using corner points with angle values ​​within a preset range as first candidate contour corner points; obtaining the coordinate values ​​of all first candidate contour corner points; determining the tooth peak region and tooth valley region of the sawtooth region of the target to be detected based on the distribution of all first candidate contour corner points in the coordinate system; using the first candidate contour corner points located in the tooth peak region and tooth valley region as detection contour points of the sawtooth region of the target to be detected; matching the detection contour points of the sawtooth region of the target to be detected with the contour points of the sawtooth region of the template; and determining the sawtooth defect type based on the matching result. This invention is applicable to the detection of various sawtooth targets on wafers, especially to the detection of targets in sparse periodic roughening processes, such as wafer substrate colloids or grains.