239results about How to "Reduce missed detection" patented technology

The invention discloses a lightweight semantic segmentation network and method for power grid inspection, and the method comprises the steps: employing an encoder-decoder architecture, inputting original data into a lightweight MobileNet v2 backbone network for feature extraction at an encoder part, inputting an obtained feature map into a lightweight large-scale ASPP module, and carrying out the feature extraction; further performing multi-scale feature extraction on the high-dimensional feature map; and in the decoder part, high-dimensional features and low-dimensional features obtained by the backbone network are respectively input into a cross attention wavelet fusion module, after an obtained feature map is fused with the output of a decoder, the feature map is recovered to the size of original data through down-sampling operation by a full-dimensional dynamic convolution post-processing module, and a final prediction mask is obtained. According to the invention, by optimizing a coding and decoding structure, the receptive field of the model and the modeling capability of a narrow and long target are improved, the balance between the precision and the complexity can be effectively realized, and the method is suitable for an unmanned aerial vehicle inspection system with limited resources.

The invention discloses a weld defect 3D point cloud detection method, and relates to the technical field of defect detection, and the method comprises the following steps: S1, point cloud data acquisition and preprocessing; s2, point cloud data enhancement and feature extraction; s3, weld defect detection and identification; and S4, explaining a detection result. According to the 3D point cloud detection method for the weld defect, the point cloud data is enhanced by means of point cloud completion and density increase, so that incomplete structural information and semantic information of a target due to few obtained target point clouds are avoided, the influence of point cloud sparsity on a detection result is effectively relieved, and the detection accuracy is improved. Therefore, false detection or missing detection of weld defects is effectively reduced, meanwhile, detection results are labeled and explained in various modes of colors, shapes and labels, various presentation modes of generating a three-dimensional model, labeling defect positions and providing detection reports are provided, and the detection results can be presented to users in an easy-to-understand mode.

The invention discloses a saliency target detection method for an intelligent auxiliary driving system, and the method comprises the following steps: 1, obtaining a driving scene image based on a driver visual region in real time through a vehicle-mounted image collection device, and carrying out the preprocessing of the image; step 2, performing bottom-layer multi-scale feature extraction, interaction and global enhancement of feature information on the backbone network, and extracting rich multi-scale feature information; step 3, the neck network comprises an AESwinTransform attention module, and a cross-layer feature guide fusion module and a multi-level feature interaction fusion module in the TFG network, and the multi-level features extracted from the backbone network are fused together; step 4, detecting and classifying the feature map; and 5, inputting the adversarial sample and the normal sample into the model in a mixed manner, and performing adversarial training by using the trained model. According to the method, the detection precision of the small saliency target can be improved, the generalization ability and robustness of the model in a complex environment are improved, and the interference of irrelevant scene information is reduced.

The invention relates to the technical field of machine vision and detection, in particular to a shoe pattern defect detection system based on the visual technology, which comprises an acquisition processing unit, a three-dimensional model unit, a defect detection unit and a defect processing unit. The three-dimensional model unit reconstructs an accurate shoe pattern three-dimensional model in sequence, the defect detection unit divides the model into a plurality of areas, calculates the average curvature, and accurately analyzes and judges the flatness defect degree by adopting a plane fitting algorithm based on a least square method and a curvature difference algorithm based on a sliding window; and the defect processing unit automatically processes the shoes with serious defects, so that high efficiency, accuracy and automation of shoe pattern defect detection are realized, and the quality detection level of shoe production is powerfully improved.

The invention discloses a PCB production line defect detection analysis method and system based on an identification model, particularly relates to the technical field of PCB quality detection, and is used for solving the problems of low PCB defect detection precision and high omission ratio in the prior art. The method comprises the following steps: scanning the whole surface of a PCB to generate a thickness distribution diagram, identifying a thickness non-uniform region by adopting a threshold segmentation algorithm, and marking a key detection region; potential defects irrelevant to process fluctuation are identified through historical production data analysis; and adjusting the polarization angle of the light source to analyze the reflectivity difference, and identifying the hidden defect area in combination with the microstructure of the material. The union set of the potential defect area and the hidden defect area is marked as the suspected incomplete area, and the influence of the suspected incomplete area on the performance of the PCB is analyzed according to the quality standard, so that the detection precision and comprehensiveness are improved, and the high-quality control of the PCB is realized.

The invention discloses an unmanned aerial vehicle mechanical arm control system and method based on machine vision, and relates to the technical field of mechanical arm control. The system comprises an image acquisition module, a feature point extraction and matching module, a pose analysis module, a control parameter calculation and adjustment module and a motion control and execution module. The image data acquisition module acquires target area image data; the feature point extraction and matching module is used for extracting feature points of a target object from a reference image, constructing a feature set, extracting and matching the feature points of real-time image data, and screening effective matching points; the pose estimation module analyzes the actual pose of the unmanned aerial vehicle mechanical arm according to the matching points and calculates the actual pose and the expected pose through an optimization algorithm; the control parameter calculation and adjustment module is used for calculating the deviation between the expected pose and the actual pose, setting a control gain and updating a motion control parameter; and the motion control and execution module updates the motion track of the unmanned aerial vehicle mechanical arm in real time based on the adjusted control parameters.

The invention discloses a steel plate butt weld pseudo defect ultrasonic detection method, system and device and a medium, and relates to the technical field of weld detection, the method comprises the following steps: setting a plurality of detection areas on a steel plate butt weld based on acquired structure parameters and surface characteristic data of a to-be-detected steel plate; performing multi-frequency and multi-angle ultrasonic scanning according to a preset first detection parameter by taking each detection area as a basic unit to obtain corresponding echo signal data; analyzing and identifying potential pseudo-defect signals, and calculating corresponding pseudo-defect characteristic values according to the pseudo-defect signals; taking each detection area as a basic unit, performing pseudo defect feature analysis according to the second detection parameters, and determining reliability evaluation data of pseudo defects; based on the pseudo-defect reliability evaluation data and the pseudo-defect characteristic value corresponding to each detection area, the pseudo-defect condition of the steel plate butt weld is analyzed, and a final weld quality comprehensive evaluation result is generated. According to the invention, the detection accuracy of steel plate butt weld detection is improved.

The invention relates to the technical field of braid quality detection in industrial production, in particular to a defect data processing query method and system for braid visual inspection. The method comprises the steps of obtaining braid image data, performing enhancement processing on an image based on a dynamic illumination compensation algorithm, and generating a high-quality image in combination with material characteristics; inputting the enhanced image data into a deep learning defect detection model, detecting and classifying defect areas in the image, and updating the model weight based on defect feature parameters; performing multi-dimensional indexing processing on the detected defect feature data to generate a defect distribution database supporting real-time query; and the defect distribution database and the production process parameter sequence are combined, the causal relationship of defect occurrence is analyzed, process optimization parameters are generated and fed back to the detection module, and self-adaptive updating of model parameters is completed. According to the invention, high precision, high efficiency and closed-loop optimization of braid defect detection are realized, and intelligent support is provided for industrial production quality control.

The invention relates to the technical field of assembly welding, and particularly discloses an assembly welding positioning method and device for automobile parts. A welding state image between a target hush pipe and a target partition plate is collected through a camera; carrying out welding state mask segmentation on the welding state image by using an image identification and analysis algorithm based on computer vision, and then carrying out welding state feature extraction on the segmented welding detection area state mask image; and according to the expression obtained through neighborhood importance optimization of the extracted welding detection state characteristics, intelligent detection on whether defects exist in the welding state between the target hush pipe and the target partition plate or not is achieved. In this way, tiny defects such as tiny cracks and internal pores can be captured, and therefore the accuracy of defect recognition is improved. Moreover, the detection speed is greatly improved, the consistency and stability can be maintained, and the probability of missing detection or false detection is reduced, so that each welding point is ensured to be subjected to strict quality control.

The invention provides an infrared small target detection method and system based on self-adaptive partial self-attention, and the method comprises the steps: carrying out the processing of a single-frame infrared image through a double-residual attention block and a feature encoder, and obtaining a coding feature map; performing maximum pooling down-sampling processing on the coding feature maps of different levels to obtain coding features, and splicing the coding features in a channel dimension to obtain a multi-scale advanced coding feature map; obtaining a global context long-range feature map based on the multi-scale advanced coding feature map; obtaining a code-added feature map based on the global context long-range feature map; and inputting the decoded feature map into the prediction head to obtain a prediction result. According to the method, the salient small target in the infrared image is effectively extracted, the self-attention block of the self-adaptive part is utilized to integrate global context information, the long-distance dependence learning ability of the model is enhanced, and the perception ability of the position and the shape of the infrared small target is effectively enhanced.

The invention provides an AI analysis method and system based on a straight ethyl mirror image, and relates to the technical field of AI.The AI analysis method comprises the steps that multi-scale features of the straight ethyl mirror image are extracted through a pre-trained deep convolutional neural network, and feature fusion is carried out through a self-adaptive feature weight distribution module; and performing lesion area detection on the fused feature map by using a double-branch detection network, and classifying the detected lesion areas. And performing three-dimensional reconstruction based on a continuous orthoscope image sequence, mapping a detection result into a three-dimensional model, realizing accurate positioning and three-dimensional visualization of a lesion area, and recording three-dimensional coordinate position information of the lesion area. The method can improve the accuracy and efficiency of the image analysis of the straight ethyl mirror, and assists a doctor in diagnosis.

The invention discloses a meat product foreign matter on-line detection method and system, and belongs to the field of visual detection.The detection method comprises the steps that a fixed detection area is set, a multi-mode sensor is installed above the fixed detection area, and the multi-mode sensor collects data and transmits the data to a data processing center through a wireless network; the data processing center decompresses and preprocesses the received data and fuses the image data twice to obtain a final fused image; according to the feature information in the final fusion, extracting fusion feature data; analyzing the feature data by using a support vector machine, and judging whether foreign matters exist or not and classifying the types of the foreign matters; a detection result is fed back to a production line control system in real time, and meat products containing foreign matters are positioned and removed. According to the method, the multi-mode sensor and an efficient data analysis technology are combined, and the accuracy and the real-time performance of meat product foreign matter detection are effectively improved.

The invention relates to the technical field of auxiliary paper marking, and particularly provides an intelligent paper marking method based on a large language model and optical character recognition. The method comprises the steps that test paper and answers are processed through a large language model, test question analysis texts are obtained, and the test question analysis texts comprise analysis texts of the answers; processing the test paper image needing to be marked through an optical character recognition technology to obtain a corresponding answer text; and through comparison of the test question analysis text and the answer text, score data of each question is determined, and the score data comprises scores and comments. According to the intelligent paper marking method provided by the invention, the problem of low paper marking accuracy of an intelligent paper marking method in related technologies can be solved.

The invention discloses a power transmission line foreign matter identification method and system based on an improved YOLOv10 model. Relates to the technical field of power transmission line detection. According to the method, the method is improved on the basis of the existing target detection technology in the scene of a special power transmission line, the structure of a YOLOv10 model is optimized and improved by introducing an ODConv dynamic convolution module, a CAFM hybrid convolution attention mechanism module and an NWD loss function on the basis of the YOLOv10 model, in the detection stage, foreign matters are recognized through a foreign matter detection model, and the foreign matters are recognized through a foreign matter recognition module. Inputting the image containing the foreign matter into a component detection model to determine the position of the component, and finally performing hazard assessment and making a response according to the relative position of the foreign matter and the component. The detection precision of the YOLOv10 model is effectively improved, the hazard assessment capability is improved, and the detection efficiency is improved, so that the efficiency of foreign matter removal work of the power transmission line is improved, data management can be performed, powerful support is provided for operation and maintenance of the power transmission line, and safe and stable operation of electric power is guaranteed.

The present invention discloses a visual inspection device for producing self-adhesive label paper, which relates to the technical field of visual inspection of label paper, and comprises an inspection table and a leveling device, wherein a winding device is arranged on the top of the inspection table, a maintenance frame is fixedly installed on the top of the inspection table, a visual inspection device is fixedly penetrated on the top of the maintenance frame, a driving plate is fixedly installed on the top of the inspection table, a servo motor is fixedly installed on the front side of the driving plate, a placement frame is fixedly installed on the top of the inspection table, a rotating motor is rotatably installed on the rear side of the placement frame, a fill light device is fixedly installed on the output end of the rotating motor, a correction rod moves in a direction close to the label paper to drive the correction frame to move and perform position correction on the label paper to be inspected, and the position correction ensures that the label paper is in a correct and centered position during inspection, thereby improving the accuracy of image capture and further reducing the scrap rate caused by misjudgment.

The invention relates to the technical field of printing quality, and discloses a label printing quality optical detection device which comprises a working table, an inclined detection table is fixedly installed at the top of the working table, a detection cylinder is arranged above the inclined detection table, an optical detection head is arranged over the inclined detection table, and first hinge seats are arranged on the two sides of the inclined detection table; a moving assembly is arranged below the two first hinge seats, a discharging assembly is arranged on one side of the two first hinge seats, and when the first hinge seats and the optical detection head are driven by a downward moving assembly to move downwards along the inclined face of the inclined detection table, the detection cylinder can roll along the inclined face of the inclined detection table under the action of gravity; the label printed on the outer wall of the detection cylinder can be detected by the optical detection head all the time, the label can be exposed under the optical detection head in an omnibearing and dead-angle-free mode, the situation of missing detection or false detection caused by improper position or shielding of the label is reduced, and the detection accuracy is improved.

The invention discloses an equipment maintenance management system based on RFID. The system comprises an RFID tag, an RFID reader-writer, a tool management terminal, a maintenance personnel management terminal, an equipment management terminal and a central database. According to the system, identification and tracking of equipment, tools and maintenance personnel are achieved through the RFID technology, management and control of the equipment maintenance process are achieved through the tool management terminal, the maintenance personnel management terminal and the equipment management terminal, and storage and analysis of equipment maintenance data are achieved through the central database. The system can improve the efficiency and quality of equipment maintenance, reduce the cost and risk of equipment maintenance, and guarantee the safety and reliability of equipment.

The invention relates to a lightweight target detection method fusing an attention mechanism, and belongs to the technical field of image recognition. The method comprises the following steps: firstly, making a target image data set and preprocessing; secondly, combining an MBConv structure and a CA attention mechanism to construct a CSPCAMBConv structure, and using the CSPCAMBConv structure to reconstruct a backbone network of Yolov5s; a bidirectional feature pyramid fusion module is adopted in the neck network, and feature information transmission and sharing are enhanced; besides, a scale inconsistency item and a width difference item are introduced into a loss function to balance the difference between a prediction frame and a real frame, so that the model is more sensitive to the scale difference of the target in the training process. And finally, pruning the model through sparse training to compress the size of the network model so as to obtain an optimal lightweight model. The method is suitable for an edge terminal with limited computing resources, targets of different scales can be accurately positioned under a complex background, and the problems of wrong detection and missing detection of the targets are effectively reduced.

The invention discloses a system and a method for intelligently detecting foreign matters in a rail transit interval. The system comprises a data acquisition unit, a data transmission unit, an image processing and analyzing unit, a foreign matter algorithm unit, an alarm processing unit, a data recording unit and a user interface unit, the data acquisition unit is installed on a track line and is used for carrying out continuous image acquisition on a traffic interval and detecting abnormal conditions on a track in real time. The data transmission unit is used for transmitting the collected information; the image processing and analyzing unit is used for processing and analyzing the acquired data and identifying possible foreign matters; the foreign matter algorithm unit is used for identifying and positioning possible foreign matters; when the alarm processing unit detects the foreign matter, an alarm is given to related personnel in time; the data recording unit is used for recording all foreign matter detection data, the user interface unit is used for feeding back the obtained foreign matter information, monitoring and detection operation can be automatically carried out, manual intervention is not needed, and therefore efficiency is improved, and labor cost is reduced.

The invention relates to the technical field of image analysis, in particular to a defect detection method, system and device for a power transmission line and a storage medium. A defect detection neural network composed of a multi-level image feature extraction module, a multi-level image feature fusion analysis module, a first image semantic feature enhancement module, a second image semantic feature enhancement module, an image semantic feature fusion analysis module and an image segmentation module is constructed, so that the calculation burden can be remarkably reduced; the method is suitable for real-time application of the unmanned aerial vehicle, most importantly, the small target can be separated from the background, and the detection precision of the small target in a complex background and a shielding scene is improved; meanwhile, in the process of training the defect detection neural network, the matching degree of a prediction frame and a real frame is improved by controlling the sum of intersection-to-union ratio loss of image bounding boxes, coordinate loss of the bounding boxes and shape loss of the bounding boxes to be smaller than a loss threshold value, and therefore the accuracy of small target detection under the condition that the background is complex or the target shape is irregular is guaranteed.

The invention relates to the technical field of rail transit detection, and discloses a bogie bolt looseness detection method based on a series steel wire, which comprises the following steps: identifying an area where 8-shaped series anti-looseness steel wire series bolts are located by using an improved YOLOv8 bolt area detection model, and inputting a trained U-net network for identification, judging whether the output result is an image containing two sides of the 8-shaped anti-loosening steel wire or not; carrying out binarization on the image containing the two sides of the 8-shaped anti-loosening steel wire, and then carrying out skeleton extraction to obtain a first single pixel line and a second single pixel line; calculating an included angle between the first single pixel line and the second single pixel line, and judging whether the included angle is smaller than a set threshold value or not; extracting interval pixel points of the first single pixel line, performing rectangular coordinate conversion, and performing fitting to obtain an interval pixel point fitting curve; a first curve section and a second curve section are selected, the curvature of each interval pixel point in the curve sections is calculated, and whether bolt looseness exists or not is judged through the maximum curvature value; the method improves the bolt detection precision and efficiency.

The invention discloses an ore grinding hard rock target detection method and system based on a HardRockNet network structure, and the method comprises the steps: constructing an ore grinding hard rock target detection model which comprises an input layer, a multi-scale feature extraction layer, a DFPN feature fusion layer and an output layer which are sequentially connected in series; the multi-scale feature extraction layer comprises a plurality of feature extraction sub-layers which are sequentially connected in series and have different feature extraction scales, the output ends of the plurality of feature extraction sub-layers are connected with the input end of the DFPN feature fusion layer, and the plurality of feature extraction sub-layers perform feature extraction by adopting a DPBlock module; and detecting hard rock on the ore grinding picture by using the trained target detection model. According to the invention, the DPBlock module and the DFPN module are combined, and a HardRockNet network structure is constructed, so that the small target detection precision is improved, the robustness of complex backgrounds and target overlapping problems is enhanced, and accurate detection of hard rocks is realized.

The invention discloses an optical remote sensing image saliency target detection model and detection method, and belongs to the technical field of target detection. The saliency target detection model comprises a PVT backbone network module, an edge information extraction and fusion module BG, a feature enhancement module HAM, two space attention modules, a feature interaction module KTM and a feature fusion module. According to the invention, by introducing an edge detection module (BG) and a feature enhancement module (HAM), the target detection precision under a complex background is effectively improved. Compared with the existing CNN-based method, the method provided by the invention can better process targets with different scales and direction changes, reduces the probability of missing detection and false detection, and enables saliency target detection to be more complete and accurate.

The invention discloses a detection and tracking method and device for dense pedestrians, relates to the technical field of computer vision, and can solve the problem of low tracking accuracy during real-time detection and tracking in a scene with extremely dense pedestrians in the prior art. According to the specific technical scheme, firstly, a dense pedestrian detection data set and a tracking data set are obtained; an alpha-EIoU loss, a GAM attention mechanism and a Swin Transform module are used for improving a YOLOv5s algorithm, and a detection model is obtained; training and optimizing the detection model to obtain a target detection model; a ShuffleNetV2 module is introduced to improve a DeepSORT algorithm, and then training is performed to obtain a target tracking model; and detecting and tracking the pedestrian target video according to the target detection model and the target tracking model to obtain an experimental result of detection and tracking. The method is used for detecting and tracking dense pedestrians.

The invention provides an identification and early warning method and system for dangerous behaviors of inland ships. The method comprises the following steps: acquiring infrared monitoring video image data of an inland river water area to obtain a standard data set; then, performing ship main shaft identification according to the standard data set, performing bow and stern identification according to a ship gray value, and performing ship motion direction judgment; then, according to a method based on sample generation, a large number of ship target tracking virtual samples are generated; then, inputting the generated ship target tracking virtual sample into a DVIS model tracking training framework to realize tracking training, and obtaining a tracking model; and finally, according to the obtained tracking model and input real sample data, analyzing motion track data and a motion mode of the ship, and carrying out identification and early warning on dangerous behaviors of the ship. According to the method, tracking of ship behaviors under the complex ship shore environment condition is achieved, early warning is conducted on the ship dangerous behaviors, and the recognition rate of the ship navigation dangerous behaviors is further increased.

The invention discloses a bridge crack identification method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring a bridge image; a bridge image is input into the trained bridge crack recognition network, and the trained bridge crack recognition network is used for performing feature extraction on the input bridge image by adopting a C2fPonv module, performing feature fusion on the extracted features by adopting a multi-scale attention module EMA, recognizing the fused features to obtain a plurality of prediction frames, and identifying the prediction frames; screening the prediction frames by adopting non-maximum suppression to obtain a bridge crack recognition result; a loss function adopted in the network training process is used for inhibiting the gradient when the difference between a prediction frame and a target frame is smaller than a set threshold value, so that the network focuses on identification of a difficult-to-predict sample.

The invention relates to the technical field of multi-information joint sensing, in particular to a multi-information joint sensing method and system for an unmanned surface vessel. The method comprises the following steps: acquiring target detection data of the water surface environment of the unmanned surface vessel; coordinate transformation and point cloud coordinate correction are carried out on the target detection data, and grid situation maps are constructed respectively; performing target segmentation and contour feature extraction on the grid situation map to obtain a target contour; a shore-based target and an overwater target are distinguished according to the complexity and the area of the target contour; many-to-one target matching and target attribute establishment of the laser radar and the navigation radar are completed; completing frame-by-frame matching of the target and updating of a target state list; and completing many-to-many matching of the target and the target state list established by Kalman filtering, and updating the attribute information of the target. According to the invention, advantage complementation of the laser radar and the navigation radar can be realized, leak detection and loss of the target are reduced, false alarm of the target is reduced, and tracking precision and stability of the target are improved.

The embodiment of the invention provides a fault positioning method and device based on association rules, equipment and a program product. Relates to the field of energy storage system fault diagnosis. The method comprises the following steps: acquiring a state code, a fault code and an operation parameter of the energy storage thermal management equipment; based on the state code, the fault code and the operation parameter, generating a fault rule base by adopting an association rule mining algorithm; unsupervised clustering analysis is carried out on the operation parameters, and abnormal data deviating from the standard are screened out; fusing the abnormal data with the fault rule base to generate a fault map; and based on the fault map, a fault positioning report of the energy storage thermal management equipment is output, the fault positioning report at least comprises a fault propagation path and a maintenance scheme, and the fault propagation path is a path of a causal relationship between the fault code and the abnormal mode. The method is used for achieving the effect of improving the fault positioning efficiency and accuracy.

The invention discloses a dynamic traffic target detection tracking and forward collision early warning method, and solves the problems of low target detection precision, poor tracking stability and high real-time requirement in a complex traffic environment. By improving the YOLOv8 network, the small target detection precision and the lightweight performance of the network are improved; and the tracking process of DeepSORT is optimized by using a complete intersection-parallel ratio, so that the tracking effect of the vehicle in a rapid moving and shielding scene is improved. In addition, the distance between the vehicles and the collision time are calculated in real time based on the Mobileye model, and three-section type risk early warning is achieved. The vehicle collision early warning method has the advantages of high detection precision, strong real-time performance, wide application and the like, and is suitable for vehicle collision early warning tasks in automatic driving and intelligent traffic systems.

The invention relates to the technical field of aluminum alloy detection, and discloses clamping equipment for aluminum alloy detection, which comprises a base, two electric guide rails are symmetrically mounted on the base, an area for placing an aluminum alloy profile is formed between the two electric guide rails, brackets are mounted on the two electric guide rails, a bearing seat is mounted on each bracket, and elastic rolling pieces for pressing the aluminum alloy profile are mounted on the bearing seats; the electric guide rail can drive the support to drive the elastic rolling piece on the bearing base to roll on the surface of the aluminum alloy profile. And a hardness detection piece is mounted in the elastic rolling piece. Rapid identification of surface unevenness is achieved through the elastic rolling part and the displacement sensor, local hardness can be quantitatively detected through the built-in hardness detection part, internal defects caused by insufficient hardness are prevented, dynamic pressing and friction resistance detection of the profile in the full-length range are achieved in cooperation with the adjustable limiting detection part and the pressure sensor, and the detection precision is improved. The comprehensiveness of pre-screening before flaw detection is improved, and the condition of missing detection is effectively prevented.