150results about How to "Reduce missed detection" patented technology

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 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.

The invention provides a surface defect detection method for aero-engine blades, and the method comprises the steps: sequentially shooting each blade, and splicing the blades into a panoramic image; due to the fact that the images are partially overlapped, the images can be sequentially spliced into the panoramic image according to the shooting sequence on the basis of the overlapped parts. The panoramic image not only contains enough defect information, but also can fuse a plurality of blades into the same image, so that the problem that the blades are difficult to match with the corresponding blades after the defects are identified is greatly reduced. Inputting the panoramic image into a recognition model added with a space-frequency dynamic convolution module, a Mangban module and a joint attention mechanism; and finally, outputting defect parts of all blades of the engine to finish identification. On the basis of the scheme, all defects on all the blades can be identified only by carrying out an identification process on the identification model once; the process has enough detection accuracy and does not need a large amount of calculation; and relatively high identification and blade quality detection efficiency can be kept.

The invention discloses a distributed photovoltaic panel anomaly detection method under a high-altitude view angle based on a YOLOv8n model, and belongs to the technical field of target detection. The model comprises the following steps: acquiring a distributed photovoltaic panel image data set under a high-altitude view angle, and performing data enhancement and labeling; an improved YOLOv8n model is constructed, and the improvement comprises the steps that a C2fAT module is introduced into a backbone network, and the small target feature extraction capacity is enhanced; an SPPF module is replaced by an SPPF-LSKA module, and complex background interference is suppressed; an EMA attention mechanism is introduced into the neck network, and the multi-scale adaptive capacity is improved; optimizing a training process by adopting a WIoU v3 loss function; and training and optimizing the model by using the training set, and finally outputting a detection result through the test set. According to the invention, the problems of false detection and missing detection of the distributed photovoltaic panel in a high-altitude view angle are effectively solved, the detection precision of the distributed photovoltaic panel is further improved, and inspection personnel are helped to troubleshoot the photovoltaic panel in an abnormal state in the high-altitude view angle.

The application discloses a fan shell front and rear hole position positioning method and system based on image recognition, comprising the following steps: S1: collecting a fan shell image through a calibrated binocular camera, and performing stereo correction and illumination invariance enhancement processing to obtain binocular enhanced image data; S2: performing instance segmentation on the binocular enhanced image data based on an instance segmentation neural network to obtain a hole position pixel region set with front and rear semantic labels; S3: performing data-driven hole position coarse matching on the hole position pixel region set through a graph neural network, and constructing a physical constraint rule model to optimize and verify the matching result, and outputting an accurate image matching pair set of the front and rear hole positions; and S4: based on the accurate image matching pair set, constructing a cost function to solve the shell pose and obtain final world coordinates, and realizing the front and rear hole position positioning of the fan shell.

This invention discloses a system and method for labeling automotive abnormal noise data, relating to the field of automotive NVH detection technology. The method includes: receiving raw signal data of automotive abnormal noise; performing time-frequency transformation on the raw signal to generate a time-frequency diagram; identifying key frequency ranges characterizing the abnormal noise features; then performing directional filtering to enhance the abnormal noise signal; identifying candidate time ranges for abnormal noise through an automatic detection algorithm; and outputting the final abnormal noise time range labels and key frequency ranges in a structured format after verification and correction. The system includes a data receiving module, a signal processing module, a display module, an intelligent labeling module, an audio playback module, a labeling output module, and an interaction module, achieving triple-assisted labeling through auditory, visual, and intelligent pre-selection methods. This solves the problems of low efficiency, low accuracy, and poor consistency in traditional manual listening methods, providing high-quality labeled data for deep learning classification of automotive abnormal noises.

The invention relates to a rail traffic unmanned aerial vehicle emergency patrol method based on an optical fiber monitoring and signal system, and the method is characterized in that the method comprises the steps: S1, obtaining all abnormal events in a target event interval and the position information of each abnormal event based on real-time physical parameters collected by a monitoring optical fiber in combination with train operation data; s2, grading the abnormal events based on the real-time physical parameters corresponding to the abnormal events to obtain grades of the abnormal events; s3, based on the position and the remaining endurance of each unmanned aerial vehicle, pairing the unmanned aerial vehicles for each abnormal event in combination with the position and the grade of each abnormal event, and performing flight path planning on each unmanned aerial vehicle based on the position of the paired abnormal event; and S4, controlling each unmanned aerial vehicle to inspect each abnormal event. Compared with the prior art, the accuracy of the abnormal event can be improved by combining the train operation data of the signal system.

The invention provides a directional ship detection method and system based on joint attention. The method comprises the following steps: acquiring image acquisition data; performing feature extraction by using a backbone network to obtain a multi-scale feature map, performing feature re-calibration on the multi-scale feature map, and outputting an enhanced target feature map; carrying out convolution operation on the enhanced target feature map by adopting self-adaptive geometric convolution to obtain ship direction characterization information; generating a candidate frame based on the ship direction representation information, calculating intersection-parallel-ratio statistical characteristics in combination with a ship real frame, and performing training sample adaptive distribution according to the intersection-parallel-ratio statistical characteristics; based on an Anchor-free detection framework, carrying out ship orientation detection by utilizing the training sample subjected to self-adaptive distribution to obtain a ship orientation detection result; through multi-scale feature re-calibration, adaptive geometric convolution and dynamic sample distribution, the ship direction and features in a maritime monitoring scene can be captured, and the detection precision and real-time performance are improved.

The application discloses a kind of ball grid array package chip solder ball bubble defect automatic detection method and system, it is related to chip intelligent detection technical field, the method is guided by designing semantic cross-layer fusion framework, realizes multi-scale feature balanced fusion in neck network by introducing feature alignment and redistribution module, strengthens small target details and semantic information using local semantic enhancement module, and the positioning accuracy of bubble is optimized in combination with smooth geometry positioning loss function.The application significantly improves the detection capability of small, fuzzy and occluded bubbles, has strong robustness and generalization, and can be widely used in solder ball bubble, material porosity and surface defect industrial vision detection scene.

This invention belongs to the field of intelligent detection technology for wind turbine blades, specifically relating to a feature detection method and system for multi-category surface damage on blades. The method includes: constructing a pyramid network structure EFPN to replace the baseline model's FPN+PAN neck network. The EFPN includes a pyramid feature enhancement (PFE) module, a multi-feature fusion (MFF) module, and a dynamic interpolation fusion (DIF) module, used to integrate multi-scale damage features and suppress background noise; designing a Focaler PIoU loss function, combined with a size-adaptive penalty factor and a dynamic sample weighting mechanism, to improve the model's ability to perceive difficult samples and the accuracy of bounding box regression; and using the DepGraph structured pruning method to compress the network, combined with an HDCFR hybrid knowledge distillation strategy, to reduce model complexity while maintaining detection accuracy. This invention achieves an mAP@0.5 of 84.5% on the WTBs GLE dataset, with reduced parameter and computational costs, combining high accuracy, lightweight design, and strong robustness, making it suitable for deployment on UAV edge devices.

This invention belongs to the field of power monitoring technology and discloses a brightness balancing and regional smoothing fusion algorithm for power monitoring images. The method includes: image denoising, using mean filtering and median filtering to smooth the image; image registration, performing optimal matching on two or more images obtained from different sensors, at different times, or from different angles; and image region fusion, smoothly fusing images with different angles and brightness contrasts. This method is applicable to panoramic video monitoring systems. Designed specifically for the complex lighting and high electromagnetic interference characteristics of power operation sites, this invention effectively eliminates on-site noise through adaptive preprocessing, and achieves a balance between global brightness uniformity and local detail preservation through layered brightness balancing, solving the problems of incomplete brightness balancing and detail loss in power scenarios encountered by general algorithms.

This invention belongs to the field of laser ultrasound imaging technology, specifically disclosing a laser ultrasound imaging method based on neural networks, comprising the following steps performed sequentially: S1, acquiring the A-scan signal; S2, decomposing and reconstructing the A-scan signal through empirical mode decomposition; S3, extracting the temporal features of the ultrasound signal using pooling; S4, establishing a neural network and training it multiple times to obtain multiple neural networks with different representation capabilities; S5, inputting the temporal features of the ultrasound signal of the sample to be tested into each trained neural network, and filling the recognition results into the corresponding positions according to the detection point order to form a pixel matrix; S6, superimposing all pixel matrices, setting a threshold to evaluate the state of each detection point, and generating the corresponding pixel image. The method of this invention is insensitive to noise and can be used to process low signal-to-noise ratio signals, improving the accuracy of material state identification. This invention is applicable to detecting the state of materials.

The application relates to the technical field of computer vision, in particular to a power safety monitoring image detection method and system, which comprises the following steps: inputting a to-be-detected power safety monitoring image and a generated image into a trained power safety monitoring image detection model respectively to obtain a detection result, the power safety monitoring image detection model comprises a feature extraction network structure, a feature distillation network structure and a multi-scale aggregation network structure in sequence, the feature extraction network structure is used for extracting local spatial features and global context information features from the to-be-detected power safety monitoring image and each generated image respectively, and fusing the two to obtain fused features corresponding to each image; the feature distillation network structure is used for extracting Value values and Key values from the fused features of each image, and obtaining splicing features based on the Value values and the Key values of each image; and the multi-scale aggregation network structure is used for processing the splicing features to obtain the detection result of the to-be-detected power safety monitoring image.

The invention relates to the technical field of image recognition, and discloses a wind power fan blade defect recognition method and system based on image recognition, and the method comprises the steps: carrying out the blocking cutting, illumination normalization and image enhancement processing of an original image, constructing a defect-free sample and a defect sample, and dividing the samples into a training set and a test set; the method comprises the following steps: taking a GANopen network as a basic framework, fusing the lightweight design of Mamba-YOLO, constructing a joint loss function by adversarial loss, reconstruction loss and coding loss based on a training set, carrying out unsupervised training, optimizing network parameters until convergence, and obtaining a defect identification model; inputting the preprocessed to-be-detected fan blade image into the trained defect recognition model, performing defect recognition and positioning, and outputting the defect type and position; based on an output result of the defect identification model, dynamically adjusting an early warning level and response measures through a self-adaptive early warning mechanism; according to the invention, the efficiency and accuracy of wind power fan blade defect identification are improved.

This application discloses a method, device, and medium for training and applying a geoscientific big data feature extraction model, relating to the fields of geoscientific big data processing and artificial intelligence technology. The method includes: cropping geoscientific modal sample data to obtain several candidate cropping samples corresponding to each geoscientific modal sample data; extracting visual token sequences and text token sequences from the candidate cropping samples and text task prompts; calculating cross-modal entropy based on the visual token sequences and text token sequences, and determining the optimal cropping; inputting the geoscientific modal sample data, the optimal cropping, and the text task prompts into a large language model to obtain sample feature extraction results; and training a trained geoscientific big data feature extraction model using a total loss function, where the total loss function includes the cross-modal entropy based on the candidate cropping samples and the sample feature extraction results. This method avoids the occurrence of feature extraction fragmentation or missed detection problems, and also reduces background interference, thus reducing false positives or false negatives.

The application discloses a non-electric detonating tube appearance-inner wall global flaw detection system, mainly relates to the detonating tube flaw detection technical field, and is through laser measurement detonating tube outer diameter and reads batch parameter, when the size is out of tolerance, through the mapping formula, the camera, light source equipment parameters are automatically calculated, the servo mechanism automatically adjusts and corrects the focus, the outer wall image is collected by the multi-camera, the rectangular ROI of the pipe wall is extracted through pretreatment and Hough circle transformation detection, the texture heat map is drawn, the defects are identified through threshold segmentation and morphological processing, the physical position and size of the defects are automatically classified and converted; the segmented detonation velocity is calculated by collecting the detonation wave signal, the inner wall coating defects are identified and positioned through sliding filter and double criterion, the correlation defects are distinguished by fusing the inner and outer wall defect positions, the product is determined to be qualified when there is no flaw, the detection result is visually output, the system is suitable for multiple specifications of pipe materials, the inner and outer wall defects are synchronously detected, and the system has high precision and few false alarms.

The application belongs to the technical field of vehicle information anonymization detection, and particularly relates to a multi-type image anonymization annotation dataset construction and target coverage rate determination method. The method is based on a face and license plate image dataset with double annotation of theoretical anonymization region and anonymization features, introduces an anonymization feature extraction branch in an improved YOLOv5-L model and performs cross-modal feature fusion, combines a plurality of loss functions with theoretical anonymization region positioning loss as the core and a two-stage progressive training and difficult example mining mechanism, and realizes precise learning of the anonymization features. After normalizing the input anonymization image, the model inference obtains the theoretical anonymization region coordinates and maps them back to the original size, and through non-maximum suppression and matching of the IoU threshold, the region coverage rate is calculated to determine the positive detection, missed detection and statistical false detection rate. The application effectively overcomes the feature dependency failure and model robustness problem, and realizes high-precision, low-misjudgment anonymization detection and evaluation under various anonymization conditions.

The utility model discloses an elevator overhauls detection device, including wire rope flaw detector, the both sides of wire rope flaw detector are connected with the positioning board, and the one side of positioning board is opened and has adjusted the groove, and the inside rotation of adjusted the groove has installed first guide roller, and the surface one side of positioning board is connected with support frame, and the transmission clamping mechanism is equipped between support frame and positioning board, and one side of transmission clamping mechanism is equipped with detection cleaning mechanism. The utility model has following effect: through transmission clamping mechanism, can to different diameter size's elevator wire rope carry out steady clamping, and drive wire rope flaw detector along elevator wire rope and move detection, realize the automatic flaw detection of elevator wire rope, through detection cleaning mechanism, can carry out effective cleaning to the dust, rust slag and other impurities on the surface of wire rope when detecting, reduce the interference of impurity to flaw detection signal, the accuracy of detection result is improved significantly, reduce the probability of missed detection and misjudgment.

The application belongs to the technical field of application software development test, and discloses an application software development test system with real-time vulnerability detection, which comprises a code real-time collection module, a multi-dimensional vulnerability detection module, a vulnerability accurate positioning module, a vulnerability risk quantitative evaluation module, a dynamic repair guidance module, a data storage module, a visual interaction module and an iterative optimization module, and each module cooperates to form a whole-process closed-loop vulnerability detection and management and control system. The application software development test system with real-time vulnerability detection is adopted, real-time capture, accurate positioning, risk evaluation and dynamic repair suggestion output of the vulnerability are realized, and the software development test efficiency and application program security are improved.

This application relates to the field of image processing technology, and in particular to a machine vision-based method for detecting the filling quality of sound-absorbing materials in sound barriers. The method includes: acquiring a sequence of synchronized infrared and visible light video frames, performing pixel-level registration, and then stitching them together to form multi-channel input features; constructing a detection network containing an improved octave convolution module to decompose the multi-channel input features into three branches: high-frequency, mid-frequency, and low-frequency; generating an offset field and performing deformable convolution on the mid-frequency branch; averaging the variance of each channel in the feature map along the channel dimension, adding a minimum constant, and taking the square root as a temperature coefficient; performing cross-frequency fusion through a cross-attention mechanism; and extracting the low-frequency feature map as a spatial prior weight matrix when a defect region exists in the current frame, which is then fed back to the next frame after inter-frame motion compensation. This application can introduce mid-frequency transition band information and combine the differences in local spatial distribution of dual modalities to adjust the convolution sampling position, reducing boundary jitter and missed detections in video stream detection.

This invention provides a method and system for detecting damage to the glass back panel of an OLED display, relating to the field of defect detection technology. The method includes: performing grayscale conversion and image enhancement processing on the original glass back panel image P0 to obtain an enhanced glass back panel image P1; performing threshold segmentation processing on the enhanced glass back panel image P1 to obtain the contour coordinates of the glass back panel; removing edge protrusion noise from the binarized glass back panel image P2 to obtain a standard glass back panel contour image P3; generating a glass back panel mask image P4 using the contour coordinates of the glass back panel; performing differential processing between the glass back panel mask image P4 and the standard glass back panel contour image P3 to obtain a glass back panel damage detection image P5; and determining whether the glass back panel is damaged based on the glass back panel damage detection image P5. This solves the problem in the prior art of OLED display glass back panel defect detection where the small difference between the defect and the background leads to over-detection or under-detection.

This utility model provides an adjustable AOI inspection machine, belonging to the field of AOI inspection technology. It includes a support and a workpiece to be inspected, as well as a drive assembly. The drive assembly includes a slide rail fixedly connected to the top end; a flexible assembly including a connecting slider slidably connected to the slide rail. Pressure flaps are fixedly connected to both ends of the connecting slider's outer side, and a vertical plate is fixedly connected to the outer side of each pressure flap. A horizontal plate is fixedly connected to the end of the vertical plate away from the pressure flaps. A rack is fixedly connected to the top end of the support, and a gear is movably connected to the top end of the support. This design achieves high-precision horizontal transport, flexible support, and anti-deformation protection for the workpiece to be inspected, while maintaining stability and precise positioning during high-speed movement. It effectively solves the problem that fixed lens positions are difficult to adapt to products of different specifications. Precise transport and stable support ensure clear and accurate images are obtained during inspection, reducing missed or false detections and improving inspection accuracy.

The invention discloses an intelligent detection system and method for coating color flaws and defects, and the method comprises the steps: selecting a standard sample, and creating a color code model based on a machine vision system; stacking and feeding, and conveying to a visual inspection station one by one; coating image information of all products in each tray is sequentially collected based on a machine vision detection system; feature parameters in the image information are extracted, and color flaws and defects of each to-be-detected product in each tray are compared and judged according to a color code model; marking unqualified products, and outputting a judgment result; and stacking and blanking. According to the invention, accurate quantification of coating colors of industrial products is realized, the defect of subjective judgment of human eyes is avoided, the detection efficiency and the defect identification precision are high, the manual dependence is reduced, the operation is convenient, mass production and full detection can be realized, and the pain point that manual detection and spectrograph detection cannot adapt to mass production is solved; and all microscope base product detection data in the corresponding charging tray can be called by identifying the identity information of the charging tray through the two-dimensional code, so that whole-course traceability is realized.

This invention provides a method for detecting rotating debris in coal flow based on physical fuzzy modeling and scene-customized attention. The method includes: acquiring a clear coal flow image and then labeling non-coal debris targets in the image; establishing a dataset; constructing a debris detection network; constructing a physics-driven motion fuzzy mapping model; using the motion fuzzy mapping model to dynamically blur the clear coal flow image, generating training samples simulating motion fuzziness phenomena in a conveyor belt; training and optimizing the debris detection network; inputting a real-time coal flow image into the optimized network to obtain candidate detection results for non-coal debris targets; post-processing the candidate detection results and outputting the final filtered detection results. This invention can improve the stability and accuracy of debris detection under complex conditions of high-speed coal flow, reduce the influence of motion fuzziness and background interference, and provide technical support for the safety monitoring and automatic sorting of coal mine conveying systems.

This application discloses a security protection method and related products. The method includes: identifying abnormal behavior in the data to be processed corresponding to a target vehicle based on a preset security rule base and a pre-trained anomaly recognition model to obtain a security recognition result; if the security recognition result indicates a first anomaly level, then performing local security protection on the target vehicle according to a local protection strategy; if the security recognition result indicates a second anomaly level, then obtaining the collaborative protection strategy sent by the cloud platform corresponding to the target vehicle, and performing collaborative security protection with other vehicles according to the collaborative protection strategy. This improves the security of the entire vehicle-to-everything (V2X) environment.

The invention relates to the technical field of agricultural remote sensing monitoring, in particular to a method and system for measuring the number (area) of plants in a tobacco field based on visible light images of an unmanned aerial vehicle, and is suitable for automatic and high-precision growth monitoring and resource accounting of a large-scale tobacco field. Comprising six steps of tobacco field visible light image acquisition, image preprocessing, tobacco field region segmentation, tobacco field plant target detection and plant number statistics, tobacco field area calculation, and result post-processing and output, and through combination of an improved target detection algorithm and an image segmentation technology, high precision of tobacco field plant number statistics and high accuracy of area determination are realized. Meanwhile, the detection efficiency is guaranteed, the actual requirement of large-scale tobacco field monitoring is met, data acquisition, processing, analysis and output can be automatically completed in the whole process, manual intervention is not needed, the monitoring efficiency is greatly improved, and the labor cost is reduced.

This application discloses a sample detection device and a sample analysis equipment. The sample detection device includes a carrier component, a carrier base, a light source component, and a detection component. The carrier component has a detection channel; the carrier base is at least partially disposed within the detection channel and is used to place the sample to be tested. The carrier base is used to move the sample to be tested relative to the carrier component along the scanning direction. The light source component and the detection component are disposed on the carrier component. The light source component generates detection light, and the detection component receives the detection light. The detection light forms an elliptical spot on the sample to be tested through a first optical path and is reflected to the detection component through a second optical path. The above-mentioned sample detection device can realize optical path detection of the sample to be tested. During optical path detection, it will not interfere with non-target samples. Moreover, the detection light irradiating the sample to be tested can form an elliptical spot. The formation of the elliptical spot allows more detection points to fall within the detection range of the detection component, which can reduce false detections and missed detections.

The present application relates to the technical field of image data processing, and particularly relates to a carbon slide edge detection optimization method based on cascade filtering. The present application extracts local mean, local median and local variance from the carbon slide gray image pixel by pixel, and corrects abnormal statistics after three layers of verification. The pixel uncertainty descriptor composed of the variance term, mean and median of local gray level quantifies and distinguishes the contribution proportion of Gaussian noise and impulse noise. The quantile feature working point is extracted from the uncertainty descriptor field, and the pixel-level hybrid weight is generated by the serial cascade link composed of the sorting filter, bilateral filter and anisotropic diffusion filter through Sigmoid mapping, so as to perform the cascade recursion in the convex combination fusion mode. The adaptive edge judgment threshold is constructed by combining the local gradient kurtosis and the local gradient absolute median difference with the centralization reference of the median, and the suspicious false edge is filtered through the connected component length threshold, and the final edge map is output after the secondary judgment of the corrected threshold.

The application discloses a satellite image on-orbit change detection method and device, a storage medium and an electronic device, and relates to the technical field of information. The method comprises the following steps: searching for reference images matched with the to-be-detected images, and performing block processing on the to-be-detected images; extracting multi-scale semantic feature maps of each block image in the plurality of block images and the reference images; determining semantic probability distribution vectors corresponding to each block image and the reference images according to the multi-scale semantic feature maps of each block image and the reference images; determining a change mask corresponding to each block image based on the multi-scale semantic feature maps and the semantic probability distribution vectors corresponding to each block image and the reference images, and simultaneously evaluating the change detection uncertainty of each block image; and based on the change mask and the change detection uncertainty, transmitting part of data under a sparsification back transmission strategy to complete on-orbit change detection. The application can meet the real-time response requirement of satellite image change detection.

This utility model discloses a rail coating thickness measuring instrument, relating to the field of rail inspection technology. The instrument includes a mounting plate, a testing mechanism, and a positioning mechanism, with a rail body positioned below the mounting plate. The instrument moves along the rail body via casters on the mounting plate, eliminating the need for manual operation. This significantly reduces the physical strain and difficulty of prolonged hand-held operation and avoids positional shifts and data fluctuations caused by hand tremors, ensuring the stability and accuracy of coating thickness measurement data. It is particularly suitable for continuous testing of long-distance rails, minimizing interruptions. Simultaneously, guide wheels embedded in grooves on both sides of the rail body provide precise positioning for the thickness measuring instrument as it moves the mounting plate, preventing lateral shift during testing.