305results about How to "Improve the detection rate" patented technology

The invention discloses a perception fusion method of an unmanned driving system, and relates to the field of unmanned driving, and the method comprises the steps: obtaining a laser point cloud, image data and millimeter wave radar data; a Zhang's calibration method is adopted to obtain internal parameters of a camera, and a first pose transformation relation of a camera coordinate system relative to a laser radar coordinate system is solved; a second pose transformation relation of the millimeter wave radar relative to the laser radar is obtained by using the millimeter wave reflector; a Point RCNN method is adopted to obtain first target information; obtaining target information II by adopting a YOLOv8 method; obtaining target information III by adopting a clustering algorithm; and carrying out coordinate unification on the target information by utilizing a pose transformation relation, fusing the multi-sensor target information under a unified coordinate system by utilizing an unscented Kalman filtering algorithm to obtain target state estimation, and generating an unmanned driving control instruction. The target sensing precision is improved aiming at the low target sensing precision of multiple sensors under the unmanned driving scene of the well industry and mining in the prior art.

The invention relates to a solanaceous fruit picking robot and a fruit cluster observation pose estimation method and device.The solanaceous fruit picking robot comprises the fruit cluster observation pose estimation device, the device is used for achieving the fruit cluster observation pose estimation method, and the method comprises the following steps that RGB image data and depth data of solanaceous fruit plants are collected; respectively normalizing the RGB image data and the depth data, inputting the RGB image data and the depth data into the trained bimodal fusion semantic segmentation model, and predicting masks corresponding to two types of fruits and stalks in the RGB image; extracting depth information corresponding to a mask communication region, and carrying out local mask refinement in combination with a density clustering method; single fruit bunch point clouds are extracted in a clustering mode, point cloud center information of each cluster of fruit bunches is calculated, and stalk point clouds close to the fruit bunches are extracted for linear fitting; and determining the observation pose of the end camera according to the relative position of the point cloud center of the fruit bunch and the vertical point of the corresponding stem fitting straight line. The identification and positioning precision of the picking mechanical arm tail end camera on the fruit stem picking point is effectively improved.

The invention discloses a two-dimensional and three-dimensional fused fabric defect detection method, and belongs to the technical field of machine vision recognition. The technical problem of limitation of a traditional two-dimensional machine vision system is solved. According to the technical scheme, the method is characterized in that multi-angle light source imaging is carried out, a detected fabric is polished through light sources at different angles, and two-dimensional images of illumination at different angles are obtained through shooting of a linear array camera; and two-dimensional and three-dimensional information fusion: carrying out pixel-level fusion on the shot two-dimensional grey-scale map and the reconstructed gradient map and depth map with the three-dimensional information, carrying out high and low frequency decomposition and fusion on the two-dimensional information and the three-dimensional information through non-subsampled contourlet transformation, and fusing the two-dimensional defect feature information and the three-dimensional defect feature information into one map. After the non-subsampled contourlet transform decomposes the image, the frequency domain space is divided into wedge-shaped directional sub-bands; defects are identified based on the deep learning model. The method has the effect of improving the accuracy of textile quality detection.

The invention discloses a security detection method and device, computer equipment and a storage medium. The method comprises the steps of obtaining to-be-detected data; and performing security detection on the to-be-detected data based on a pre-trained target large-scale language model to obtain a security detection result for the to-be-detected data, the target large-scale language model being obtained by training based on a corpus in the network security field. Through the semantic understanding ability of the trained target large-scale language model in the network security field, matching rules and features do not need to be constructed depending on expert experience, and the target large-scale language model obtained after training is used for security detection, so that hacker attacks with various changes can be coped with, the detection rate of hostile attacks is increased, and the security detection efficiency is improved. And the false alarm rate is reduced.

The invention discloses a small target detection method fusing a region average gray difference and an infrared image block, and belongs to the technical field of image processing, target detection and image segmentation. The method comprises the following steps: obtaining an original image matrix containing a single-frame infrared small target; constructing an original image matrix into an infrared block image matrix by using a local region construction method; performing robust principal component analysis on the infrared block image matrix to obtain a background matrix and a to-be-detected target matrix; obtaining all connected domains in the target matrix to be detected, namely suspected target areas; a rectangular local area is constructed for each suspected target area, and the center of each rectangular local area coincides with the center of the corresponding suspected target area; and calculating and comparing the average gray difference between each suspected target area of the target matrix to be measured and the corresponding rectangular local area to obtain a real target. According to the technical scheme of the invention, the regional average gray difference and the infrared image block model are fused, so that the detection rate of the target is effectively improved.

The invention belongs to the technical field of biology, and relates to a plasma marker for colorectal cancer diagnosis or prediction and application of the plasma marker. The invention creatively provides the application of specific enhancer RNA (eRNA58712) in serum in early diagnosis or detection of colorectal cancer, which marks that important progress is achieved in the aspect of finding novel biomarkers. The expression quantity of the eRNA58712 in serum of a colorectal cancer patient is obviously higher than that of a healthy individual, and the expression level of the eRNA58712 has an obvious positive correlation relationship with occurrence and development of the colorectal cancer, which means that rapid and accurate early recognition of the colorectal cancer can be realized on a molecular level by measuring the concentration of the eRNA58712 in a blood sample.

The present invention provides a DC power supply system and a DC arc detection method and device thereof, which simultaneously collect the AC signal and various DC signals of an inverter; when there is external noise introduced into the AC side of the inverter, the noise can be crosstalked to its DC side through the inverter; and the operation of the inverter itself may also cause noise, and the noise may appear on both sides of it at the same time; therefore, according to the AC signal and the various DC signals, it can be judged whether the DC power supply system has noise interference; if there is noise interference, the arc detection data is denoised, so that the arc detection data can be adjusted according to the actual noise situation of the system; and then according to the data results after denoising, it is judged whether the inverter has arcing; compared with the prior art, it can reduce the false alarm rate and improve the detection rate, and can be applicable to the situation where high-power inverters or inverters with nonlinear loads are prone to harmonic deterioration.

The invention relates to the technical field of remote sensing image processing, in particular to a residual feature fusion inversion-based accumulated snow coverage estimation method, which comprises the following steps of: acquiring terrestrial satellite data and performing image data screening according to a screening condition; acquiring meteorological satellite data, preprocessing the meteorological satellite data and splicing the meteorological satellite data with the geographic elevation data; generating a binary accumulated snow map, calculating the accumulated snow coverage according to a definition method and a meteorological satellite resolution, and obtaining an accumulated snow coverage label; cutting the meteorological satellite image into a rectangle according to the accumulated snow coverage label; establishing an accumulated snow coverage estimation model based on deep learning; optimal experiment parameters are found through repeated experiment debugging and comparison; after the prediction is completed, obtaining respective accumulated snow coverage estimation results and splicing the accumulated snow coverage estimation results into a complete accumulated snow coverage estimation result; and the cloud shielding interference is effectively filtered by combining the generated snow coverage estimation result with the satellite data of the front and back time phases, so that the method has higher time resolution and reliability.

The invention discloses a concrete slump real-time detection system and method based on machine vision. Video streams in the concrete discharging process are collected through a high-definition camera device installed at the front end of a concrete discharging opening; transmitting the video stream to a data center, analyzing the video stream in real time by using a pre-trained three-dimensional deep convolutional neural network model, extracting flow characteristics of the concrete mixture, and calculating a slump value; judging whether the slump numerical value exceeds a preset range or not, and if yes, sending alarm information to a central management platform through an alarm module; and adjusting the material ratio of the concrete by field operators according to the slump numerical value and the alarm information. According to the invention, the advanced deep learning algorithm is utilized to carry out real-time analysis on the concrete blanking process, the slump value is accurately calculated, and automatic and intelligent quality detection is realized.

The invention discloses a sealant gluing quality detection method, which comprises the following steps: S1, acquiring images of a vehicle body sealant gluing area, and matching the images; s2, carrying out binarization processing on the corrected image, setting a target point and a width threshold value in the binarized image according to the vehicle model, measuring the gluing widths of the two sides of the target point, and comparing and judging the gluing centering degree; s3, performing gridding processing on the corrected image, equally dividing a gluing area in the gridding image along the length direction, setting the area of a target gluing area and a gluing coverage rate threshold value, calculating the area of an actual gluing area, and calculating the area of the actual gluing area; according to the area of the actual gluing area, the area of the target gluing area and the gluing coverage rate, whether missed gluing and glue breaking occur or not is judged; and S4, collecting image information in the step S2 and the step S3, importing the image information into a visual deep learning system, and establishing a deep learning model. The detection method disclosed by the invention can be used for comprehensively judging the quality according to the centering degree of gluing and whether missed gluing and broken gluing occur or not.

The present invention provides an antibody composition for detecting multiple myeloma based on full-spectrum flow cytometry and its application, belonging to the technical field of multiple myeloma detection. The antibody composition includes CD3 antibody, CD20 antibody, HLA-DR antibody, CD8 antibody, CD11b antibody, CD10 antibody, CD71 antibody, CD28 antibody, CD14 antibody, CD13 antibody, Igκ antibody, CD15 antibody, Igλ antibody, CD117 antibody, CD56 antibody, CD45 antibody, CD19 antibody, CD34 antibody, CD138 antibody, CD4 antibody, CD38 antibody, CD22 antibody and CD27 antibody. The kit containing the antibody composition can be applied to a full-spectrum flow cytometer to accurately perform preliminary screening on patients with multiple myeloma.

The invention relates to the field of photoelectric material and device physics, and discloses a preparation method of a Ga2O3: Sn / HfOx heterojunction photoelectric sensor, and the method comprises the following steps: S1, cleaning a substrate, S2, preparing a Ga2O3: Sn thin film, S3, growing an electrode, S4, preparing an HfOx thin film, and carrying out neural synapse bionic application of the Ga2O3: Sn / HfOx heterojunction photoelectric sensor. Comprising Ga2O3: Sn / HfOx synaptic plasticity research and Ga2O3: Sn / HfOx photoelectric sensor sensing and storage calculation integrated application research. The Ga2O3: Sn thin film is prepared by adopting a sol-gel method with accurate and controllable doping amount, donor doping with controllable concentration is realized by controlling the mass of the added tin sheet, so that the photocurrent and responsivity of the photoelectric sensor are increased, the HfOx thin film is prepared by adopting a magnetron sputtering method, the photoelectric sensor with a double-layer structure is constructed, the detection rate and responsivity of the device are improved, and the photoelectric sensor is suitable for the field of photoelectric sensors. An ultraviolet signal is used to simulate a plurality of synaptic functions, including EPSC, PPF and empirical learning behaviors. Furthermore, a high-linearity light-enhanced electrical suppression behavior is realized, and a basis is provided for realizing a high-precision image recognition function.

The invention discloses a substation defect identification method and device, a medium and equipment. The method comprises the following steps: identifying inspection picture data in an inspection task, and determining an abnormal inspection picture data set; searching a normal sample data set with the highest similarity with the abnormal inspection picture data set from a normal sample database; registering inspection pictures in the abnormal inspection picture data set with pictures in the corresponding normal sample data set, and determining a registered abnormal inspection picture data set; detecting a region of interest in the normal sample data set by using an equipment detection model, and performing global change detection on the registration anomaly inspection picture data set and the normal sample data set by using a change detection model to determine a predicted change region; and rejecting an invalid change detection result of the prediction change region by using the region of interest, determining an abnormal detection result of the abnormal inspection picture data set, and determining a defect identification result of the transformer substation according to the abnormal detection result.

The invention discloses an enrichment and culture method based on immunomagnetic beads, which comprises the following steps: acquiring a to-be-enriched sample, and judging the bacterial concentration of the to-be-enriched sample; if the bacterial concentration is greater than a first preset threshold value, directly performing first operation on the to-be-enriched sample; if the bacterial concentration is smaller than a second preset threshold value, performing a second operation on the to-be-enriched sample; and if the bacterial concentration is greater than a second preset threshold value and less than a first preset threshold value, performing third operation on enrichment of the to-be-enriched sample. According to the present invention, with the method, the enriched bacteria characteristics are significant, the positive reporting rate is improved, the positive reporting time is substantially shortened, the immunomagnetic beads are adopted to enrich and culture the bacteria in the sample so as to shorten the time, reduce the false negative, improve the positive reporting rate, further improve the microbiological identification level, shorten the identification time, and improve the detection accuracy. And the clinical application is better realized.

The invention discloses a back-cleaning device and method for detecting and removing cotton foreign fibers, and relates to the field of textile equipment. The back-cleaning device comprises a machine body, a main cotton channel, a back-cleaning channel, a foreign fiber detection module, a spray valve module, a first impurity-containing cotton conveying module, an opening module, a second impurity-containing cotton conveying module, a waste cotton collecting module and a control module, a plurality of groups of foreign fiber detection modules are arranged, and the plurality of groups of foreign fiber detection modules are used for carrying out repeated and cyclic foreign fiber detection on the cotton flow in the main cotton channel and the cleaning channel. Single-face detection and double-face detection of the main cotton flow can be achieved, foreign fibers and impurity-containing cotton in the to-be-detected cotton flow can be effectively removed, the impurity-containing cotton is subjected to re-cleaning and re-inspection, multi-channel cyclic re-inspection and foreign fiber removal of the impurity-containing cotton and recycling of the impurity-containing cotton are achieved, waste of the cotton in the foreign fiber removal process is reduced, and the cotton quality is improved. And meanwhile, the foreign fiber detection and removal rate is improved, and the economic benefit of a cotton spinning mill is guaranteed.

The invention belongs to the technical field of contraband detection, and particularly relates to an intelligent contraband image detection method based on a double-projection radiation imaging system. According to the scheme, a double-projection image pair containing the contraband is generated through a projection simulation algorithm, then a training data set is constructed, training of a side view image data set and a look-up image data set is achieved through the algorithm, and a contraband detection model of the side view image and the look-up image is obtained; in the side view process, different weights are distributed to the detection results of the side view image and the upward view image according to the distribution characteristics of different types of contrabands in the double-projection image, and the three-dimensional space distribution and existence probability of the contrabands in the vehicle are estimated in combination with the position distribution and size information, so that the detection process is integrally optimized, and the detection efficiency is improved. The detection speed and precision are effectively improved, and the detection rate and the recognition rate are improved.

The invention discloses a black box Web vulnerability scanning entry collection method and device, and relates to the technical field of network security. The method comprises the following steps of: identifying all interactive elements in a Web page by traversing a DOM (Document Object Model) tree; acquiring an event bound with each interactive element; performing simulation operation on an event bound with each interactive element; judging whether a new Web page generated in response to the simulation operation reaches a stable state or not, if yes, traversing the updated DOM subtree, positioning newly added interactive elements in the current Web page, and performing weight reduction processing on the newly added interactive elements; and repeatedly executing the steps for the new Web page until the maximum recursion depth is reached or the Web page does not generate new interactive elements any more. According to the method, the authenticity and comprehensiveness of Web application request collection are improved, so that the detection rate of black box Web vulnerabilities is improved.

The invention relates to the field of pathogen detection, in particular to a porphyromonas gingivalis detection method based on a portable glucometer. The method provided by the invention comprises the following steps: S101, adding a sample into a first reaction system and incubating to obtain a first mixture; s102, a modified matrix electrode is in contact with the first mixture and then washed; s103, inserting the washed modified substrate electrode into a blood glucose meter; s104, obtaining a glucometer reading, and further obtaining the concentration of the porphyromonas gingivalis in the sample; wherein the modified matrix electrode is modified by an Au (at) V4C3MXene material and is combined with an antibody for specifically recognizing porphyromonas gingivalis and a sealing agent. The detection method provided by the invention is sensitive, rapid, stable, accurate and simple, and accurate detection of the single-digit porphyromonas gingivalis in an actual sample can be realized in a relatively short time (such as 20 minutes).

The invention discloses a change detection method and device for multi-dimensional information interaction and small target enhancement, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: selecting a change detection data set with front and back time phase images and label data from a remote sensing image data set, and carrying out the preprocessing of the front and back time phase images in the change detection data set; screening small targets of which the connected domain area is smaller than 32 * 32 pixels from the labels in the change detection data set, and calculating small target enhancement factors; extracting double-time-phase layering features from the preprocessed front and back time-phase images; performing time-space-channel three-dimensional information interaction on the extracted dual-time-phase hierarchical features, and outputting hierarchical change features fusing dual-time-phase information; fusing the layered change features to obtain fused change features; and performing network training on the fused change features by using the change detection loss of small target enhancement, and outputting a change detection result. According to the invention, the small target detection rate and the change detection accuracy are improved.

The invention relates to an enameled wire paint film on-line detection device and detection system, and relates to the field of paint film detection, and the device comprises a parameter receiving unit which is used for receiving paint film generation monitoring control parameters and paint film generation expected control parameters; the abnormity prediction unit is used for performing abnormity probability prediction to obtain a paint film abnormity probability; the abnormity identification unit is used for identifying suspected abnormal enameled wires; the contact detection unit is used for performing contact detection to obtain a first quality detection result; the normal identification unit is used for carrying out paint-film-free abnormal identification to obtain a second quality detection result; and the result feedback unit is used for feeding back the quality detection result to the client. The technical problems of low detection efficiency and low abnormal product detection rate in the existing enameled wire paint film quality detection are solved, the product with the suspected quality problem is screened out in advance through virtual detection, then the suspected abnormal product is subjected to contact detection, and the technical effect of improving the abnormal product detection rate and the detection efficiency is achieved.

The invention relates to the technical field of detection equipment, and discloses a medical vessel leakage detection device, which comprises a detection mechanism, the detection mechanism comprises a support frame, the inner wall of the support frame is fixedly connected with a detection frame, and the inner wall of the detection frame is fixedly connected with an optical detector. A fixing ring is fixedly connected to the outer wall of the end, close to the optical detector, of the supporting frame, a groove is formed in the outer surface of the top of the fixing ring, the adjusting mechanism comprises a sliding frame slidably connected with the fixing ring, and a first auxiliary motor is fixedly connected to the outer wall of the top of the sliding frame. The medical vessel is clamped and fixed by means of the adjusting mechanism, and can be rotated and adjusted at different angles, so that the optical detector can perform leakage detection on the medical vessel from multiple angles, the missing area during detection is effectively reduced, and the detection comprehensiveness and accuracy are improved.

The invention relates to the technical field of nondestructive testing of constructional engineering, and discloses a laser measuring instrument for nondestructive testing of concrete cavities, which comprises a laser interference measuring assembly, a paraxial sound wave excitation assembly, coaxially arranged anti-interference equipment and a cooperative control processing system. The sound wave excitation assembly performs wide-area excitation on the concrete surface, and the laser interference measurement assembly receives returned light through a clean light path constructed by the anti-interference equipment. The cooperative control processing system establishes acousto-optic time-frequency association by using an adaptive control module, generates a phase compensation angle according to ranging data to eliminate phase lock loss caused by sound wave propagation delay, and monitors a spectral gradient to automatically trigger fixed-frequency locking to capture resonance response. And the signal analysis module uses the phase compensation correction signal, and outputs cavity features through dual data cleaning and thermodynamic diagram analysis. According to the invention, the problems of time-space synchronization and weak signal extraction of long-distance non-contact detection in a complex environment are effectively solved.

The invention discloses a low-altitude unmanned aerial vehicle countering monitoring system based on state feature decoding, relates to the technical field of unmanned aerial vehicle prevention and control, and is used for solving the problem of poor low-altitude target detection and countering. According to the invention, through closed-loop management and control of heterogeneous radar networking detection, signal level fusion processing, threat assessment and resource scheduling and intelligent countering decision, accurate identification and directional countering of the low-altitude unmanned aerial vehicle are realized; according to the method, the detection precision and the micro-Doppler feature extraction capability of a low-speed small target are improved by combining a virtual aperture synthesis technology, then unbalanced resource allocation is implemented based on a threat level, the system efficiency is optimized, and finally seamless migration of an interfered node task is realized by constructing a signal interference situation map. And continuous tracking and effect evaluation on the target are maintained in the active radio frequency countering process, so that the overall efficiency and reliability of low-altitude security and protection in a complex environment are effectively improved.

The present application discloses a hole position detection method, device and storage medium for a connector. The hole position detection method for the connector includes: updating the hole position tracking result corresponding to the previous frame image based on the position change of the connector to obtain the hole position tracking result corresponding to the current frame image; and performing image recognition processing on the current frame image to obtain the hole position recognition result; calculating the connection hole position deviation between the hole position tracking result corresponding to the current frame image and the hole position recognition result, optimizing the hole position tracking result corresponding to the current frame image based on the connection hole position deviation; and obtaining the connection hole position corresponding to the connector based on the optimized hole position tracking result corresponding to the current frame image. The hole position tracking result can be optimized according to the hole position recognition result to improve the accuracy of the hole position tracking result, and the optimized hole position tracking result can avoid omissions in hole position detection and obtain a hole position detection result with high detection rate and high accuracy.

The invention relates to the field of electrochemical sensors, in particular to an electrode modification material and an electrochemical sensor based on the electrode modification material. The electrode modification material provided by the invention is an MXene material with gold nanoparticles deposited in situ, and the preparation method of the electrode modification material comprises the following steps: S101, dispersing V4C3MXene powder in an organic alkaline compound, stirring and centrifuging to obtain a first precipitate; s102, adding a first solvent into the first precipitate, and centrifuging to obtain a single-layer V4C3MXene suspension; s103, a KAuCl4 solution and the single-layer V4C3MXene suspension are mixed and stirred until the solution is red, a reducing agent is added, and a second precipitate, namely the electrode modification material, is obtained; wherein the concentration ratio of the KAuCl4 solution to the single-layer V4C3MXene suspension liquid is (0.30 to 0.45): 1. The electrode modification material provided by the invention can improve the electron transfer efficiency and the active specific surface area of an electrode, and shows a very strong advantage in the aspect of improving the sensitivity of an electrochemical sensor.

The invention provides a combined immunochromatography detection test strip for multiple respiratory tract pathogenic microorganisms, and belongs to the technical field of in-vitro diagnostic reagents. The detection test strip provided by the invention has an H-shaped structure and comprises a left test strip, a right test strip and a horizontal sample adding belt, a sample adding area is arranged in the middle of each of the test strip 1 and the test strip 2, and a binding area, a reaction area and a water absorption area are sequentially overlapped in an up-and-down symmetry manner by taking the sample adding areas as centers; one end of the sample adding belt is lapped on the sample adding area of the test strip 1, and the other end of the sample adding belt is lapped on the sample adding area of the test strip 2. The detection test strip has the characteristics of high detection rate, avoidance of cross reaction and simplicity and convenience in operation, and has obvious advantages compared with a traditional detection test strip.

The invention discloses a pavement landslide collapse detection alarm device based on laser signals, which comprises a laser signal transmitting unit, a shading screen array and a laser signal receiving and alarm unit, and is characterized in that the shading screen array comprises a plurality of shading screens of which the centers are provided with light through holes; the plurality of shading screens are sequentially arranged on a monitored road surface, and the light through holes are oppositely aligned; when landslide collapse does not occur on a monitored road surface, a laser signal emitted by the laser signal emission unit sequentially passes through the light through holes of the shading screens and then is received by the laser signal receiving and alarming unit. When landslide collapse occurs on a monitored road surface, a laser signal transmitted by the laser signal transmitting unit is blocked by the shading screen so as to trigger the laser signal receiving and alarming unit to send out an alarming signal. The invention aims to provide a pavement landslide collapse detection technology which is simple and convenient in construction and installation, low in cost, high in detection rate and strong in anti-interference capability, and realizes real-time monitoring and real-time alarming.

The utility model relates to a PCB feeding detection device, which comprises a feeding conveying belt, a feeding transfer unit, a conveying part, an upper lighting unit, a lower lighting unit, an upper detection camera and a lower detection camera, and is characterized in that the feeding transfer unit is used for transferring PCBs on the feeding conveying belt to the conveying part; the upper detection camera is arranged above the conveying part, and the lower detection camera is arranged below the conveying part; the upper lighting unit comprises an upper light supplementing light source, and the upper light supplementing light source is used for lighting the upper detection camera; the lower lighting unit comprises a driving assembly and a lower light supplementing light source, and the driving assembly is connected with the lower light supplementing light source and used for driving the lower light supplementing light source to move in the vertical direction. According to the device provided by the utility model, the driving assembly is arranged, so that the lower light supplementing light source can be adjusted according to the thicknesses of different PCBs, the clearness of images acquired by the camera is ensured, and the detection rate of defects of the PCBs is increased; and the feeding conveying belt, the feeding transfer unit and the conveying component are arranged, so that the feeding efficiency is improved.

The invention provides a medium-frequency high-precision detection method, which comprises the following steps: integrally evaluating a workpiece to be detected, and determining a high-risk part and an easily-corroded position of the workpiece to be detected; performing pulsed eddy current large-area rapid scanning on the high-risk part and the easily corroded position to obtain a primary scanning map; analyzing the primary scanning map, and determining a corrosion area and a suspected small defect area; replacing a high-precision probe to carry out pulsed eddy current high-precision scanning detection on the suspected small defect area to obtain a high-precision scanning atlas, analyzing the initial scanning atlas and the high-precision scanning atlas, determining the position of the small defect, and marking the position; ultrasonic thickness measurement is carried out on the marked position, and wall thickness value reinspection is carried out; according to the invention, a plurality of composite detection modes are adopted, and a plurality of corrosion defects of the detected pipe fitting are comprehensively detected; according to the detection method, the detection rate and the detection efficiency of small defects are greatly improved, and the omission ratio is reduced to the greatest extent.

The application discloses a vehicle reverse driving detection method for an expressway, and comprises the following steps: reading single-frame initial data of an expressway video stream, inputting the single-frame initial data into a lane line detection model to obtain lane lines and generate initial lane information; adaptively generating a first region and a second region; merging the two target detection regions, inputting the two target detection regions into a target detection model to obtain vehicle detection results, fusing the vehicle detection results, inputting the fused results into a multi-target tracking algorithm to obtain vehicle trajectory information; judging the driving direction of a lane; and identifying whether reverse driving occurs based on the trajectory information of a current vehicle and the driving direction of the lane where the current vehicle is located. The application can solve the problem of large calculation overhead caused by frame-by-frame analysis of a lane region in a traditional vehicle reverse driving detection method for an expressway by analyzing the relative position relationship between the vehicle motion trajectory and the identified lane in real time.