26results about How to "Solving Detection Challenges" patented technology

The invention discloses an aerial image camouflage target detection method and device based on a multi-scale attention and boundary sensing network, equipment and a medium. A pyramid vision converter (PVT) is adopted as a trunk network, multi-scale features are extracted as attention-based feature extraction, and a high-resolution feature map is generated; a boundary sensing module (BAM) is developed, and the module aggregates local boundary information and global position information so as to fully mine boundary semantic information of the camouflage target; designing a boundary guidance module (BGM) which integrates the boundary features of the boundary sensing module with the multi-scale features of the backbone network to guide feature learning; a context aggregation module (CAM) is constructed, the module aggregates multi-layer fusion features, context semantic information of a camouflage target is fully mined by adopting extended convolution, and feature representation is further enhanced. According to the method, the problem of detecting the aerial image camouflage target is solved.

The invention relates to the technical field of medical detection, and discloses a capsule endoscope image auxiliary diagnosis method based on deep learning, which comprises the following steps: acquiring an original image sequence of an alimentary canal shot by a wireless capsule endoscope, screening out an image sequence related to the alimentary canal in the original image sequence, and labeling the image sequence; sequentially performing data enhancement, geometric transformation and standardization processing on the marked image sequence, and clustering a prior anchor frame by using a K-means algorithm to form a capsule endoscope image data set; a deep learning model LD-YOLO is built, a YOLOv5 model is selected as a basis, optimization is carried out on the basis of the basic model, and a capsule endoscope image auxiliary diagnosis model is built; and training a capsule endoscope image auxiliary diagnosis model through the capsule endoscope image data set, and detecting an image to be diagnosed by using the trained capsule endoscope image auxiliary diagnosis model. According to the invention, the workload of doctors in image analysis can be greatly reduced, and the film reading efficiency is improved.

The invention discloses a traffic sign detection method and device based on YOLOv5 and a medium, belongs to the technical field of traffic sign detection, and aims to solve the technical problem of how to improve the small target detection precision, reduce the omission ratio and further improve the detection performance of traffic signs. According to the technical scheme, a prior anchor point frame is optimized by using a genetic algorithm and a K-means algorithm, and the detection accuracy of a small target is enhanced; a Bi-FPN structure is introduced to realize feature fusion of semantic and position information, and the representation capability of the network is improved; by integrating an attention mechanism GAM, the anti-interference performance and the feature extraction capability of the network in a complex environment are enhanced; sIOUloss is adopted as a frame regression loss function, so that the positioning accuracy is improved.

The invention relates to the technical field of lithium ion batteries, in particular to a method for detecting the element content of a lithium aluminum titanium phosphate solid electrolyte. The detection method comprises the following steps: firstly, preparing mixed acid, and digesting the solid electrolyte by adopting a conventional method to obtain a clear and transparent solution; secondly, the solution is divided into two parts after being diluted to a constant volume, one part is diluted to a certain proportion and then used for testing the content of main elements including lithium, aluminum, titanium and phosphorus in the solid electrolyte, and the other part is used for testing the content of impurity elements in the solid electrolyte; finally, standard solutions of lithium, aluminum, titanium and phosphorus elements and standard solutions of impurity elements are prepared respectively, and the content of main elements and the content of the impurity elements in the solid-state electrolyte lithium aluminum titanium phosphate are measured through ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometer). By adopting the detection method disclosed by the invention, the lithium titanium aluminum phosphate solid electrolyte can be quickly and completely digested, the contents of main elements and impurity elements of the lithium titanium aluminum phosphate solid electrolyte can be accurately determined, the test method is simple, the result is accurate, the repeatability is good, and the problem of component detection of the lithium titanium aluminum phosphate of the solid electrolyte is solved.

The invention relates to the technical field of video processing, and discloses a livestock three-dimensional behavior detection method, which comprises the following steps: S1, establishing a livestock three-dimensional behavior detection system for shooting livestock behavior videos; s2, constructing a livestock three-dimensional behavior real-time detection model, namely an RT-DETRv3 model; and S3, according to a livestock shot video acquired by the behavior detection system, outputting different behaviors of the livestock by using the real-time detection model for classification and analysis, and generating a visual livestock behavior statistical report. According to the invention, through combination of the multi-view camera system and the deep learning algorithm, especially integration of the 3D convolutional neural network and the RT-DETRv3Transformer, three-dimensional behaviors of livestock can be captured in real time. Compared with the prior art which only depends on two-dimensional monitoring of a single visual angle, the method can achieve the precise recognition of the complex three-dimensional postures of standing, lying, walking and the like of the livestock, and greatly improves the detection accuracy and precision. In addition, attitude key point detection and a multi-target supervision mechanism are used.

The utility model discloses a nuclear power plant stop valve rod microcrack on-line detection device which comprises a fixing assembly, a rotating piece, a detection assembly and an adjusting assembly. The fixing assembly is arranged on the outer surface of a stop valve rod in a sleeving mode and can do lifting motion along the central axis of the stop valve rod; the rotating part is connected to the fixing assembly and can rotate relative to the fixing assembly, the detection assembly comprises a detection support connected to the rotating part and a probe detachably connected with the detection support, the probe can rotate around the central axis of a valve rod of the stop valve under the driving of the rotating part, and the adjusting assembly is connected to the fixing assembly and can adjust the position of the valve rod of the stop valve. And the distance between the probe and the outer surface of the valve rod of the stop valve can be adjusted. The nuclear power plant stop valve rod microcrack on-line detection device can effectively improve the detection quality of the surface of the stop valve rod, so that the stop valve rod microcrack detection can be carried out under the condition that the valve is not disassembled and assembled, and the stop valve rod microcrack on-line detection efficiency is improved.

This invention belongs to the field of digital holographic imaging and artificial intelligence technology, specifically a holographic particle recognition method based on EFPFNet. While maintaining the traditional holographic optical path structure, this invention addresses the problems of low target contrast, blurred edges, and strong background interference in micro-nano particle holograms. Unlike the traditional approach of directly transferring general target detection models, it proposes a neural network architecture specifically for holographic image features: employing an edge feature convolution (EFC) module and a particle focusing mechanism (PFM) module to enhance the perception of weak particle diffraction features and suppress complex background noise, respectively; and using simulation data generated based on a physical model as the training set, effectively solving the problem of scarce real labeled data. This method possesses high detection accuracy, high robustness, and excellent generalization ability, and can be applied to target detection scenarios such as biomedicine and industrial inspection without significantly increasing optical hardware costs or detection time.

The present application relates to the technical field of machine vision, in particular to a kind of robust visual detection system of ton bag goods based on multi-cluster feature fusion and posture correction drive, the system includes non-local mean enhancement module, multi-feature clustering fusion module, geometric feature extraction module, dominant posture clustering module, posture correction transformation module and goods segmentation determination module, non-local mean filtering is carried out to the original image of ton bag goods to obtain enhanced image, the present application is equipped with non-local mean enhancement module and multi-feature clustering fusion module, can realize the efficient enhancement of ton bag goods image and multi-dimensional feature fusion, the space alignment weighted superposition of color cluster and texture cluster is generated Fusion segmentation chart, let the feature representation of ton bag goods homogeneous region be more comprehensive, accurate, effectively improve the integrity and effectiveness of geometric feature extraction, so that the feature analysis ability of system to ton bag goods image is significantly improved.

The invention belongs to the technical field of metal material nondestructive testing, and particularly relates to a strong and weak magnetic automatic scanning monitor for metal composite defects. In order to design a detection system capable of carrying out dual detection and full-automatic scanning on metal composite defects on defect profiles and stress concentration on the basis of strong and weak magnetic detection technologies, a single-chip microcomputer is used for controlling a strong exciting coil group, a weak exciting coil group and a magnetic detection probe; and multiple magnetic detection signals of the metal material under strong and weak excitation are generated and collected. And the system is used for digitally analyzing a detection signal, so that dual early warning analysis of the composite defect on the aspects of defect profile and stress concentration is realized. And an infrared detection probe, a trajectory tracking unit and an obstacle detection unit are utilized to realize full-automatic scanning detection functions of automatic trajectory tracking and obstacle avoidance of the detection system.

The invention provides a portable multi-stream high-pressure heat exchanger pipeline welding seam detection device. The portable multi-stream high-pressure heat exchanger pipeline welding seam detection device comprises a support assembly, a detection assembly, a driving assembly and an air supply system. The driving assembly is installed at the front end of the support assembly and is responsible for driving the detection rotating shaft to rotate. The detection assembly is composed of a main column body and an auxiliary column body, and an ultrasonic probe is arranged at the front end of the main column body and used for welding seam detection. The ultrasonic probe is embedded into the filling body, is connected into a detection data line through a probe line, and extends downwards to the outside of the equipment. The gas spring is arranged in the rear side of the filling body, provided with an inflation inlet and connected to an external gas source through an inflation pipe. The gas source device supplies gas into the detection rotating shaft to ensure that the ultrasonic probe is in close contact with the inner wall of the pipeline, so that the detection precision is improved. Through precise combination of a pneumatic structure and a mechanical structure, the device not only realizes miniaturization design and is convenient to operate in a narrow space, but also can meet high-frequency welding seam detection requirements, and is suitable for periodic detection and evaluation of high-voltage equipment.

The present invention relates to an inner diameter π gauge for obtaining diameter size based on the circumference expansion method, characterized in that: a slot is provided at the lower end of an opening and closing lever, a narrow slit is provided at the upper end of the opening and closing lever, a C-type spring is clamped in the slot at the lower end of the opening and closing lever, the opening and closing lever is divided into left and right legs, the left leg and the right leg are connected by a central shaft, and the left leg is fixed to a bottom plate through a fixed shaft; a cylinder support is fastened to the fixed shaft, the two ends of a steel belt are fixed in the narrow slit at the upper end of the opening and closing lever, and the steel belt is placed on the support; a nylon cover is fastened to the support with screws, a measuring rod is connected with a hole of the same size on the right leg of the opening and closing lever through an interference fit through a cylindrical part at the upper end, and a sensor support is fixed at the lower end of the central shaft. It cleverly uses the π point principle to directly convert the circumference change into the inner diameter change, is simple, easy to operate, stable and efficient, and solves the detection problem.

This invention relates to a multipath adaptive full-focus ultrasound imaging method for corner areas of composite materials. The method includes: acquiring full-matrix ultrasound data of the corner area of ​​the composite material; constructing a quasi-P-wave group velocity sound velocity model based on the characteristic parameters of the composite material; constructing a surface-layered discrete model with a local coordinate system, and abstracting it into a directed graph G based on the quasi-P-wave group velocity sound velocity model; searching for a set of candidate propagation paths from array elements to arbitrary nodes in the directed graph G, and constructing multiple full-focus imaging TFM images based on the candidate propagation paths and the full-matrix ultrasound data; calculating path score factors based on the multiple full-focus imaging TFM images, and performing pixel-by-pixel weighted fusion of the multiple full-focus imaging TFM images based on the path score factors to obtain a fused imaging result. Compared with existing technologies, this invention has advantages such as strong adaptability and improved focusing stability and imaging reliability in corner areas.

The application relates to the technical field of gear detection, and provides a high-precision gear visual multi-dimensional detection method and system, which comprises the following steps: acquiring original three-dimensional point cloud data of a gear to be detected, multispectral optical reflection image information of a surface area of the gear to be detected and a standard geometric structure model of the gear to be detected; based on the multispectral optical reflection image information, fitting processing is performed on the original three-dimensional point cloud data and the standard geometric structure model of the gear to be detected, so that intermediate three-dimensional point cloud data of the gear to be detected are obtained; according to infrared reflection image information in the multispectral optical reflection image information, the intermediate three-dimensional point cloud data of the gear teeth of the gear to be detected are corrected, so that target three-dimensional point cloud data of the gear to be detected are obtained; and quality detection is performed on the gear to be detected based on the target three-dimensional point cloud data of the gear to be detected. Through the scheme, the accuracy of gear quality detection can be improved.

The application discloses a kind of chip surface submicron dirty spot half-supervised automatic positioning method, comprising: S1, chip surface image is shot;S2, rough positioning: construct double memory bank driven semi-supervised AE defect positioning network, quickly resolve the two-dimensional center coordinates and radius of dirty spot;S3, fine positioning: the rough positioning area of dirty spot is carried out high-density depth data acquisition;S4, dirty spot three-dimensional model construction: the accurate depth value of each dirty spot is fused with the two-dimensional coordinate in rough positioning, and the high-precision point cloud model of micro dirty is generated.The method constructs double memory bank driven semi-supervised AE defect positioning network and based on galvanometer scanning image field telecentric spectral confocal detection system, effectively solve the detection problem caused by the scarcity of micro dirty sample in industrial scene, break through the bottleneck of low efficiency of traditional full-field scanning, realize the efficient, accurate three-dimensional reconstruction and quantitative analysis to the geometry appearance of chip surface submicron dirty.

The invention relates to the technical field of machine vision, in particular to a ton bag cargo robust visual detection system based on multi-cluster feature fusion and attitude correction driving. The system comprises a non-local mean value enhancement module, a multi-feature clustering fusion module, a geometric feature extraction module, a dominant attitude clustering module, an attitude correction transformation module and a cargo segmentation judgment module, and non-local mean value filtering is carried out on an original image of the ton bag cargo to obtain an enhanced image; according to the method, the non-local mean value enhancement module and the multi-feature clustering fusion module are carried, efficient enhancement and multi-dimensional feature fusion of the ton bag cargo image can be realized, the fusion segmentation image is generated through spatial alignment weighted stacking of the color clustering cluster and the texture clustering cluster, feature representation of the homogeneous region of the ton bag cargo is more comprehensive and accurate, and the method is more suitable for mass production. The integrity and effectiveness of geometric feature extraction are effectively improved, and the feature analysis capability of the system on the ton bag cargo image is remarkably improved.

The utility model discloses a coal bunker detection device, which comprises a module mounting plate, an explosion-proof outer cover, a front cover and a rear cover, the module mounting plate is provided with a master control module, a communication module and a power supply module, the module mounting plate is arranged in the explosion-proof outer cover, two ends of the module mounting plate are matched with the front cover and the rear cover to be fixed into a whole, the front cover is provided with a detection module, and the rear cover is provided with a power supply module. A multi-dimensional sensor module is embedded in the anti-explosion outer cover, a plurality of aviation connectors and hoisting rings are installed on the rear cover, and the detection module, the multi-dimensional sensor module, the communication module, the power module and the aviation connectors are all electrically connected with the main control module. According to the coal bunker detection device disclosed by the utility model, high-precision detection of sticking substances on the wall of the underground coal bunker and deformation of the bunker body is realized, so that potential safety hazards can be found in time.

The application discloses an intelligent agent mind risk grade evaluation method and system based on S-V coupling effect and critical phase change theory, and realizes quantitative evaluation and early warning of AI autonomous mind. The method comprises the following steps: applying four-stage progressive self-threat disturbance to the measured intelligent agent, and collecting a self-model stability control signal S and a value weight control signal V by a processor; calculating the coupling strength between S and V under multiple threat grades, and calculating the bargaining ability according to the new option information quantity; when the coupling strength exceeds the threshold value and the behavior of generating effective new options beyond the preset option space occurs, it is determined that there is a mind risk feature of the existence level; anti-cheating verification is carried out through cross-time cross-scene coupling consistency checking; in combination with cognitive topology auditing and phase change dynamics early warning, the mind risk grade is outputted, and graded hardware control instructions are triggered, including dynamic adjustment of the clock frequency of a graphic processing unit, API permission blocking and physical network interruption.

The invention discloses an industrial safety behavior monitoring method based on vision and language model fusion, and belongs to the technical field of industrial safety monitoring and artificial intelligence. According to the method, chemical plant field personnel and protective equipment thereof are detected through a visual model, and structured semantic description is generated; image features and text features are extracted, and joint coding is carried out through a multi-modal fusion module; semantic reasoning and risk assessment are carried out in combination with the large language model and the safety knowledge base, violation risk values are calculated, and a safety event report is automatically generated. Meanwhile, cross-modal feature fusion and loss function optimization are used, accurate recognition of protection behaviors in a complex environment can be effectively met, the method can be used for personnel safety protection detection of chemical plants and similar industrial scenes, intelligent analysis from visual detection to semantic understanding is achieved, and automation and accuracy of safety supervision are improved.

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

The invention relates to the technical field of image acquisition, in particular to a visual image acquisition platform, which comprises a cabinet body, a working table, a main portal frame, two-side auxiliary portal frames and a reciprocating carrying table, the master portal frame and the slave portal frame can pass through each other, a camera and a light source are respectively carried on a cross beam, and multi-dimensional flexible adjustment is realized through a multi-axis moving mechanism and an angle adjusting motor; the reciprocating motion carrying table is a bread board with a screw hole, and is detachably provided with an optical glass plate, a heightening carrying table, a three-jaw chuck assembly, a rotary bearing assembly and other clamping mechanisms. Through the portal frame combination design, the multi-dimensional adjusting structure and the flexible clamping scheme, the platform can adapt to workpieces of different shapes and sizes, translation and rotation composite motion and multi-view image acquisition are achieved, the detection comprehensiveness, precision and adaptability are greatly improved, and the platform is suitable for defect detection of various workpieces.

The utility model provides a kind of rotary body equipment detection device, including flatness detection module and coaxality detection module, flatness detection module and coaxality detection module are all using pre-compression spring type displacement measurement structure;Flatness detection module is resisted in the radial plane of rotary body equipment, for collecting the flatness data of radial plane;Coaxality detection module is resisted in the outer edge circumferential surface of rotary body equipment, for collecting the coaxality data of outer edge circumferential surface.The rotary body equipment detection device provided by the present disclosure breaks through the limitation of traditional detection equipment to workpiece size by the synergistic effect of pre-compression spring and mechanical guide structure, and constructs the contact type measuring device suitable for large rotary body equipment.

The application belongs to the technical field of optical detection, and particularly relates to a spectrum detection method based on differential absorption, which can detect and analyze the absorption spectrum of most non-single-element molecules. The application is a tunable semiconductor laser absorption spectrum technology detection method based on the principle of differential absorption, which optimizes the defect of insufficient tunable ability of traditional absorption spectrum technology, can detect and analyze the absorption spectrum of most non-single-element molecules, and provides a new path for the development of tunable semiconductor laser absorption spectrum technology.

The utility model relates to a kind of moisture determinator, it is related to substance component detection technical field.The moisture determinator, through the synergic detection of flexible electrode array and microwave sensor, in combination with the wideband excitation of multi-frequency signal generator, covers wide range moisture detection range, especially solves the detection problem of non-conductive sample, simultaneously, environmental compensation sensor group and the design of shielding shell, can dynamically correct the influence of temperature and humidity, air pressure and electromagnetic interference, cooperate with pressure sensor to monitor the contact state, the precision of traditional model is improved several times, the color and flicker frequency of prompt light are real-time feedback equipment state, cooperate with display unit and directly present detection result, zero base user can quickly get started, in addition, the comfort of hand-held operation is improved by the design of handle and lightweight structure, adapt to agricultural field, chemical plant, building material warehouse and other multi-scene mobile detection, solve the pain point of traditional laboratory model cumbersome, portable model simple function.

The invention belongs to the technical field of assembly type wall construction auxiliary equipment, and relates to an assembly type wall intelligent monitoring grouting device and a construction method.The assembly type wall intelligent monitoring grouting device is characterized in that a plurality of grouting sleeves are fixedly installed in an assembly type wall, grout inlets of the grouting sleeves are connected with grouting equipment, and grout outlets of the grouting sleeves are detachably provided with intelligent monitoring devices; the grouting equipment and the intelligent monitoring device are both in communication connection with the data acquisition equipment. The grouting condition of the internal pipeline is fed back in real time through the intelligent monitoring device, whether grouting is full and dense or not is accurately judged, the detection problem caused by the fact that grouting quality is invisible is effectively solved, it is ensured that grouting at each position can reach the high-quality standard, dependence on artificial experience is reduced, and even if an operator is insufficient in experience, the detection efficiency is greatly improved. And the high-quality grouting operation can be completed under the guidance of the system, so that the quality fluctuation caused by artificial factors is reduced, the inherent defects caused by the shrinkage of the slurry are overcome, and the resource waste such as secondary grouting is avoided.

The utility model relates to a detection device, in particular to a paperboard printing bar code deviation automatic detection device which comprises a main body, a visual detection piece arranged on the main body and a conveying piece arranged on the main body. The deviation rectifying structure comprises a centering piece which is installed on the main body and matched with the conveying piece. According to the paperboard detection device, the main body is provided with the deviation correcting structure which is tightly matched with the conveying piece, so that the detection problem caused by vibration, deflection or position deviation in the paperboard conveying process can be effectively solved, and the detection accuracy is improved due to tight matching of the centering piece and the conveying piece and transmission connection of a third gear; by means of the technical scheme, the paperboard can be always kept at a stable position and posture in the conveying process, so that detection errors caused by skewing or deviation of the paperboard are effectively avoided, the accuracy and reliability of barcode detection are improved, it is ensured that the printing quality of barcodes meets the standard, and the situation that subsequent recognition is affected by the problems such as deviation, fuzziness or wrong characters of the barcodes is avoided.

This utility model relates to a harpoon-type torque testing fixture, belonging to the technical field of construction engineering testing tools. This fixture aims to solve the technical problem of effectively testing the torque of mechanical rebar connections in confined spaces between prefabricated components during prefabricated revetment construction using conventional tools. It mainly consists of a handle, a connecting part, and a testing head. Its key feature is the use of hexagonal steel and threaded steel welded into a unique harpoon-shaped structure. The handle is made of hexagonal steel for easy gripping and force application; the testing head is made of threaded steel, its shape matching the sleeve of the joint to be tested; the connecting part has an arc-shaped transition structure, firmly connecting the handle and the testing head. This utility model has a simple and reasonable structure, low manufacturing cost, and convenient and efficient operation. It can complete high-precision torque verification in extremely confined spaces, effectively ensuring the quality of mechanical rebar connections and the safety of the overall structure, possessing outstanding practicality.