924 results about "Foreign object detection" patented technology

The present invention relates to the technical field of railway inspection, and relates in particular to a railway foreign-object intrusion detection method and system based on deep learning. The method comprises: S10, acquiring image data to undergo detection; and S20, inputting the image data into a trained attention semantic segmentation network to obtain a foreign-object detection result. The attention semantic segmentation network is obtained by first training a preset attention semantic segmentation network architecture using a preconfigured data set, and then re-training the attention semantic segmentation network on specified image data using an adaptive correction algorithm. A backbone network architecture is obtained by inserting a specified attention mechanism at a specified position within a pre-selected residual neural network, and using three parallel dilated convolutions as an initial convolutional layer in the residual neural network. A dual-branch decoder combines an edge recognition branch and a semantic segmentation branch. The method is applicable to various scenarios, achieves improved detection accuracy and maintains a lightweight design.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

The invention discloses a power transmission line foreign matter detection method and system based on multi-modal image fusion, and relates to the technical field of intelligent operation and maintenance and state monitoring of a power system, a lightweight Ev-Mama architecture is introduced into a backbone network part of YOLOv13, the model keeps relatively low calculation complexity, and meanwhile, the power transmission line foreign matter detection efficiency is improved. And the modeling capability of the method on the long-range dependency relationship and the global semantic information is obviously enhanced. Besides, by using the CDIDF module, the EVCS module and the MHSAA module, on the basis of increasing a small amount of calculation, the scale sensing ability, the space structure modeling ability and the context understanding ability of the model are effectively improved, and the performance bottleneck of a traditional YOLO series network in the aspects of processing small targets, shielding targets and cross-scale information fusion is effectively relieved.

The invention provides a rail turnout foreign matter detection and type analysis method and system, and the method comprises the steps: arranging a first camera group at the front end of a train, recognizing the position of a turnout through a preset target detection model, and calibrating foreign matters in the turnout through a preset foreign matter detection model; shooting an image calibrated to the foreign matter in the turnout through a second camera group as a to-be-analyzed image; extracting a second boundary region rolled by a wheel on the foreign matter from the to-be-analyzed image through a preset wheel rolling track detection model, performing coarse classification on the foreign matter in the to-be-analyzed image to obtain a coarse classification result, and selecting a boundary detection strategy to identify the first boundary region according to the coarse classification result in the foreign matter image; and respectively extracting corresponding texture features in the first boundary region and the second boundary region through a deep learning network, performing one-to-one mapping on the extracted texture features to obtain a mapping relation group, performing analysis through the mapping relation group to obtain a feature change rate, and performing analysis through a multi-feature analysis strategy to obtain a specific foreign matter type.

The invention discloses a liquid medicine foreign matter detection method and system based on machine vision, and the method comprises the steps: obtaining multiband spectral data of a liquid medicine sample, and carrying out the normalization processing to generate a liquid medicine background standard spectral feature template; comparing the refractive index spectral characteristics of the template and the foreign matter-containing liquid medicine, and recording the surface roughness scattering mode and polarized light reflection angle change data of a foreign matter area; calculating a spectrum peak displacement quantized value according to the polarized light data, and extracting foreign matter edge spectrum gradient information in combination with a threshold value; calculating a transparency spectral attenuation coefficient based on the edge gradient information, and obtaining spectral contrast enhancement factors of different foreign matters in combination with the spectral response characteristic data; and if the enhancement factor does not reach the standard, adjusting the spectrum correction parameter and recalculating, fusing the scattering mode and the multi-angle incident spectrum response result to carry out consistency verification, and obtaining a final identification result containing the foreign matter material type and the danger level. The method can improve the drug detection accuracy and guarantee the drug quality safety.

The invention relates to the technical field of wireless charging, and provides a foreign matter detection method and system for a wireless charging system and a storage medium. The method comprises the following steps: firstly, calibrating at multiple temperature points and establishing a temperature-parameter comparison table under the condition of no foreign matters; in a no-load standby state, the inversion module sends out detection drive and collects voltage, current and temperature; calculating a real-time quality factor and resonant frequency according to sampling, obtaining a compensation reference according to a temperature look-up table, and solving the variation of the quality factor and the resonant frequency; and performing fusion judgment by adopting dual-condition locking or weighted scoring, and triggering fault protection when a foreign matter is confirmed. Due to the fact that equivalent inductance and resistance changes are caused by metal foreign matters, quality factors are remarkably reduced along with resonant frequency deviation, temperature excursion and foreign matter effects are distinguished through temperature compensation, and dual-condition locking or weighted scoring fusion judgment is combined, the interference immunity and accuracy of detection are improved, false alarms and missing alarms are remarkably reduced, and the detection accuracy is improved. The method is suitable for high-power wireless charging scenes.

The invention belongs to the technical field of track detection, and more specifically relates to a track scene thunder-vision fusion anti-intrusion monitoring method and system based on sparse feature fusion. The method comprises the steps that a laser radar obtains three-dimensional point cloud data, a high-definition camera obtains visible light image data, and data preprocessing is carried out; performing external parameter automatic calibration optimization and time alignment compensation on the laser radar and the high-definition camera to obtain time-space aligned multi-modal data; fusing the camera semantic features and the laser radar geometric features based on multi-modal data of space-time alignment to obtain fused features; and after multi-modal feature fusion is completed, a target heat map is generated through a sparse detection head, and graded early warning is realized in combination with a dynamic safety distance model. The method solves the problems that an existing recognition algorithm is high in false alarm rate, and the false alarm rate is higher under low-light conditions such as rainy days or dusk; the data dependence is strong, and the ability to detect unlabeled novel foreign matters is almost zero.

The invention discloses an integrated airport foreign object detection system and method, and the system comprises a multi-source data collection module which is combined with a self-adaptive triggering mechanism, and dynamically adjusts the working mode of a sensor according to a flight timetable and meteorological data; the edge calculation processing module is used for extracting a foreign object candidate region by adopting a multi-modal data fusion technology, carrying out confidence coefficient weighted decision based on a D-S evidence theory and outputting accurate position and size information of a foreign object; the automatic submission module is used for establishing a grading alarm mechanism, pushing high-risk foreign objects to a control tower terminal in real time through a 5G / wireless private network, and synchronously generating an encrypted standardized report; and the intelligent analysis platform predicts a foreign object high-incidence area by using an LSTM time sequence model, traces foreign object causes in combination with a knowledge graph technology, and provides management decision support through a visual interface. According to the invention, full-process optimization is realized through a closed-loop architecture of multi-source data acquisition, edge calculation processing, automatic submission and intelligent analysis.

The invention relates to the technical field of material detection, and discloses a material multi-modal X-ray foreign matter detection method, which comprises the following steps of: acquiring a transmission gray-scale time sequence array and a scattered photon energy spectrum distribution matrix output by a multi-modal X-ray acquisition system, generating a composite characteristic body through cross-modal characteristic interactive coding, constructing a material-contour associated nodal topology network, and obtaining a material-contour associated nodal topology network; simulating a foreign matter feature conduction path and outputting a correlation confidence value set, performing space-time dimension mapping transformation to generate a foreign matter-detection coupling prediction curved surface, calculating a sensitivity adjustment factor and generating a standardized coupling coefficient thermodynamic cloud chart, extracting a detection quantitative index set and dynamically matching the detection quantitative index set with a preset reference range; and when the indexes exceed the boundaries, generating a regulation and control scheme. According to the method, the accuracy, reliability and real-time performance of foreign matter detection are improved, and the detection requirements of complex scenes can be met.

Disclosed are a device and method for detecting and identifying shallow-stratum foreign objects based on distributed acoustic sensing. The device includes a vibrating sensitivity-enhanced fiber optic cable, a distributed acoustic sensing demodulator, a vibrator system, a vibration data processing unit, a velocity structure inversion unit and an AI-aided locating and identification unit. The distributed acoustic sensing demodulator transmits vibration signals to the vibration data processing unit for preprocessing to obtain waveform signals; the velocity structure inversion unit performs inversion to obtain the shallow-stratum underground velocity structure; positions of shallow-stratum foreign objects are identified through the underground velocity structure and abnormal waveforms; and the types of shallow-stratum foreign objects are identified. A distributed acoustic sensing method for fiber optic cables is used to obtain vibration signals, velocity imaging is performed on a shallow-stratum velocity structure, and AI is utilized to achieve locating and type identification of shallow-stratum underground foreign objects.

The invention discloses a deep learning-based coal mine foreign matter visual detection system, which is characterized in that a coal mine image is acquired through a multispectral imaging technology, a static compensation image is obtained through registration, enhancement and motion compensation processing, multidimensional features are extracted, dimensionality reduction is carried out by utilizing principal component analysis to generate an optimized feature set, an improved YOLOv12 model is trained, and the visual detection of foreign matters in a coal mine is realized. A lightweight foreign matter detection model is obtained through parameter simplification and network optimization, and foreign matter real-time recognition and detection report generation are achieved. A time sequence analysis model is constructed by associating historical detection data, and a detection model is iteratively updated in combination with an incremental learning mechanism. Multispectral imaging and deep learning are combined, the foreign matter detection precision of the complex underground environment is improved, industrial site deployment is adapted through model lightweight design, the learning ability of the system for the foreign matter distribution rule is enhanced through a time sequence analysis mechanism, and efficient detection and early warning of the coal mine foreign matter are achieved.

The present invention discloses a method and system for detecting foreign objects in food based on deep learning, including an image acquisition module and a foreign object analysis module connected thereto; the image acquisition module: for acquiring RGB images and penetration images of food; the foreign object analysis module: inputting the food images corresponding to the RGB images, the food images corresponding to the penetration images, color features and shape features into a foreign object detection model, relating to the technical field of food detection, and solving the technical problem that when using X-ray images of food and analyzing the foreign objects therein, the sensitivity for detecting low-density foreign objects in food X-ray images is too low, resulting in difficulty in meeting the current high-quality requirements for food; taking the color situation in food as one of the judgment bases and taking the shape features of food as one of the judgment bases, and judging through the trained foreign object detection model can avoid a large missed detection rate when detecting foreign objects in food by a single image.

The invention discloses a foreign matter detection method and system based on millimeter wave radar three-dimensional point cloud imaging, and the method comprises the steps: carrying out the Fourier transform of a distance dimension and a speed dimension, achieving the coherent accumulation, and effectively improving the signal energy; incoherent accumulation is achieved by means of a multi-channel technology, target information is extracted by applying a two-dimensional CFAR algorithm, so that the anti-interference capability in the recognition process is enhanced, the problem that clutter and noise interference in the environment is extremely serious is effectively solved, the detection precision is improved, meanwhile, a clustering algorithm and multi-frame point cloud data fusion are combined to solve the point cloud data sparsification problem, and the detection accuracy is improved. An overfitting phenomenon occurring when a classification algorithm model is directly used is prevented, the efficiency of target feature extraction is improved, and remote detection is broken through.

The invention provides a road foreign matter detection method based on tunnel inspection robot vision and related equipment, and relates to the technical field of intelligent traffic tunnel inspection robots. The method comprises the following steps: training by using an existing tunnel robot video data set to obtain an ROI region and a road object instance segmentation model; acquiring a video stream data frame of the inspection robot, generating an edge binary image through edge detection, obtaining related mask data through model reasoning, and recording classified foreign matter surrounding frame data; processing and combining various data, judging residual objects through morphological operation and contour detection, and determining unclassified foreign matter bounding box data; then reporting two types of foreign matter bounding box data according to rules; and finally, generating an annotation file according to the unclassified foreign matters, and updating the road object instance segmentation model. By implementing the method, tunnel road foreign matters can be timely and accurately detected, potential safety hazards are reduced, smooth tunnel traffic is guaranteed, and the intelligent and automatic level of tunnel inspection is improved.

A wireless power transmitter that transmits power to a wireless power receiver, the wireless power transmitter including a communication unit configured to communicate with the wireless power receiver; and a controller, wherein the communication unit receives a plurality of packets including a foreign object detection (FOD) status packet from the wireless power receiver, wherein the controller determines whether a foreign object is present in a charging area of the wireless power transmitter on the basis of the FOD status packet, wherein the communication unit transmits, to the wireless power receiver, a response signal indicating whether the foreign object is present in the charging area of the wireless power transmitter on the basis of a result of the determination, wherein the response signal is determined using a measured peak frequency of a power signal transmitted by the wireless power transmitter and a reference peak frequency included in the FOD status packet received from the wireless power receiver, and wherein the plurality of packets further include at least one of a signal strength packet, an end power transfer packet, a power control hold-off packet, a configuration packet, an identification packet for transmitting receiver identification information, an extended identification packet, a general request packet, a special request packet, a control error packet, a renegotiation packet, a 24-bit received power packet, an eight-bit received power packet, and a charging status packet.

The present invention relates to the field of railway foreign object detection. Disclosed are a railway foreign object intrusion detection method and system, a device and a medium. The method comprises: acquiring a first key railway image frame of the current scene as a template image, and an N-th railway image frame thereof as an image under test; inputting the template image and image under test of the current scene into a foreign object intrusion prediction model to obtain a foreign object feature map of the current scene, so as to determine whether there is a different object between the image under test and template image of the current scene; and, if there is a different object, using a moving object filtering model to filter out a moving object from the different object in the image under test of the current scene, so as to obtain a real foreign object in the image under test of the current scene. The present invention can improve the reliability of railway foreign object intrusion detection.

The invention belongs to the technical field of foreign matter detection and alarm, and discloses a vehicle-mounted multi-sensor fusion runway FOD detection and alarm device. Comprising a data acquisition module for acquiring radar data, visual data and infrared data, and performing space-time synchronization and coordinate calibration to obtain synchronous data; the feature analysis module is used for analyzing the synchronous data to obtain radar features, visual features and infrared features; the analysis and complementation module is used for detecting the radar features, the visual features and the infrared features to obtain combined features, and performing shielding detection and feature complementation on the combined features to obtain complemented features; constructing an FOD dynamic feature tree; the positioning alarm module is used for analyzing the FOD dynamic feature tree and making an FOD alarm decision according to an alarm triggering condition; according to the method, accurate detection and reliable alarm of the runway FOD are realized, the accuracy, robustness and practicability of FOD detection in a complex scene are remarkably improved, and the runway FOD safety management and control requirements in a vehicle-mounted scene are effectively met.

The invention provides a screen foreign matter detection device and method, and belongs to the technical field of optical detection, and the screen foreign matter detection device comprises an SPAD SoC chip, a VCSEL chip, a packaging substrate, a packaging cover plate, a first optical filter, a second optical filter, a transmitting lens, a receiving lens and a lens base. The mounting position of the foreign matter detection device is selected; selecting opposite sides; recording a fixed distance during initial installation; according to the method and the device, whether foreign matters exist on the surface of the screen or not is detected by adopting linear light spots according to the setting of a foreign matter detection judgment basis, and the method and the device have the advantages of small size, low cost and high precision, can be applied to foreign matter detection of an automobile HUD and other related fields with certain requirements on projection safety, enable the screen to be more intelligent and improve the overall safety.

The invention provides an automatic feeding control method and device for mine rough crushing, electronic equipment and a storage medium, and the automatic feeding control method for mine rough crushing comprises the steps that operation data and machine vision recognition data of a rough crushing system are obtained, the machine vision identification data comprises material form detection data, crushing hopper material quantity detection data, signal bin material detection data and foreign matter detection data; and based on the operation data, the material form detection data, the crushing hopper material quantity detection data, the signal bin material detection data and the foreign matter detection data, feeding control data are determined, and the feeding control data comprise the feeding speed. According to the method, automatic feeding of mine rough crushing can be achieved in combination with the operation data and the machine vision recognition data, manual operation is not needed, and therefore the feeding speed control precision is improved, and manual dependence is reduced.

The invention relates to the field of intelligent monitoring of electric power facilities, in particular to a power transmission line foreign matter detection method based on multi-component semantic segmentation and hybrid Transform-CNN (Convolutional Neural Network). According to the method, the Transform module is combined with the CNN model, multi-component semantic segmentation is performed on the to-be-analyzed image through the decoder based on the UNet network, and the method can be applied to rapid detection under complex conditions and numerous interference environments. Therefore, the foreign matter detection of the power transmission line for performing high-precision segmentation and real-time detection on the attached foreign matters on multiple parts such as a lead, an insulator and a fitting of the power transmission line in a complex scene is realized, the safety level alarm is performed when an abnormal trigger mechanism is reached, and the test shows that the IoU is improved by 15% and the false detection rate is reduced by 40% in rainy and foggy weather, so that the safety of the power transmission line is improved. The FPS reaches 32 frames per second (real-time edge calculation is met), the accuracy and robustness of foreign matter detection of the power transmission line are greatly improved, and the timeliness of safety early warning of the power transmission line is also greatly improved.

The application provides a foreign matter intrusion detection method and system for railway perimeter protection, relates to the technical field of railway perimeter protection, and first unifies the space-time reference of a millimeter wave radar, a visible light video and a fiber vibration signal based on a space-time calibration parameter, eliminates the physical space deviation between heterogeneous sensor data, and ensures the accurate alignment of multi-modal features under complex weather and terrain conditions;Secondly, the real-time reliability of each mode is dynamically evaluated through a data quality index, a deformable convolution offset field is generated combined with a cross-modal semantic correlation matrix, the feature space mapping relationship is adaptively adjusted, and the interference of video blur, radar noise and vibration false alarm is effectively suppressed;Finally, based on a lightweight model, the dynamically fused joint features are efficiently inferred, sub-meter level positioning accuracy of foreign matter detection is realized on an edge computing device, existing railway monitoring facilities are upgraded, maintenance cost is reduced, and an intelligent solution is provided for railway perimeter safety protection.

A computer-implemented system for foreign object detection in a scene. The system includes a memory-suppress diffusion network module adapted to reconstruct a reconstructed image from an encoded image, and a contrastive dissimilarity network adapted to combine the input image and the reconstructed image to predict an anomaly map for the input image. The encoded image is based on an input image, and the memory-suppress diffusion network module and the contrastive dissimilarity network are trained using only normal, real images. The system leverages only normal images in training and does not compromise the detection performance at the inference stage.

In order to solve the technical problems that an existing wireless communication system based on magnetic coupling needs to be additionally provided with a foreign matter detection unit, so that the system is complex, the cost is high, the dynamic adjustment capability is lacked and the anti-interference performance is poor, the invention provides a wireless communication system and method with foreign matter detection and anti-interference functions. Impedance data of the transmitting coil module are used for foreign matter detection, detected foreign matter distribution information is coded into binary codes to be embedded into communication frames to be transmitted together with the communication frames, and data interaction delay among functional units is eliminated; the working mode switching module predicts the working mode of the wireless communication system at the next moment in real time based on the detected foreign matter distribution data, the actually measured energy efficiency ratio and the channel quality data, and triggers the current regulation and control unit to perform cooperative control on each coil unit of the transmitting coil module when the anti-interference mode needs to be started. And foreign matter detection and efficient information anti-interference transmission are synchronously realized.

The invention relates to the field of computer vision and deep learning, in particular to a related research and technical scheme for enriching a data set by adopting RandAuger data enhancement in a data preprocessing stage in deep learning and replacing a loss function and adding an attention module in a YOLOv8 target detection algorithm. An original loss function CIOU is replaced by an SIOU loss function, and a new attention mechanism module RACmix is added to a Neck part of YOLOv8, so that the small target sensing ability of the network under complex conditions is enhanced, and the accuracy and robustness of airport runway foreign matter detection are improved through a modified network model result.

The invention discloses a foreign matter detection method, a server, a foreign matter detection system, a foreign matter detection device and a computer readable storage medium. The foreign matter detection method is applied to the server in the foreign matter detection system. The foreign matter detection system further comprises a transmitting end and a receiving end. The transmitting end is used for transmitting a data packet to the receiving end. And the receiving end is used for receiving the data packet, acquiring the CSI original data according to the data packet, and sending the CSI original data to the server. The foreign matter detection method comprises the following steps: receiving CSI original data; and judging whether a moving object enters the charging area in the wireless charging process or not according to the CSI original data. Therefore, the detection work of whether the moving object enters the charging area in the wireless charging process can be realized, and the method is suitable for detecting various moving objects, and has the advantages of high detection precision, small environmental influence, high safety, low complexity, low cost, no dependence on WPT system operation and the like.

The utility model discloses an interface foreign matter detection circuit. The interface foreign matter detection circuit comprises a detection module and a judgment module, the detection module comprises a voltage dividing unit and a comparison unit, the voltage dividing unit is arranged between the SBU pin of the interface to be detected and the output end of the judgment module, and the comparison unit is arranged between the SBU pin of the interface to be detected and the input end of the judgment module. When a foreign matter appears at the interface to be detected, the interface generates impedance to the ground or VBUS, the comparison unit compares the generated impedance with the voltage dividing unit and then outputs a level signal, the judgment module analyzes the level signal and judges whether the foreign matter exists at the interface, the structure is simple, and the cost is low.

The invention discloses a method for detecting deviation and foreign matters of a conveyor belt of a mining belt conveyor, which comprises the following steps of: S1, acquiring image data including the conveyor belt, a carrier roller and the foreign matters, segmenting and marking the area of the conveyor belt, and marking bounding boxes of the carrier roller and the foreign matters; s2, training a multi-task detection model based on the image data, constructing an improved A-YOLOM network, and constructing an adaptive cross-scale feature fusion mechanism by introducing a progressive feature pyramid network, so as to significantly improve the spatial perception capability of foreign matters; meanwhile, the overall network parameter quantity is reduced by adopting GSConv; s3, inputting a to-be-detected image into the trained multi-task detection model, and obtaining a segmentation result, a carrier roller detection frame and a foreign matter detection frame of the conveyor belt; and S4, fault identification. The coal foreign matter detection precision is effectively improved, the parameter quantity of the model is reduced, foreign matter detection and conveying belt segmentation are carried out in one model at the same time, the conveying belt deviation detection time is shortened, and the detection efficiency is improved.

A misalignment tolerant foreign object detection approach is proposed for use with wireless charger systems where sensor coils or portions thereof are mirrored about one or more axes of radial symmetry. A first variant is proposed adapted for x-axis misalignment tolerance, and a second variant is proposed for simultaneous x and y axis misalignment tolerance. Further variants are proposed with additional rotational misalignment tolerance. Detection of the foreign object despite misalignment in receiver and transmitter coils is an important safety and efficiency consideration.

The embodiment of the invention provides a method for detecting foreign matters on a conveyor belt, and the method comprises the steps: obtaining a first image according to an image collection device in the transportation process of the conveyor belt; wherein the first image comprises visual information of the surface of the conveyor belt and objects on the conveyor belt; inputting the first image into a conveyor belt foreign matter detection model obtained by pre-training, and outputting a detection result corresponding to the first image; wherein the detection result comprises a second image, at least one target foreign matter name and confidence corresponding to the target foreign matter name, the second image comprises a detection frame used for framing the target foreign matter, and the conveyor belt foreign matter detection model at least comprises a first sub-module, a second sub-module and a third sub-module. According to the technical scheme, foreign matter detection is carried out on the conveyor belt based on the conveyor belt foreign matter detection model obtained through pre-training, the foreign matter on the conveyor belt can be rapidly and accurately recognized, and the effect of remarkably improving the detection efficiency is achieved.

The invention discloses a foreign matter detection method and device for a glass fiber cloth cover and a medium, and relates to the technical field of intelligent sensing, and the method comprises the steps: setting a transverse range based on a calibration value of scanning a transverse coverage width, extracting a minimum longitudinal coordinate and a maximum longitudinal coordinate of an update node, constructing a reconstruction point, and combining a reference difference to obtain a foreign matter detection result. A continuous foreign matter state field is constructed on the reconstruction points, a time window sequence is extracted based on the continuous foreign matter state field, an optimization objective function is constructed, iterative optimization is carried out, the final order and diffusion intensity of each reconstruction point are obtained, and a residual error is calculated; by introducing a time sequence judgment method based on fractional order kinetic parameter inversion, transient disturbance and real foreign matter events are effectively distinguished, the false alarm rate is reduced, and the stability of foreign matter type judgment is improved; and the space-time consistency and reliability of a foreign matter detection result under a high-speed production condition are improved.