1484 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 transit foreign matter invasion risk identification and evaluation method and system, and belongs to the technical field of target identification of image processing. Image data of a running environment in front of a train are collected in real time; performing foreign matter detection on the acquired image data frame by frame through a foreign matter detection algorithm, and obtaining the category and position information of the detected foreign matter if the foreign matter is detected to exist in a certain frame of image; segmenting a track region in the frame of image through a track segmentation algorithm to obtain position information of the track region; based on the relative position relation between the foreign matter and the rail area, the distance between the foreign matter and the train and the category of the foreign matter, comprehensively evaluating and outputting the rail transit foreign matter invasion risk level. According to the method, the foreign matter invasion risk in the rail transit scene can be accurately identified in real time, meanwhile, a rail transit foreign matter invasion risk grade evaluation system is established, and efficient detection, identification and evaluation of the rail transit foreign matter invasion risk are achieved.

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 lightweight power transmission line foreign matter detection method and device, a medium and a product, and relates to the field of power transmission line foreign matter target detection, and the method comprises the steps: taking a YOLOv8n network as a basis, replacing the last three convolution blocks in a backbone network of the network and a first convolution block in a neck network with a lightweight adaptive weight down-sampling module, replacing a C2f module of a backbone network of the network with an efficient multi-scale fusion module, and after a connection layer of a neck network of the network, introducing a space and channel reconstruction convolution module to combine the space and channel reconstruction convolution module with the C2f module of the neck network of the network to obtain a lightweight transmission line foreign matter detection model; and obtaining the position of the foreign matter and the type of the foreign matter by using the trained and optimized model. The method can significantly improve the detection precision of a small target under a complex background, enables the model to be lighter, and can be better suitable for the actual foreign matter detection of a power transmission line.

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 relates to a foreign matter detection wireless charging method, apparatus and device, and a storage medium. The method comprises the following steps: monitoring charging data of a target charging panel to a to-be-charged device based on a second detection signal; if the charging data is abnormal, identifying whether a metal foreign matter exists in the target charging area based on the second detection signal and the first detection signal; based on the target charging area image, identifying whether a non-metal foreign matter exists in the target charging area; if the target charging area has the metal foreign matter and / or the non-metal foreign matter, performing charging danger degree analysis on the target charging area based on a second detection signal corresponding to the metal foreign matter and the first detection signal and / or the target charging area image to obtain a danger coefficient; and obtaining a safety response strategy based on the danger coefficient, thereby solving the technical problem of how to quickly and accurately detect various types of foreign matters on the premise of not influencing the charging efficiency.

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.

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

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 invention relates to a belt foreign matter detection and sorting method based on deep learning. The method comprises the steps that a belt detection image is acquired; inputting the belt detection image into a pre-training model, and obtaining a foreign matter detection result output by the pre-training model; the foreign matter detection result is transmitted to mechanical arm control equipment, the control equipment plans and solves according to the real-time position and state of the mechanical arm, and the optimal track path of the mechanical arm is obtained; and the mechanical arm is driven to sort the foreign matter along the optimal track path, and a grabbing tool is selected based on the foreign matter category. According to the method, a deep learning target detection model is adopted, the reliability of foreign matter recognition is remarkably improved, and particularly, the method has better performance in light change and high-dust environments. Through intelligent path planning and priority scheduling strategies of the mechanical arm, the sorting speed can reach 60 pieces per minute, the sorting efficiency is improved by about three times, and meanwhile stability and consistency are guaranteed. According to the system, full-automatic detection and sorting are achieved, only one person is needed to monitor system operation, and the labor cost is effectively reduced.

The invention discloses X-ray meat product foreign matter detection equipment and a detection method.The detection equipment comprises a conveying mechanism and an X-ray detection machine arranged on the conveying mechanism, and is characterized in that the conveying mechanism comprises a first conveying belt and a second conveying belt which are distributed up and down, and the rear portion of the first conveying belt is located above the front portion of the second conveying belt; the rear part of the first conveying belt and the front part of the second conveying belt are positioned in the X-ray detector; the left side and the right side of the rear part of the first conveying belt are respectively provided with two groups of symmetrical clamping devices, and each clamping device comprises a transmission belt mechanism and a plurality of clamping mechanisms assembled on the transmission belt mechanism. The X-ray detection device has the advantages that a detected object is projected in two directions to form two complementary X-ray detection images, so that the inherent blind area problem of the single-light-source detection device is solved, and the accuracy of foreign matter detection is improved.

The invention relates to the technical field of image processing, in particular to a method for judging the image blurring degree based on an airfield pavement FOD monitoring system. Comprising the following steps: selecting a reference image; edge detection and energy value calculation; collecting and marking image data; constructing a deep learning model; training a deep learning model; and evaluating and predicting. According to the design, before FOD detection is carried out, the fuzzy degree of the detected image is detected, and the image which does not affect foreign matter detection is screened out for subsequent operation; by understanding the application of the image blurring degree in FOD detection, the efficiency, accuracy and stability of a detection system can be remarkably improved, manual intervention is reduced, the automation level is improved, resource utilization is optimized, and a solid guarantee is provided for airport safety and operation.

The invention relates to the technical field of target detection, and discloses a training method, device and equipment of a railway foreign matter phrase positioning model and a medium, which can perform cross alignment on an image feature vector and a text feature vector to obtain an aligned image feature vector and an aligned text feature vector. The aligned image feature vector and the aligned text feature vector are used for training, so that the model can accurately correspond the foreign matter described by the text to a target area in the image, phrase positioning in a complex scene is ensured to be accurate and errorless, the method is suitable for fine-grained scene description in railway foreign matter detection, and the accuracy and accuracy of the detection are improved. The trained railway foreign matter phrase positioning model can effectively execute and complete a railway foreign matter phrase positioning task, and phrase positioning of the railway foreign matter is effectively achieved.

The invention discloses a multi-layer screen cover plate glass interlayer foreign matter detection device and detection method, and belongs to the field of image processing, and the detection method comprises the following steps: image acquisition; extracting a region of interest; feature enhancement; carrying out anisotropic Gaussian filtering; edge detection; foreign matter screening. According to the method, the ROI image is enhanced by applying frequency tuning saliency detection FT and Haar wavelet transform, and an attention mechanism is introduced to fuse features, so that the saliency of tiny and low-contrast defects is enhanced; the influence of noise is reduced by adopting anisotropic Gaussian filtering, and important edge information is reserved and interference is suppressed by adaptively adjusting parameters of a filter.

The invention relates to the technical field of tunnel foreign matter detection, in particular to a visual detection method and system for foreign matters in an intelligent tunnel and a medium, and the method comprises the steps: determining a texture difference coefficient of each contour in each frame of tunnel grey-scale map based on the complexity and average distribution condition of texture feature coefficients of all pixel points on each contour; determining a shadow change coefficient of each contour in each frame of tunnel grey-scale map based on the correlation of histograms of all super-pixel blocks in the contour of the same target between different frames of tunnel grey-scale maps and the dispersion degree of texture difference coefficients of the contour of the same target between different frames of tunnel grey-scale maps; and foreign matters in the tunnel are detected in combination with the neural network model. By analyzing the texture difference between the inner side and the outer side of the contour and the influence degree of the light on the texture of the same target area between different frames, the accuracy of tunnel internal foreign matter detection is improved.

A power receiving apparatus capable of receiving power transmitted from a power transmitting apparatus capable of executing first foreign object detection and second foreign object detection, transmits, to the power transmitting apparatus, data to be used by the power transmitting apparatus to execute the first foreign object detection; before transmitting the data, determines whether a predetermined condition for the power transmitting apparatus to execute the second foreign object detection different from the first foreign object detection is satisfied; and transmits, to the power transmitting apparatus, a signal for causing the power transmitting apparatus to execute the second foreign object detection in accordance with the determination.

The invention discloses an infrared expressway foreign matter detection method based on a Mama framework, and the method comprises the steps: continuously carrying out the data collection of expressway scenes under different illumination conditions and different weather conditions through an infrared sensor, obtaining infrared image data, carrying out the preprocessing of the infrared image data through data augmentation, generating training data, and carrying out the detection of foreign matter in an expressway. Dividing the training data into a training set, a test set and a verification set in proportion; constructing a detection network based on a Mama framework, wherein the detection network comprises a student network and a teacher network; training the teacher network through back propagation based on the training set; training a student network based on the result of the teacher network and the real data to obtain a target detection network; and inputting a to-be-identified image into the target detection network to obtain a detection result. According to the invention, the method achieves the quick and accurate recognition of potential risks, guarantees the high detection precision through the combination of the infrared imaging technology and the powerful processing capability of a Mama frame, achieves the real-time response to foreign body threats, and remarkably improves the efficiency and safety of expressway monitoring.

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

This disclosure provides systems, methods and apparatuses for foreign object detection (FOD) in a wireless power transfer (WPT) system. An FOD protocol can be coordinated with different operating phases of the WPT system, such as an idle phase, a configuration phase, a connected phase, and a power transfer phase. An FOD assessment unit may perform foreign object detection assessments as part of each operating phase. The FOD protocol may include an initial idle phase foreign object detection assessment in the idle phase to handle a scenario in which a Power Receiver is placed on the Power Transmitter before the Power Transmitter has been turned on. The FOD assessment unit may adjust offset values used during each FOD assessment to improve accuracy of foreign object detection. The offset values may be adapted to accommodate movement of the Power Receiver after the initial idle phase foreign object detection assessment.

The invention provides a conveyor belt foreign matter detection method and system based on coal mine production. A camera is installed above or on the side face of the conveying belt, and images of objects on the conveying belt are collected through the camera; preprocessing the object image to generate a region image corresponding to the object part; detecting an image edge of the regional image, and extracting edge information and contour information of the regional image; generating matching parameters according to the contour information and a preset foreign matter shape template; and according to the matching parameter and the edge information, judging whether the object on the conveyor belt is a foreign matter. The foreign matter detection accuracy is improved, manual intervention can be greatly reduced through the conveyor belt foreign matter detection system based on the visual technology, the labor intensity is reduced, and the production efficiency is improved. Meanwhile, the production efficiency and safety are improved through the real-time monitoring and early warning functions, an intelligent and automatic solution is provided for industrial production, and the production cost and the safety risk are reduced.

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 embodiment of the invention provides a salvage unmanned ship for water surface tiny foreign matter image recognition and an image capturing and processing method thereof, and belongs to the technical field of water surface cleaning and water quality monitoring. The salvage unmanned ship comprises a ship body; the image acquisition and processing module is used for capturing an image of the water surface and identifying tiny foreign matters through a target detection algorithm; the garbage collecting device is mounted on the ship body and can collect tiny foreign matters on the water surface according to an identification result so as to clean garbage floating on the water surface; wherein the target detection algorithm is a Cascade R-CNN target detection algorithm, and the Cascade R-CNN target detection algorithm is combined with a feature pyramid network to process a multi-scale target through feature extraction from bottom to top and feature fusion from top to bottom. The Cascade R-CNN target detection algorithm is combined with the feature pyramid network to effectively solve the problems of low water surface garbage cleaning efficiency, difficulty in small foreign matter detection and the like in the prior art.

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.