660results about How to "Reduce missed detection" patented technology

The invention provides a battery film production automation control method and system based on visual inspection, and relates to the technical field of production control, and the method comprises the steps: collecting a film image, carrying out the fusion, generating a fused film image, carrying out the noise reduction, constructing a feature point matching relation, removing wrong point pairs, carrying out the geometric correction, carrying out the sub-pixel-level alignment, and obtaining a registration image; edge enhancement is carried out based on an improved Frangian filtering algorithm, an edge region is repaired and segmented to obtain a film edge contour, three-dimensional reconstruction is carried out, three-dimensional point cloud coordinates are solved, a film three-dimensional contour model is reconstructed, a film defect detection model is constructed, and a film defect distribution diagram and defect confidence are obtained through forward propagation; performing association analysis with a production process, constructing a feature mapping model, generating a defect knowledge base and matching a process parameter optimization strategy, generating a dynamic instruction and sending the dynamic instruction to a station control unit, monitoring operation data, performing closed-loop feedback control, and obtaining a current optimal control scheme.

The invention discloses a textile product defect detection method and system, and the method comprises the steps: collecting a textile surface image, and carrying out the preprocessing; constructing a feature extraction model, and extracting multi-modal features of the textile fabric surface defects; analyzing the extracted features, and classifying defect types; positioning defect positions, and marking defect areas and types; and outputting a defect detection report, and optimizing model parameters according to a detection result. Through multi-modal feature extraction and a feedback optimization mechanism, the classification accuracy and the positioning precision of textile fabric defect detection are remarkably improved, and particularly, the method has outstanding performance in detection of complex and low-contrast defects. According to the invention, the detection efficiency is greatly improved, false detection and missing detection are reduced, and an efficient and intelligent solution is provided for quality control of the textile industry.

The invention discloses a photovoltaic cleaning robot based on automatic path planning and an operation method thereof, relates to the technical field of robots, and solves the problem of cleaning blind spots in an existing cleaning robot and the endless loop risk of a path planning algorithm. According to the invention, the surface information of the photovoltaic panel is accurately captured through the multispectral acquisition module, a high-resolution and high-definition comprehensive image is generated through the data fusion processing module, then dirt is accurately identified on different scales by using a multi-scale pyramid structure and an adaptive feature fusion mechanism, and an attention mechanism and environmental perception data are combined, so that the real-time performance of the system is improved. The recognition threshold is dynamically adjusted, and misrecognition and missing detection are reduced; and finally, enabling the robot to autonomously learn in a complex environment and optimize an obstacle avoidance strategy through a dynamic obstacle avoidance algorithm based on deep reinforcement learning. The intelligent level and the cleaning effect of an existing photovoltaic cleaning robot are greatly improved.

The invention discloses a lightweight semantic segmentation network and method for power grid inspection, and the method comprises the steps: employing an encoder-decoder architecture, inputting original data into a lightweight MobileNet v2 backbone network for feature extraction at an encoder part, inputting an obtained feature map into a lightweight large-scale ASPP module, and carrying out the feature extraction; further performing multi-scale feature extraction on the high-dimensional feature map; and in the decoder part, high-dimensional features and low-dimensional features obtained by the backbone network are respectively input into a cross attention wavelet fusion module, after an obtained feature map is fused with the output of a decoder, the feature map is recovered to the size of original data through down-sampling operation by a full-dimensional dynamic convolution post-processing module, and a final prediction mask is obtained. According to the invention, by optimizing a coding and decoding structure, the receptive field of the model and the modeling capability of a narrow and long target are improved, the balance between the precision and the complexity can be effectively realized, and the method is suitable for an unmanned aerial vehicle inspection system with limited resources.

The invention discloses a weld defect 3D point cloud detection method, and relates to the technical field of defect detection, and the method comprises the following steps: S1, point cloud data acquisition and preprocessing; s2, point cloud data enhancement and feature extraction; s3, weld defect detection and identification; and S4, explaining a detection result. According to the 3D point cloud detection method for the weld defect, the point cloud data is enhanced by means of point cloud completion and density increase, so that incomplete structural information and semantic information of a target due to few obtained target point clouds are avoided, the influence of point cloud sparsity on a detection result is effectively relieved, and the detection accuracy is improved. Therefore, false detection or missing detection of weld defects is effectively reduced, meanwhile, detection results are labeled and explained in various modes of colors, shapes and labels, various presentation modes of generating a three-dimensional model, labeling defect positions and providing detection reports are provided, and the detection results can be presented to users in an easy-to-understand mode.

The invention discloses a saliency target detection method for an intelligent auxiliary driving system, and the method comprises the following steps: 1, obtaining a driving scene image based on a driver visual region in real time through a vehicle-mounted image collection device, and carrying out the preprocessing of the image; step 2, performing bottom-layer multi-scale feature extraction, interaction and global enhancement of feature information on the backbone network, and extracting rich multi-scale feature information; step 3, the neck network comprises an AESwinTransform attention module, and a cross-layer feature guide fusion module and a multi-level feature interaction fusion module in the TFG network, and the multi-level features extracted from the backbone network are fused together; step 4, detecting and classifying the feature map; and 5, inputting the adversarial sample and the normal sample into the model in a mixed manner, and performing adversarial training by using the trained model. According to the method, the detection precision of the small saliency target can be improved, the generalization ability and robustness of the model in a complex environment are improved, and the interference of irrelevant scene information is reduced.

The invention discloses a high-precision industrial quality control system based on an AI large model and an application method, which are characterized by being applied to detection and measurement of industrial products. Comprising a product data acquisition module in an industrial production process, a data preprocessing module, an AI large model suitable for industrial quality control, a quality detection module, a result feedback and decision support module, a control module and a system management module, the application of the AI large model suitable for industrial quality control comprises the steps of constructing the AI large model suitable for industrial quality control, calling the AI large model suitable for industrial quality control, executing the control system, carrying out multi-modal data fusion, and carrying out multi-modal data fusion. The product data acquisition module in the industrial production process acquires data of multiple modes for quality detection and measurement, the AI large model suitable for industrial quality control adopts a data fusion algorithm, and the feedback module provides a visual detection result. And visually displaying the detected quality problem to a user in the form of an image and a chart.

The invention relates to the technical field of defect detection, in particular to a precise copper material surface defect identification method and system based on optical online detection.The method comprises the steps that a plurality of light source devices are arranged on the surface of a precise copper material, and a multi-light-source illumination system is constructed; dividing the surface of the precise copper material into a plurality of detection areas, and installing an industrial camera in each detection area; collecting surface images of the precise copper material through a plurality of industrial cameras, and adding a timestamp to each surface image to obtain a copper material surface image; the collected copper material surface image is transmitted to a data processing center for feature processing, and a copper material surface feature image is obtained; and constructing a surface defect identification model, and inputting the copper material surface feature image into the surface defect identification model to obtain a copper material surface defect identification result. According to the invention, the hysteresis and inconvenience of off-line detection are effectively solved, the detection precision and efficiency are greatly improved, and the risks of missing detection and false detection are effectively reduced.

The invention relates to the technical field of machine vision and detection, in particular to a shoe pattern defect detection system based on the visual technology, which comprises an acquisition processing unit, a three-dimensional model unit, a defect detection unit and a defect processing unit. The three-dimensional model unit reconstructs an accurate shoe pattern three-dimensional model in sequence, the defect detection unit divides the model into a plurality of areas, calculates the average curvature, and accurately analyzes and judges the flatness defect degree by adopting a plane fitting algorithm based on a least square method and a curvature difference algorithm based on a sliding window; and the defect processing unit automatically processes the shoes with serious defects, so that high efficiency, accuracy and automation of shoe pattern defect detection are realized, and the quality detection level of shoe production is powerfully improved.

The invention discloses a mobile phone screen glass defect detection method based on improved YOLOv8, and relates to the technical field of mobile phone screen glass cover plate quality detection. The method comprises the following steps: simulating an industrial production environment, collecting mobile phone screen glass images, and constructing a data set; improvement and optimization are carried out on the basis of the YOLOv8 target detection network model, a mobile phone screen glass defect detection classification model is constructed, and parameters are trained and optimized; constructing a semantic segmentation model of the YOLOv8 network, segmenting the defects of the mobile phone screen glass image to be identified, and realizing detection and identification of different defect category edges; and fitting the actual damage size of the mobile phone screen glass defect range. The mobile phone screen glass defect detection system is suitable for accurate detection of mobile phone screen glass defect characteristics, can measure the defect range, and provides intelligent and efficient services for the mobile phone screen glass quality inspection process.

The invention discloses a PE pipe quality detection method and system based on a target image, and relates to the technical field of pipe quality detection. In order to solve the problems that multi-angle image data cannot be obtained, under the condition of complex surface textures or slight defects, false detection or missing detection possibly occurs, so that the detection accuracy is influenced, and the method cannot adapt to defect detection of various types of pipes. Through multi-angle image acquisition and preprocessing, the comprehensiveness and definition of image data are improved, false detection or missing detection caused by complex surface textures or slight defects is reduced, the adaptability of a detection system and extraction and matching of target features are enhanced, the key detection area of the PE pipe can be recognized more accurately, preset standards and key features can be compared, and the detection accuracy is improved. According to the method, the quality of the PE pipe is accurately evaluated, the detection accuracy and efficiency are improved, and an operator can quickly recognize problems through visual output and a detailed detection report.

The invention discloses an abnormal behavior detection method and device based on improved YOLOv8 and a storage medium. According to the method, a new FDPN structure is provided to replace a neck network architecture of an original YOLOv8 model; the FDPN has the advantages of a feature focusing module and a feature diffusion mechanism, the feature focusing module effectively captures and integrates cross-scale feature information through parallel deep convolution operation, and the diffusion mechanism promotes contextual information to flow among scales, so that each feature map contains detailed semantic information, the small target detection precision is improved, and the detection efficiency is improved. And the adaptability of the network to shielding and complex backgrounds is enhanced. In addition, an RCSOSA module and a DyHead dynamic prediction head are also introduced; the RCSOSA module extracts and enhances diversified features through a RepVGG module, optimizes feature selection and channel shuffling through a plurality of RCS modules, and enhances feature fusion between channels; and by using the DyHead dynamic prediction head, the recognition capability of the shielded crowd can be improved. According to the method, the robustness and the precision of abnormal behavior detection in a complex scene are remarkably improved.

The invention discloses a PCB production line defect detection analysis method and system based on an identification model, particularly relates to the technical field of PCB quality detection, and is used for solving the problems of low PCB defect detection precision and high omission ratio in the prior art. The method comprises the following steps: scanning the whole surface of a PCB to generate a thickness distribution diagram, identifying a thickness non-uniform region by adopting a threshold segmentation algorithm, and marking a key detection region; potential defects irrelevant to process fluctuation are identified through historical production data analysis; and adjusting the polarization angle of the light source to analyze the reflectivity difference, and identifying the hidden defect area in combination with the microstructure of the material. The union set of the potential defect area and the hidden defect area is marked as the suspected incomplete area, and the influence of the suspected incomplete area on the performance of the PCB is analyzed according to the quality standard, so that the detection precision and comprehensiveness are improved, and the high-quality control of the PCB is realized.

The invention relates to a multi-modal target identification and tracking prediction method based on a laser radar and a binocular camera, and belongs to the field of robot / unmanned system perception identification. Comprising the following steps: preprocessing a target two-dimensional image acquired by a binocular camera and target point cloud data acquired by a laser radar to obtain a preprocessed two-dimensional image and point cloud data; s2, performing two-dimensional target detection and tracking detection based on the preprocessed two-dimensional image to obtain a two-dimensional target detection and tracking result; s3, performing three-dimensional target detection based on the preprocessed point cloud data and the two-dimensional target detection and tracking result to obtain a three-dimensional detection result; and S4, based on the pixel coordinates in the two-dimensional target detection and tracking result and the pixel coordinates of the vertexes of the target point cloud boundary in the three-dimensional detection result, target fusion tracking prediction is carried out, and the target ID, the position, the category and the target point cloud boundary are obtained. And accurate identification and tracking of the target are realized.

The invention discloses an intersection blind area auxiliary driving method and system based on V2X, and relates to the technical field of intelligent auxiliary driving, and the method comprises the steps: collecting multi-modal data through a vehicle-mounted sensor, carrying out the preprocessing of the multi-modal data, carrying out the feature extraction of the preprocessed multi-modal data, and forming a modal feature vector; based on the multi-modal feature vector, a deep learning model is adopted for comprehensive perception, and the position of an obstacle in the blind area of the intersection is recognized; monitoring the position change of the obstacle in the blind area of the intersection in real time, and predicting the future movement track of the obstacle; evaluating the risk level of the vehicle passing through the intersection according to the future movement track of the obstacle; and receiving real-time traffic information through V2X communication, and generating a driving decision in combination with the real-time traffic information and the risk level. According to the invention, a safer and more efficient driving decision is finally generated through the combination of real-time obstacle position change monitoring, motion trajectory prediction, risk assessment and V2X communication.

The invention discloses a data processing and quality evaluation method for efficiency test data of an electric drive system. The method comprises the following steps: acquiring an original electric drive efficiency test data set; performing noise reduction on the original electric driving efficiency test data set through a probability distribution-based data noise reduction method to obtain a noise-reduced electric driving efficiency test data set; carrying out the first abnormity detection and processing of the noise reduction electric drive efficiency test data set through a DBSCAN model, and obtaining a preliminary screening electric drive efficiency test data set; carrying out the second anomaly detection and processing of the preliminary screening electric drive efficiency test data set through an LOF algorithm model and an iForest algorithm model, and obtaining a preprocessed electric drive efficiency test data set; and performing quality evaluation on the preprocessed electric drive efficiency test data set through the constructed six-tuple data quality model. According to the invention, effective noise reduction and abnormity detection can be carried out on the efficiency test data of the electric drive system, so that the data quality is improved.

The invention discloses an unmanned aerial vehicle mechanical arm control system and method based on machine vision, and relates to the technical field of mechanical arm control. The system comprises an image acquisition module, a feature point extraction and matching module, a pose analysis module, a control parameter calculation and adjustment module and a motion control and execution module. The image data acquisition module acquires target area image data; the feature point extraction and matching module is used for extracting feature points of a target object from a reference image, constructing a feature set, extracting and matching the feature points of real-time image data, and screening effective matching points; the pose estimation module analyzes the actual pose of the unmanned aerial vehicle mechanical arm according to the matching points and calculates the actual pose and the expected pose through an optimization algorithm; the control parameter calculation and adjustment module is used for calculating the deviation between the expected pose and the actual pose, setting a control gain and updating a motion control parameter; and the motion control and execution module updates the motion track of the unmanned aerial vehicle mechanical arm in real time based on the adjusted control parameters.

The invention discloses a steel plate butt weld pseudo defect ultrasonic detection method, system and device and a medium, and relates to the technical field of weld detection, the method comprises the following steps: setting a plurality of detection areas on a steel plate butt weld based on acquired structure parameters and surface characteristic data of a to-be-detected steel plate; performing multi-frequency and multi-angle ultrasonic scanning according to a preset first detection parameter by taking each detection area as a basic unit to obtain corresponding echo signal data; analyzing and identifying potential pseudo-defect signals, and calculating corresponding pseudo-defect characteristic values according to the pseudo-defect signals; taking each detection area as a basic unit, performing pseudo defect feature analysis according to the second detection parameters, and determining reliability evaluation data of pseudo defects; based on the pseudo-defect reliability evaluation data and the pseudo-defect characteristic value corresponding to each detection area, the pseudo-defect condition of the steel plate butt weld is analyzed, and a final weld quality comprehensive evaluation result is generated. According to the invention, the detection accuracy of steel plate butt weld detection is improved.

The invention relates to the technical field of braid quality detection in industrial production, in particular to a defect data processing query method and system for braid visual inspection. The method comprises the steps of obtaining braid image data, performing enhancement processing on an image based on a dynamic illumination compensation algorithm, and generating a high-quality image in combination with material characteristics; inputting the enhanced image data into a deep learning defect detection model, detecting and classifying defect areas in the image, and updating the model weight based on defect feature parameters; performing multi-dimensional indexing processing on the detected defect feature data to generate a defect distribution database supporting real-time query; and the defect distribution database and the production process parameter sequence are combined, the causal relationship of defect occurrence is analyzed, process optimization parameters are generated and fed back to the detection module, and self-adaptive updating of model parameters is completed. According to the invention, high precision, high efficiency and closed-loop optimization of braid defect detection are realized, and intelligent support is provided for industrial production quality control.

The invention provides a hydropower station floating object detection method based on global relation modeling, and aims to improve the detection precision and efficiency of hydropower station water surface floating objects, and the method comprises the steps: collecting hydropower station water surface scene data through a multi-view device; extracting shallow multi-scale features through a lightweight convolutional network; modeling a global relationship between the floaters through a multi-head self-attention mechanism; carrying out the classification and positioning of floating objects through a target detection head; network training is performed through a multi-task loss function to optimize the model; and reasoning by using the trained floating object detection network, inputting a to-be-detected water surface image into the network, outputting the category and the position of the floating object, and finally generating a detection result. According to the method, the global relation modeling method is introduced, the detection precision of the hydropower station water surface floating objects is effectively improved, detection errors caused by insufficient local features in a traditional method are reduced, and the method has low calculation complexity and is suitable for real-time detection in practical application.

The invention discloses a welding seam defect detection method and system based on WGOA-HHO improved YOLOv8, and relates to the technical field of deep learning, and the method comprises the following steps: receiving a welding seam image data set, employing a Canny edge detection algorithm to carry out the preprocessing of the welding seam image data set, and obtaining a preprocessed welding seam image data set; the preprocessed weld seam image data set is input into a pre-established YOLOv8 weld seam defect detection model for training, and based on a WGOA-HHO optimization algorithm, an Improved-YOLOv8 model is obtained through output; and acquiring a to-be-detected welding seam image, inputting the to-be-detected welding seam image into the Improved-YOLOv8 model, and outputting to obtain a welding seam defect detection result image.

The invention relates to the technical field of assembly welding, and particularly discloses an assembly welding positioning method and device for automobile parts. A welding state image between a target hush pipe and a target partition plate is collected through a camera; carrying out welding state mask segmentation on the welding state image by using an image identification and analysis algorithm based on computer vision, and then carrying out welding state feature extraction on the segmented welding detection area state mask image; and according to the expression obtained through neighborhood importance optimization of the extracted welding detection state characteristics, intelligent detection on whether defects exist in the welding state between the target hush pipe and the target partition plate or not is achieved. In this way, tiny defects such as tiny cracks and internal pores can be captured, and therefore the accuracy of defect recognition is improved. Moreover, the detection speed is greatly improved, the consistency and stability can be maintained, and the probability of missing detection or false detection is reduced, so that each welding point is ensured to be subjected to strict quality control.

The invention relates to the technical field of appearance defect monitoring, in particular to a rubber whole mold appearance defect detection method and system based on machine vision. The method comprises the following steps: acquiring rubber whole mold parameter data; performing O-shaped ring inner and outer diameter setting on the rubber whole mold based on the rubber whole mold parameter data to obtain rubber whole mold built-in O-shaped ring inner and outer diameter adjustment data; performing first surface overall shooting on the rubber whole mold according to the inner and outer diameter adjustment data of the O-shaped ring arranged in the rubber whole mold to generate a first overall appearance image of the standard rubber whole mold; performing detection area division on the first overall appearance image of the standard rubber whole mold to generate rubber whole mold detection area data; and comparing the rubber whole mold detection area data with a preset detection area number range to generate rubber whole mold product specification data. Through accurate parameter setting, automatic region division, focal length adjustment, high-precision defect detection and comprehensive report generation, the accuracy and comprehensiveness of defect detection are improved.

The invention provides an infrared small target detection method and system based on self-adaptive partial self-attention, and the method comprises the steps: carrying out the processing of a single-frame infrared image through a double-residual attention block and a feature encoder, and obtaining a coding feature map; performing maximum pooling down-sampling processing on the coding feature maps of different levels to obtain coding features, and splicing the coding features in a channel dimension to obtain a multi-scale advanced coding feature map; obtaining a global context long-range feature map based on the multi-scale advanced coding feature map; obtaining a code-added feature map based on the global context long-range feature map; and inputting the decoded feature map into the prediction head to obtain a prediction result. According to the method, the salient small target in the infrared image is effectively extracted, the self-attention block of the self-adaptive part is utilized to integrate global context information, the long-distance dependence learning ability of the model is enhanced, and the perception ability of the position and the shape of the infrared small target is effectively enhanced.

The invention discloses a road small target detection algorithm based on Real-ESRGAN and improved YOLOv5, and the algorithm comprises the steps: S1, preparing a data set, collecting data, and dividing the data into a training set, a verification set and a test set; s2, building a YOLOV5 algorithm model, inputting data and training the data to obtain a YOLOV5 algorithm model weight; the trained YOLOV5 algorithm model weight is used to detect whether a small road target appears, and if yes, a detected image frame is intercepted and stored; s3, performing super-resolution reconstruction on the detected image frame, and outputting a detection frame image after the super-resolution reconstruction; and S4, identifying the output detection frame image after super-resolution reconstruction, and updating a detection result after classification. By adopting the Real-ESRGAN and the improved YOLOv5 detection algorithm, the detection confidence and accuracy of the small road target are improved, and the false detection and missing detection conditions are reduced.

The invention provides an AI analysis method and system based on a straight ethyl mirror image, and relates to the technical field of AI.The AI analysis method comprises the steps that multi-scale features of the straight ethyl mirror image are extracted through a pre-trained deep convolutional neural network, and feature fusion is carried out through a self-adaptive feature weight distribution module; and performing lesion area detection on the fused feature map by using a double-branch detection network, and classifying the detected lesion areas. And performing three-dimensional reconstruction based on a continuous orthoscope image sequence, mapping a detection result into a three-dimensional model, realizing accurate positioning and three-dimensional visualization of a lesion area, and recording three-dimensional coordinate position information of the lesion area. The method can improve the accuracy and efficiency of the image analysis of the straight ethyl mirror, and assists a doctor in diagnosis.

The invention relates to the field of gas leakage detection, and discloses a gridding directional laser methane detection system, which comprises the following steps of: acquiring area information which comprises a methane absorption signal, environmental data, background gas, an area image and historical leakage data; generating first two-dimensional structure data from the collected regional information, wherein the first two-dimensional structure data comprises a data matrix and a relation matrix; first one-dimensional structure data is obtained based on sorting of the first two-dimensional structure data, wherein the first one-dimensional structure data comprises n data items sorted according to time; and inputting the first two-dimensional structure data into the detection model, and outputting a methane leakage processing strategy. According to the invention, detection and treatment strategies of methane in fuel gas can be realized, and a distribution strategy of the methane laser detection device can be further given.

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.

The invention discloses a visual detection device for adhesive label paper production, and relates to the technical field of label paper visual detection, the visual detection device comprises a detection table and further comprises a leveling device, a winding device is arranged at the top of the detection table, a maintenance frame is fixedly installed at the top of the detection table, and visual detection equipment fixedly penetrates through the top of the maintenance frame; a driving plate is fixedly mounted at the top of the detection table, a servo motor is fixedly mounted on the front side of the driving plate, a placement frame is fixedly mounted at the top of the detection table, a rotating motor is rotatably mounted on the rear side of the placement frame, and light supplementing equipment is fixedly mounted at the output end of the rotating motor; the correcting rod moves in the direction close to the label paper to drive the correcting frame to move to correct the position of the label paper to be detected, it is ensured that the label paper is located at the correct center position during detection through position correction, the image capturing accuracy is improved, and the rejection rate caused by misjudgment is further reduced.

The invention discloses a bird intrusion detection method for an iron tower scene, and the method comprises the steps: obtaining a plurality of frames of monitoring images of a target region, carrying out the target detection through a bird target monitoring model, and obtaining M continuous frames of monitoring images after a certain frame if the frame detects a bird; judging whether a bird is detected in each frame of the M frames of monitoring images and whether the size of a bird target candidate detection frame in each frame is greater than a preset pixel value; judging whether the area change rate of the bird target candidate detection frames in all two adjacent frames meets a preset first judgment threshold value or not; and judging whether the change rates of the central point positions of the bird target candidate detection frames in all adjacent two frames meet a preset second judgment threshold value, and if so, determining that the current target area is invaded by birds. According to the method, the problems of missing detection and false detection caused by single-frame detection are effectively reduced, the detection accuracy is remarkably improved, and the stability and consistency of a detection system in an all-weather environment are ensured.