4154 results about "False detection" patented technology

The invention discloses a glass lens surface scratch detection method and system, relates to the technical field of precision optical detection, and aims to solve the problems of scratch false detection, leak detection and poor algorithm adaptability caused by interference fringes, noise coupling and poor form adaptability in a high-reflection / complex coating process scene in the prior art. According to the scheme, an orthogonal polarization state composite light field is generated based on a multi-angle polarization light source array and a near-infrared compensation light source, and candidate regions are extracted through dynamic threshold segmentation and a direction gradient tensor matrix; gaussian pyramid multi-scale feature fusion and refraction angle consistency verification are utilized to eliminate artifact interference; constructing a direction constraint convolution kernel group to decompose scratches and background textures, and dynamically allocating computing resources in combination with a cascade network; feeding back closed-loop calibration light source wavelength and convolution kernel parameters in real time through coating parameters; according to the method, the precision and robustness of high-reflectivity surface scratch detection are remarkably improved, and meanwhile, the requirements for high-resolution image processing and real-time performance in a high-speed production line are balanced.

A target detection method, system and device based on cross-modal fusion and an attention guiding mechanism and a medium, the target detection method comprises the following steps: obtaining a visible light-infrared image pairing data set, and carrying out data processing and division on the visible light-infrared image pairing data set to obtain a training set, a verification set and a test set; constructing a multi-modal target detection network; setting network training parameters; training and optimizing the multi-modal target detection network by using the training set, outputting a training weight file after the training is finished, verifying the trained weight file by using the verification set, and selecting the weight file with the highest precision as an optimal weight file; loading the test set and the optimal weight file into a multi-modal target detection network, and performing target detection on the test set to obtain a target detection result; the system, the device and the medium are used for bearing and implementing the method. The method has lower false detection and false detection conditions, and improves the robustness and accuracy of target detection.

The invention discloses a deep learning-based small target defect recognition model training method, relates to the technical field of defect recognition model training, and aims at meeting small defect detection requirements, starting with high-resolution diversified data construction and accurate labeling, highlighting weak targets through multi-scale feature fusion and spatial attention, and realizing high-resolution target defect recognition. A hard case scene is processed in cooperation with layer-by-layer screening and secondary intensified training, real-time iterative optimization is achieved through multi-model fusion and online dynamic adjustment and optimization, finally, multi-mode and time sequence dimensions are expanded to capture deeper and dynamic defect information, the missing detection and false detection rate is greatly reduced, and the detection efficiency is improved. The detection efficiency and adaptability of micron-sized defects under a complex process background are improved; furthermore, by means of multi-source data such as infrared, X-ray or 3D morphology and a time sequence modeling means, multiple dimensions are fused, and hidden or early cracks are brought into a detection and prediction range, so that a high-reliability and evolvable intelligent recognition system for the tiny target defects is constructed.

The present disclosure relates to the technical field of videos. Disclosed are a video anomaly event detection method, apparatus and device, and a storage medium. The method comprises: acquiring the current video frame of an industrial site, and reconstructing, by means of an anomaly detection model, a current reconstructed frame corresponding to the current video frame, wherein the anomaly detection model is a model constructed on the basis of a two-stream autoencoder network, the two-stream autoencoder network being used for extracting a spatial feature and a temporal feature; calculating the current dynamic threshold of the current video frame, wherein the current dynamic threshold is used for performing anomaly detection on the current reconstructed frame; and on the basis of the current dynamic threshold and the current reconstructed frame, detecting abnormal events in the industrial site. In the present disclosure, whether abnormal events occur is detected by means of an anomaly detection model and a dynamic threshold value; therefore, the reconstruction capability of the model on the abnormal events can be suppressed, and an auto-encoder is encouraged to produce a higher reconstruction error for the abnormal events, thus reducing the problems of false detection or missed detection, and improving the accuracy of video anomaly event detection.

The invention provides a square power battery shell visual defect rapid detection method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining a high-resolution multi-view image sequence, outputting standardized image data, carrying out front visual scratch detection, outputting scratch defect positions and features, and outputting defect candidate regions containing confidence scores; and recognizing bubble defects, summarizing recognition results of the defect candidate areas, and generating a labeling image. The method solves the problems that in the prior art, due to the fact that multi-view image information is not fully fused, traditional scratch detection depends on a single edge feature, and a main defect recognition model is insufficient in recognition capacity for tiny defects such as bubbles, defect detection accuracy is low, missing detection and false detection phenomena are serious, and detection efficiency is high. According to the method, the problems that the defect is difficult to comprehensively and accurately locate and classify in the prior art are solved, the multi-view information fusion capability and the identification accuracy and detection efficiency of multi-class defects are improved, and high-precision, comprehensive and real-time quality monitoring of defect detection results is realized.

A system and a method for automatically discovering and managing actions in an application is disclosed. The system includes a data ingestion layer for receiving application data from multiple sources, a scanning and systematic traversal engine for interacting with UI elements and capturing network calls, an action mapping and generation module for correlating UI actions with API calls and categorizing actions, an AI-driven icon and description generator for creating visual representations and textual descriptions of actions, a user interface for displaying and modifying discovered actions, and a continuous monitoring component for triggering re-scanning based on coverage metrics, error detection, or version updates. The system employs synthetic data generation and AI-driven exploration to uncover hidden or undocumented APIs, enabling comprehensive mapping of an application's capabilities at the API level.

A sensor data evaluation device may include an artificial neural network, configured to receive and process first testing data, and output a first testing data output, wherein the first testing data represent sensor data of a first sensor detecting a sensor input; and one or more processors, configured to receive sensor deficiency information representing a deficiency of a second sensor; generate second testing data, wherein the second testing data are based on the sensor deficiency information; implement the artificial neural network to receive and process the second testing data; and output from the output layer a second testing data output; determine a difference between the first testing data output and the second testing data output; and if the difference is outside of one or more predetermined ranges or does not satisfy one or more predetermined criteria, send a signal representing a result of the determined difference.

The invention relates to the technical field of industrial nondestructive testing and computer vision, and particularly discloses an intelligent detection method for surface defects of a container bottom plate based on deep learning. The method comprises the following steps: synchronously acquiring data through a laser radar and a line scanning camera, complementing a shielding area of a point cloud, and executing coordinate normalization to generate preprocessed data; constructing a lightweight feature alignment network to realize cross-modal feature mapping and pixel-level error correction; segmenting a defect area by adopting an improved PointNet + + network and reconstructing a three-dimensional model; extracting defect geometric parameters and combining with material attributes to perform stress simulation and life prediction; aggregating multi-port safety life data to construct a federated framework to update model parameters; and synthesizing physically real defect samples based on false detection cases, and injecting the defect samples into the network for training. According to the method, the bottleneck of missing detection of internal defects in traditional two-dimensional detection is overcome, the defect detection rate and quantification precision are remarkably improved, full-life-cycle safety evaluation of the container is supported, and the efficient requirement of automatic port inspection is met.

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 relates to the technical field of power system automation, in particular to a substation management system and method based on the Internet of Things technology, and the system comprises a ubiquitous Internet of Things sensing matrix, an electric power intelligence brain evolution center, a space-time fusion twin module, a nonlinear optimization decision module, and a multi-level vibration risk management and control model. Wherein the ubiquitous internet-of-things sensing matrix acquires substation equipment, environment and power grid data in real time; the electric power intelligence brain evolution center carries out health assessment, life prediction and abnormity positioning on the equipment; the space-time fusion twinborn module constructs a digital twinborn body, fuses equipment space-time data and historical data, and performs operation and maintenance simulation and fault reproduction; the nonlinear optimization decision-making module is used for generating a response regulation and control strategy aiming at the nonlinear and uncertain problems in the operation of the power grid; and the multi-level vibration risk management and control model carries out dynamic assessment and strategy optimization on equipment faults, power grid safety and environmental risks. Therefore, the problems of low inspection efficiency, high false detection risk, poor transmission stability and the like in the prior art are solved.

The invention belongs to the technical field of target detection and recognition, and particularly relates to a thinking chain-based large and small model collaborative target detection and recognition method. According to the method, the small model is responsible for most of easy-to-detect targets, the calculation pressure of the large model is reduced, the large model is responsible for suspected samples, vision and language multi-mode reasoning is combined, the overall false detection rate and the omission ratio are both reduced, confidence evaluation is conducted through the joint probability, automatic screening and manual rechecking of uncertain results are achieved, the reliability of key results is guaranteed, and the method is suitable for large-scale popularization and application. According to the'pseudo thinking chain + pseudo label 'method, by means of reasoning and labels generated by the model, data dependence on manual labeling is reduced, only low-confidence samples are manually confirmed, the human intervention range is narrowed, the human cost is remarkably saved, and semantic information with finer granularity is provided for the model by introducing phrase-level feature descriptors. And the identification capability of complex target attributes and states is improved.

The invention provides an aviation video stream target identification processing method and system, relates to the technical field of image processing, artificial intelligence and aviation monitoring, and aims to realize the target identification processing of an aviation video stream through preprocessing modal data such as video, infrared and radar and combining an adaptive weighted fusion technology, a multi-stage visual feature extraction and a global convolution optimization technology. The system can extract target features from multi-level and multi-modal data; and a historical motion track and future motion prediction are further combined, the system dynamically corrects the target position, and finally the consistency of multi-modal data is ensured through cross validation. According to the method, the recognition precision and robustness of the system to weak and small targets are enhanced, missing detection and false detection are reduced, the adaptability is high, the requirement for computing resources is low, and the method is suitable for target recognition and tracking tasks in a complex dynamic environment.

The invention relates to a self-localization and motion perception method based on deep learning, and the method comprises the following steps: multi-modal perception: collecting RGB frames and event streams through employing a DAVIS346 event camera, obtaining texture and depth information through employing an RGB-D camera, and supplementing 3D structure data through a laser radar; hybrid optical flow driven motion perception: adopting a double-branch architecture of an improved RAFT network and an event optical flow private network; multi-modal 3D detection and trajectory management: fusing multi-modal data based on VoxelNeXt-Lite to generate a 3D detection frame, filtering false detection by combining point cloud density clustering and an event density threshold, then constructing a space-time diagram associated trajectory through a Spatio-Temporal Graph Transformer, and optimizing pedestrian trajectory prediction continuity by using an LSTM (Long Short Term Memory) model perceived by a gait cycle; multi-view semantic graph matching: constructing a 3D semantic voxel map containing vertical features, calculating scene similarity in combination with top view NetVLAD features and deformable graph matching, and dynamically adjusting semantic weight by using a situation encoder and knowledge graph reasoning; and carrying out multi-sensor fusion and robust positioning.

The invention relates to a building prefabricated part quality detection method based on multi-modal vision. According to the method, multi-modal data including 2D image data and 3D point cloud data are obtained, an improved YOLOv8 model is used for performing defect coarse positioning on the 2D image data, defect parameters are calculated, defect areas such as cracks and exposed ribs can be quickly locked, and the parameters of the defect areas can be obtained. And by means of SIFT feature matching and ICP point cloud registration technologies, comparing with a two-dimensional template and three-dimensional geometric parameters of the BIM standard component model to obtain a two-dimensional registration difference chart and a three-dimensional deviation thermodynamic chart. And finally, according to the defect confidence coefficient, the two-dimensional registration difference chart and the three-dimensional deviation thermodynamic diagram, a preset dynamic weighting rule is adopted to carry out joint decision making, and a quality detection result is obtained. According to the method, through the multi-modal data, the improved YOLOv8 model, the point cloud registration technology and the preset dynamic weighting rule, the false detection problem can be effectively solved, the detection reliability and accuracy under the complex working condition are improved, and the quality management level of the building prefabricated part is improved.

The invention discloses a steel plate surface defect detection method based on improved YOLOv8, and belongs to the technical field of stainless steel plate defect identification, and the method comprises the following steps: S1, data collection and division: obtaining steel plate surface defect picture data, and dividing the data into a training set and a test set; s2, performing data enhancement and preprocessing to obtain a sample training set; s3, constructing a defect detection model based on the improved YOLOv8 network; s4, designing a loss function, and decoding a prediction result; and S5, repeatedly inputting the to-be-detected sample data in the sample training set into the defect detection model for training until the training frequency reaches a preset epoch, outputting a final defect detection model, then inputting the to-be-detected sample data in the test set into the final defect detection model, and obtaining a steel plate surface defect detection result. According to the invention, the detection precision and accuracy of the small target can be effectively improved, and the missing detection and false detection phenomena of small target defects are reduced.

The invention relates to the technical field of fabric detection, and discloses a fabric defect intelligent detection method and system based on AI visual identification. According to the method, motion blur is quantized through motion state data, optical blur caused by fabric motion is eliminated through deconvolution solution, so that motion interference in the fabric transmission process is processed in a targeted mode, self-adaptive balance of the deblurring capacity and the feature retention capacity is achieved, and then based on the optical interference principle, the deblurring capacity and the feature retention capacity are improved. Through a dynamic calibration system combining hardware-level real-time compensation and multi-dimensional optical parameter calibration, dynamic optical parameter calibration of primary correction data is realized, then fabric defect characterization data is extracted to accurately obtain defect features, and finally, a detection-production line control closed loop is constructed through a quality quantitative index and a comprehensive risk value, so that fabric defect detection is realized. The fabric defect detection precision can be improved, so that the problem of high defect missing detection and false detection rate caused by optical data distortion due to movement and environment interference in a traditional method is effectively solved.

The invention provides a machine vision defect real-time detection and classification method and system based on deep learning, and relates to the field of machine vision detection.The method comprises the steps that regional enhancement weights are determined by calculating local entropy and gradient direction consistency, and regional self-adaptive enhancement is carried out; establishing a feature transfer sequence and progressively fusing features; generating and correcting a defect area probability distribution diagram; and constructing a dynamic decision matrix to calculate a comprehensive score for defect grading. According to the method, the defect detection accuracy under a complex background can be improved, false detection and missing detection are reduced, and real-time defect positioning and accurate classification are realized.

The invention belongs to the technical field of rail transit, and discloses a rail transit environment foreign matter intrusion detection method and system based on a visual model. The method comprises the following steps: firstly, extracting a suspicious region sub-graph in an original image through preprocessing, and respectively extracting a coarse-grained global feature vector and a fine-grained local feature vector by using an image encoder; meanwhile, text prompt information matched with the image content is generated, and a corresponding text feature vector is extracted; the multi-level similarity of the image feature vector and the text feature vector is calculated, and the respective weight fusion is combined to obtain a comprehensive abnormal score; and finally, dynamically modeling abnormal score distribution based on a Gaussian mixture model to realize foreign matter invasion judgment. According to the method, image and text multi-modal information is fused, the detection precision of foreign matters in a complex scene is improved through multi-scale feature association, the dynamic environment adaptability is enhanced by adopting an adaptive threshold strategy, and the problems that a traditional method is high in false detection rate and insufficient in generalization ability in a complex background of a railway are effectively solved.

The invention discloses a light guide plate defect detection method and system based on a neural network, and particularly relates to the technical field of machine vision detection, and the method comprises the following steps: aiming at the problem of image instability of a light guide plate in a dynamic transmission or rotation process, continuously collecting an image sequence and extracting time domain features; and performing interference judgment in combination with the inter-frame consistency prediction coefficient and a first threshold to realize accurate identification of the abnormal image frame. For an abnormal image frame, further correcting the recognition credibility of the abnormal image frame by adopting a confidence adjustment and fusion mode, and meanwhile, introducing a frequency domain transformation and image enhancement strategy to compensate detail loss caused by motion blur; according to the method, inter-frame consistency analysis, confidence fusion regulation and control and frequency domain fuzzy recognition and compensation mechanisms are introduced, abnormal judgment and image quality restoration of the light guide plate image in the dynamic scene are realized, the recognition accuracy and stability of the neural network model on the defect type, position and confidence are improved, and the false detection and omission ratio is effectively reduced.

The invention relates to the technical field of electrical digital data processing, in particular to an anomaly detection and processing method and system based on deep learning. The method comprises the following steps: firstly, performing feature extraction and fusion on a log text sequence, a log timestamp sequence and a running state index sequence set to obtain an initial feature vector sequence; then, constructing a hierarchical graph neural network to perform feature updating, propagation and fusion on the initial feature vector sequence to obtain a multi-level fusion feature vector sequence, and calculating a standardized mode vector sequence in combination with a log text sequence and a running state index sequence set; obtaining a self-adaptive abnormal threshold value according to the standardized mode vector sequence and detecting candidate abnormal logs; and finally, verifying the candidate abnormal logs, determining verified abnormal logs, classifying the verified abnormal logs, generating a processing suggestion text according to a classification result, and carrying out early warning prompt. The false detection rate can be reduced, and the log anomaly detection accuracy is improved.

The invention discloses a method and a system for rapidly screening peanut endophytic mildew, particularly relates to the technical field of nondestructive testing of agricultural products, and is used for solving the problems of false detection and missing detection caused by low contrast and gradient boundary in an initial mildew stage and an internal diffusion sample in an existing detection method. Based on a hyperspectral imaging technology, a filtering kernel size is dynamically adjusted through conjoint analysis of frequency domain energy distribution and spatial local variance, and detail features of a gradient boundary in an image are enhanced; verifying and screening high-confidence candidate regions by adopting spectrum and spatial feature consistency, analyzing and extracting principal component features representing a mildew diffusion trend in combination with a texture direction, and dynamically splicing the principal component features with a spectrum gradient direction to construct a fusion feature vector; quantizing boundary confidence through a Bayesian probability classification model, combining with a neighborhood similarity constraint region growing algorithm to generate a mildewed mark graph with continuous space and consistent features, and introducing a dynamic threshold correction mechanism to adaptively optimize a segmentation standard; and the detection precision and the anti-interference capability of the fuzzy boundary are improved.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses a speech recognition error correction method, which comprises the following steps of: training and generating a target field Chinese character text understanding model by collecting dialogue Chinese character texts of a target field; collecting pinyin data, and training to generate a pinyin understanding model; receiving to-be-detected voice data and converting the to-be-detected voice data into to-be-detected Chinese character texts and pinyin texts Inputting the Chinese character text and the pinyin text to corresponding understanding models to generate semantic features and pinyin features; combining the semantic features and the pinyin features to form joint features; inputting the joint features into a speech recognition error detection model, generating error probability distribution of each word, and outputting an error tag; and correcting the Chinese character text through a template error correction module based on the error tag. According to the method, more comprehensive feature representation is formed by combining semantic features and pinyin features, and the accuracy of speech recognition error detection is improved; and a template error correction module is utilized to effectively correct identification errors.

The invention relates to the technical field of artificial intelligence, and discloses an AI-based composite insulator internal defect ultrasonic detection method, which comprises a multi-mode ultrasonic probe array module, a signal preprocessing module, an AI defect analysis module, a dynamic parameter optimization module, an edge calculation module and a visual report module, the method comprises the following steps: acquiring a full-dimensional signal through a multi-modal ultrasonic probe array, and inputting the full-dimensional signal into a deep space-time convolutional neural network for defect recognition after adaptive noise reduction and feature fusion; the detection precision is improved by combining dynamic waveform matching and multi-physics coupling analysis; model lightweight and real-time processing are realized by adopting transfer learning and edge calculation. The system integrates the functions of parameter adaptive optimization, three-dimensional visualization and Internet of Things cooperation, solves the problems of low efficiency and high false detection rate of a traditional detection method, and improves the intelligent level and engineering applicability of composite insulator defect detection.

The invention relates to the technical field of semiconductor manufacturing, in particular to a semiconductor defect detection method and system based on deep learning. The method comprises the following steps: step 1, multiband cooperative imaging and dynamic scanning control; 2, performing multi-scale feature fusion and defect identification; 3, dynamic threshold judgment and multi-scale feature fusion network optimization: dynamically adjusting a judgment threshold according to probability distribution characteristics of a current batch defect thermodynamic diagram, applying an offset to the threshold in combination with a wafer process type, and determining the offset according to a balance relationship between a false drop rate and an omission rate in historical data; critical samples and misjudgment samples with classification confidence close to a threshold value in historical detection are periodically screened, incremental learning is performed on the last layer of the multi-scale feature fusion network, and upstream network weight is frozen to prevent feature drift. Through fine image processing, multi-band information fusion, dynamic threshold adjustment, incremental learning and other mechanisms, the defect detection efficiency and accuracy in the semiconductor manufacturing process can be effectively improved, and the detection performance is continuously optimized.

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 discloses a deformation target detection method based on an adaptive feature extraction network and an attention mechanism, and belongs to the technical field of image recognition. The method comprises the following steps: constructing a high-quality electric car elevator entering data set; a tramcar entering detection model is constructed; training a tramcar ladder entering detection model; and inputting a to-be-identified image into the trained model to carry out tramcar entering detection, and verifying a detection result. The problems of missing detection, false detection and the like due to feature deformation of a detection target when a monitoring view angle is fixed, a space is narrow and a large number of people exist in an existing detection model based on deep learning are solved. Based on the above problems, a tramcar entering data set is constructed, and a tramcar entering detection model is trained. According to the model, a residual structure in a backbone network C3 module is fused with deformable convolution, a SimAM attention mechanism is introduced into a backbone network, a weighted bidirectional feature pyramid network is introduced into a neck network, and the representation capability of detail features during target deformation is enhanced, so that the accuracy of target detection is improved.

The invention relates to the technical field of engine cylinder surface defect detection, and discloses an engine cylinder surface defect detection method and system. The method comprises the following steps: acquiring three-dimensional height information of cylinder surface defect points based on a laser displacement sensor, identifying surface defects by combining an edge detection algorithm with a standard template library, calculating geometrical characteristics of the defects by fitting a polynomial and a least square method, and finally generating a defect report and feeding back the defect report to a central control system. Compared with a detection method in the prior art that fine defects on a complex curved surface are difficult to accurately identify, and especially in a data environment with large noise, accurate detection of micron-sized defects cannot be realized, the detection method has the advantages that noise is filtered through a high-precision laser displacement sensor and Fourier transform, so that the detection accuracy is improved, and the detection efficiency is improved. Precise detection and identification of surface fine defects are realized, so that the problems of missing detection and false detection in the prior art are avoided, and the detection precision and the production efficiency are improved.

The invention provides a data flow monitoring method and system based on a large model, and the method comprises the steps: obtaining a data flow record set generated by a to-be-monitored system in a continuous operation period, carrying out the correlation path construction of the data flow record set, generating a data flow topological graph containing a node interaction relation and a time sequence dependency relation, and carrying out the correlation path construction of the data flow record set; calling a pre-trained circulation behavior analysis large model to perform node sequence pattern recognition on the data circulation topological graph, and generating behavior abnormal confidence and abnormal pattern labels of each node in the data circulation topological graph; and according to the abnormal behavior confidence and the abnormal mode label, screening an abnormal interaction node cluster in the data flow topological graph. According to the method, relevance between abnormal nodes and time sequence relevance are considered, missing detection or false detection is avoided, and the reliability of the monitoring effect is improved.

The invention relates to the field of insulator nondestructive testing, and discloses an insulator product surface defect nondestructive testing method based on AI identification, and the method comprises a data acquisition module, a preprocessing module, an AI analysis module, a decision output module, a self-optimization module, and an edge calculation node. Through multi-modal data fusion and a deep convolutional neural network technology, accurate detection of surface defects such as cracks, dirt and damage is realized, the omission ratio and the false detection rate are reduced, and the detection precision is improved compared with the traditional manual inspection efficiency; visible light, infrared thermal imaging, ultrasonic waves and hyperspectral data are combined, the surface and internal defects of the insulator are comprehensively covered, the detection rate of tiny cracks and hidden dirt is increased, and the technical limitation of a single sensor is broken through.

The invention discloses a crop adaptability detection method based on an HSV color segmentation algorithm. The method comprises the steps that S1, two TOF cameras are used for shooting the same crop area; s2, through fusion of a solid-state laser radar and cameras, synchronously obtaining three-dimensional information and color information of crops, enabling a laser radar point cloud to have RGB color information, carrying out HSV color space conversion on an image captured by each TOF camera, and carrying out color segmentation by using a preset threshold value to obtain a region of interest of a crop region; s3, fusing the color information and the depth information acquired by the two TOF cameras based on a double-shooting depth perception technology; s4, generating an adaptability detection result according to the fused information; according to the crop adaptability detection method based on the HSV color segmentation algorithm, the identifiability of crops in the image is improved, false detection caused by external environment changes is reduced, and effective detection and tracking of crop rows in a complex environment are realized.