12295 results about "Image quality" patented technology

A pattern which does not appear at a flat portion in normal binarization processing is set as a code pattern, and a code formed from this pattern is attached. At this time, code attachment with little degradation in image quality is implemented by selecting an unnoticeable pattern.

The invention discloses an image enhancement method and system in a complex coal mine environment, and relates to the technical field of image processing, and the method comprises the steps: carrying out the preprocessing of a collected coal mine image of a target region, and dividing the coal mine image into different semantic regions, including a bright region, a dark region and a dust shielding region, through a deep learning semantic segmentation model; according to semantic region characteristics, a differentiation enhancement strategy is made; a traditional Retinex model is improved, non-local mean filtering is introduced, and an illumination component and a reflection component are decomposed through pixel similarity matching. According to the method, the image is divided into the bright area, the dark area and the dust shielding area through the deep learning semantic segmentation model, differential enhancement strategies are formulated according to different area characteristics, detail distortion caused by global adjustment is avoided, local contrast suppression is adopted in the bright area, illumination compensation is enhanced in the dark area, and the image quality is improved. Noise diffusion of the dust shielding area is inhibited through edge preservation smoothing, the image quality of each area is remarkably improved, and it is ensured that image details in a complex coal mine environment are clear and visible.

The invention is suitable for the technical field of video acquisition, and particularly relates to a video stream acquisition method based on deep learning, and the method comprises the steps: carrying out the video acquisition through a self-adaptive strategy, and obtaining real-time collected video data; performing parameter adjustment through edge calculation to obtain preprocessed video data; performing key frame selection on the preprocessed video data to obtain key frame data; performing inter-frame difference compression on the key frame data through the optimized variational auto-encoder model, and outputting video frame compression data; and when the original video needs to be recovered, analyzing the video compression data, reconstructing through the pre-trained neural network model to obtain a reconstructed video sequence, and generating a complete video according to the reconstructed video sequence. According to the method, the non-key frame is directly generated in the decoding process by using the deep learning model, so that the compression ratio can be remarkably improved, and the video playing continuity and the image quality, especially the performance in high-dynamic and complex scenes, can be improved.

The invention discloses an MRI medical image correction method and system based on a convolutional neural network and a computer readable storage medium, and the method comprises the steps: obtaining a brain MRI medical image, and carrying out the preprocessing; extracting lesion features of the brain lacuna infarction from the preprocessed MRI medical image through a convolutional neural network, and positioning a lesion area of the brain lacuna infarction; performing detail enhancement on the lesion area of the cerebral lacuna infarction, segmenting the lesion area and optimizing a segmentation result; and carrying out artifact restoration on the segmented image, and generating a three-dimensional focus visualization model based on the restored image. According to the method, through processing in multiple aspects such as quality evaluation, lesion recognition, segmentation, artifact repair and three-dimensional visualization of the MRI image, the lesion area can be recognized more efficiently and accurately, the image quality is improved, and an efficient and accurate brain lacuna infarction diagnosis auxiliary tool is provided.

Systems and methods are disclosed for classifying objects using electro-optical and synthetic aperture radar images through multi-modal feature alignment and fusion. A computing system acquires and preprocesses image data, then aligns features across modalities using a multi-modal alignment engine. A cross-modal attention fusion network extracts and integrates complementary information using transformer-based attention mechanisms. A modality-specific feature extraction framework processes EO and SAR images through specialized branches, ensuring optimal feature representation. An adaptive fusion decision system dynamically determines the best fusion strategy based on image quality and confidence scores. A self-supervised consistency controller enforces alignment between EO and SAR features using contrastive learning. The fused representations are processed by a neural network to generate object classifications. This system improves accuracy and robustness in environments where one modality may be degraded or missing, enhancing applications such as remote sensing, surveillance, and autonomous navigation.

The invention provides a self-adaptive brightness adjustment method for an LED backlight source, and the method comprises the steps: obtaining the intensity and spectral distribution information of downward natural light and an indoor lamp light source in real time through a light sensor, carrying out the light source superposition, calculating the total light intensity and spectral distribution, judging whether the self-adaptive adjustment condition is met or not, and if yes, adjusting the brightness of the LED backlight source. If yes, triggering an LED backlight brightness adjusting process; detecting the actual brightness of each to-be-adjusted unit in real time, comparing the actual brightness with the optimized target brightness value, calculating a brightness error, introducing a machine learning algorithm to learn and predict error data in real time, and dynamically adjusting the PWM duty ratio of the to-be-adjusted unit; and continuously obtaining evaluation feedback data of the user on the display image quality, and performing clustering analysis on the evaluation feedback data through an optimized unsupervised learning algorithm to generate a brightness adjustment curve adapted to different user preferences.

The invention discloses a manipulator grabbing method based on deep learning target detection and image segmentation, and relates to the technical field of artificial intelligence and robotics.The manipulator grabbing method comprises the following steps that a scene image to be processed is collected, the image quality is improved through the multi-light-source fusion image enhancement technology, and recognition errors caused by uneven illumination are reduced; and inputting the enhanced image to a pre-trained deep learning model, executing a target detection task, and outputting an initial bounding box and a category label of the target object. According to the method, through multi-light-source image enhancement and high-precision image segmentation, the accuracy of target recognition and contour extraction is remarkably improved, and the capture failure rate caused by image misjudgment is reduced. And meanwhile, geometric consistency verification and a multi-factor grabbing scoring mechanism are introduced, dynamic screening and collision pre-detection are conducted on the paths, the grabbing stability and safety of the mechanical arm in the complex environment are effectively guaranteed, and the intelligence and robustness of the whole system are remarkably improved.

Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. A novel correlation network composed of convolutional layers for feature extraction that extract multi-dimensional features from the image and a channel-wise transformer with attention to capture complex inter-channel dependencies. An angle optimizer enhances compressibility of an image and an error resilience subsystem improves robustness against transmission errors and data loss. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission. The correlation network incorporates error correction and concealment techniques during decoding. The model's outputs enable effective image reconstruction, achieving advanced compression while preserving information for accurate analysis.

The invention discloses an intelligent optimization method for ultrasonic image quality enhancement. The intelligent optimization method comprises the following steps: acquiring an original ultrasonic image through ultrasonic imaging equipment; performing multi-stage preprocessing on the obtained original ultrasonic image; extracting multi-scale features from the ultrasonic image subjected to the multi-stage preprocessing; performing intelligent scoring on the images subjected to multi-stage preprocessing and multi-scale feature extraction; dynamically optimizing the processing parameters under the guidance of the image quality score; identifying and emphatically enhancing a target area in the ultrasonic image; fusing a local optimization result with the global image; and outputting the optimized ultrasonic image and the corresponding quality scoring report. Compared with the prior art, the ultrasonic image quality enhancement method has the following advantages and effects that the ultrasonic image quality enhancement method provided by the invention has remarkable innovativeness in technology, solves the ultrasonic image quality problem existing in the prior art, provides a more efficient and accurate auxiliary tool for clinical diagnosis, and has wide application prospect and practical value.

The invention discloses an intelligent quality detection system based on machine vision, relates to the technical field of data processing, and solves the problem that light source parameters are difficult to automatically adjust by combining and utilizing a control algorithm according to environmental parameter influence coefficients. After multi-scale fusion alignment is difficult to carry out on image data, optimized image information is obtained by adopting a Q-learning algorithm and combining image quality scores; defect identification detection and three-dimensional deformation detection are difficult to carry out; and the quality abnormal coefficient is difficult to analyze and control feedback and visualization are difficult to carry out. Through adaptive light source control and image optimization, in combination with defect detection and three-dimensional deformation evaluation, efficient and accurate quality detection is realized, and through real-time feedback and visualization, the defect and deformation state of an object can be conveniently and rapidly known.

The invention discloses a human face image quality assessment method and system and a computer readable storage medium, and relates to the technical field of human face image quality assessment, and the method specifically comprises the following steps: uniformly dividing a human face image captured by a camera in real time into a plurality of regions through a face key point detection method under the condition that there is an artifact effect; acquiring artifact quality evaluation information of each region of the face image in real time, evaluating the quality influence degree of artifacts on each region, and dividing each region into a high influence region, a middle influence region and a low influence region according to an evaluation result; and according to the division result of each region of the face image, constructing a dynamic intervention mechanism, and respectively performing corresponding intervention measures on the high-influence region, the medium-influence region and the low-influence region. According to the method, the problem of human face image quality evaluation misjudgment caused by artifact interference in a dynamic light source environment is solved, and accurate evaluation and efficient dynamic optimization of the image quality are realized.

The invention provides an underwater image enhancement method combining high-frequency enhancement, space-frequency mixed feature extraction and a Mama enhancement fusion technology. The method aims at solving the problems of low contrast ratio, color distortion, detail loss and the like caused by absorption and scattering effects in the light propagation process of the underwater image, the image quality is improved, and the visual perception effect is improved. The method specifically comprises the following four main steps: firstly, acquiring and preprocessing initial data sets of different underwater scenes; secondly, extracting texture detail information of the image through a texture enhancement module; thirdly, further enhancing texture details by using a space-frequency mixed feature extraction module; and finally, in combination with the original image, the texture information and the spatial frequency domain enhancement result, realizing feature fusion of the image through a Mama enhancement fusion module, thereby effectively recovering details of the underwater image, correcting color deviation and improving the definition and visual effect of the image. According to the method, the enhancement performance of the underwater image in a complex environment can be improved, and powerful support is provided for applications such as underwater detection, target detection, object recognition and image segmentation.

The invention provides a remote sensing image super-resolution method based on a diffusion model and a multi-modal large language model, and relates to the technical field of image processing. According to the method, a pre-trained stable diffusion model is adopted, the model is trained on a large-scale data set, the stable and detailed super-resolution effect can be achieved, and the convergence speed is higher. According to the method, a multi-mode large language model is used for enhancing the semantic comprehension ability of the model, and detailed content description and texture description are provided. The invention also introduces an additional classifier which provides global information and a simple signal about the LR. The prior information is adaptively integrated into a diffusion model through a cross attention module. And finally, through combination of the strong generation capability of the stable diffusion model and the semantic understanding capability of the MLLM, the method provided by the invention is excellent in the aspect of remote sensing image reconstruction, and excellent performance is realized in the aspect of quality of a plurality of images on different data sets.

Embodiments of the present invention provide a server, a display device, and a digital human processing method. The method comprises: receiving image data; inputting the image data into a shared encoder to obtain a first depth encoding feature; inputting the first depth encoding feature into a segmentation decoder to obtain a segmentation mask; inputting the segmentation mask and the depth encoding feature into a guided flow architecture module to obtain a refined feature; inputting the depth encoding feature and the refined feature into an image matting decoder to obtain a face image having undergone image matting; performing image quality enhancement processing on said face image to obtain a face enhanced image; determining face keypoint data; and generating standard data. According to the embodiments of the present invention, a segmentation mask is obtained by means of a segmentation decoder, and the segmentation mask provides, for an image matting decoder, semantic information obtained by means of segmentation, such that the accuracy of an image matting result is higher, the portrait edge segmentation is cleaner, the quality and precision of standard data are improved, and human assistance information is not needed.

The invention discloses a face image enhancement and recognition method in a low-light environment, and relates to the technical field of face recognition. Firstly, multi-scale Retinex enhancement is carried out on a low-illumination face image, brightness and details are improved, then LBP and HOG features of the enhanced image are extracted, weight fusion is dynamically adjusted according to illumination, then space and channel weighted optimization is carried out on the fused features by using a residual attention module, finally, optimized features are input into a pre-training model to extract vectors, and finally, the pre-training model is used for pre-training. And through cosine similarity matching identification, face processing and identity determination under low illumination are completed. According to the method, the problem of low-illumination face recognition is solved, the image quality is improved through multi-scale enhancement, feature extraction is enhanced through dynamic fusion and an attention mechanism, and the recognition accuracy and stability are improved; in addition, living body detection is added to guarantee safety, and the method is suitable for multiple scenes such as security and protection, finance and the like.

The invention relates to the technical field of light source configuration, and discloses a semiconductor wafer defect detection light source configuration method, device and equipment, and the method comprises the steps: obtaining a semiconductor wafer image, and carrying out the dynamic image convolution processing and defect feature enhancement to obtain an enhanced defect feature image; inputting the enhanced defect feature map into a two-way deep neural network for reflected light intensity calculation and image quality scoring to obtain reflected light intensity data and image quality scoring data of the defect region; acquiring light source input current and light intensity sensor data of a plurality of light sources, and performing identification calculation on the light source angle parameters in combination with the image quality scoring data to obtain light source initial parameters; performing nonlinear control based on the reflected light intensity data and the light source initial parameter to obtain a light source control parameter; according to the method, the accuracy and the reliability of defect detection are improved, and the manual debugging cost of light source parameter configuration is reduced.

The invention discloses a rapid three-dimensional reconstruction method for an unmanned aerial vehicle mine inspection scene, and particularly relates to the technical field of image processing and three-dimensional modeling, and the method comprises four steps: environment perception collection, inclination angle adaptive path planning, image quality optimization and three-dimensional model construction. Sensing information such as illumination, wind speed and gradient through a sensor to dynamically adjust a shooting strategy, and generating a normal supplementary shooting path in a gradient sudden change area; image quality is improved through image enhancement and feature extraction, and a confidence scoring model is constructed to screen high-quality images to participate in modeling; carrying out confidence evaluation on a modeling result, triggering a supplementary shooting mechanism, and improving the precision and integrity of the model; according to the method, the feature extraction stability of the image under the condition of illumination dramatic change is improved, the image coverage integrity of a high and steep slope area is enhanced, and stable control of image acquisition and fusion in a wind disturbance environment is realized, so that the precision, continuity and robustness of three-dimensional modeling are effectively improved.

The invention relates to the technical field of medical treatment, in particular to an AI-based medical image automatic segmentation and labeling system, which comprises an image data acquisition module, a data preprocessing module, a feature extraction module, an automatic segmentation module and an intelligent labeling module, the data preprocessing module improves image quality through noise suppression, artifact removal and modal alignment, the feature extraction module extracts a feature map of a potential lesion area, and the automatic segmentation module combines a full convolutional network, a bidirectional long-short-term memory network and an attention mechanism to realize accurate segmentation of an organ area and the lesion area. The intelligent labeling module is used for labeling anatomical names, lesion types and pathological features of the segmentation results by using a medical knowledge graph and a semantic embedding technology; the method has the advantages of being high in segmentation precision, comprehensive in marking and high in expansibility, and the automation and intelligence level of medical image analysis can be improved.

The invention relates to the technical field of disease and pest identification and control, in particular to a disease and pest identification and control early warning system and method, and the system comprises a data collection module, a polarization data analysis module, an image super-resolution processing module, a feature extraction module, a disease prediction module and a decision support module. According to the method, early recognition and accurate prediction of plant diseases and insect pests are achieved by integrating capture and analysis of spectrum polarization data, image super-resolution processing and time sequence analysis, fine structural changes of plant leaves are accurately captured through the polarized light technology, fine lesion features can be revealed by deeply analyzing the polarization data, and the accuracy of the plant diseases and insect pests is improved. The local spectrum enhancement technology further refines the image quality, it is ensured that the change of the cellular level can be accurately recognized, deep learning analysis is carried out in combination with environmental data, the disease development trend can be effectively predicted, and the processing speed and accuracy are improved.

The invention provides a multi-angle photographing and three-dimensional reconstruction method and system for soft tissue, and relates to the technical field of soft tissue three-dimensional reconstruction, and the method comprises the steps: analyzing soft tissue features based on deep learning, generating a bulb tube motion track and sampling interval data, and controlling a mechanical arm to drive a bulb tube to collect projection images at multiple angles; screening an optimal image sequence by using an image quality evaluation network, and performing preprocessing such as material decomposition, feature extraction, space registration and image optimization; and inputting the optimized image and the material features into a reconstruction model, performing iterative calculation in combination with an anatomical structure template to generate density distribution data, performing curved surface reconstruction and intelligent tissue segmentation based on graph convolution based on the density distribution data, and finally generating a three-dimensional model containing tissue layering information.

The invention relates to the technical field of image data processing, in particular to a cable surface defect detection method and system based on machine vision. The method comprises the following steps: acquiring a surface image of a cable, and converting the surface image into a grayscale image; determining the local complexity of each pixel point; obtaining a plurality of areas of the grayscale image, performing complexity determination, and dividing each area to obtain a plurality of windows of each area; determining a contrast limit threshold value of each window; performing image enhancement by using a CLAHE algorithm to obtain an enhanced grayscale image; and carrying out cable surface defect detection on the enhanced grayscale image by using a defect detection algorithm. According to the method, the CLAHE parameters are adaptively adjusted based on local complexity, the window size and the contrast threshold are dynamically determined in combination with gray and gradient information, discontinuity is corrected and eliminated through boundary similarity, and the quality and reliability of a cable defect detection image are improved.

The invention discloses a cancer image denoising method and system based on a multi-scale neural network, and the method comprises the steps: carrying out the adaptive segmentation of a cancer image, carrying out the noise estimation and statistical analysis of each segmented region, and automatically adjusting the denoising parameters; performing multi-scale feature extraction by adopting wavelet transform and pyramid decomposition, and analyzing and processing the image after parameter adjustment; performing quality evaluation and distortion detection on the image after multi-scale processing, automatically identifying and positioning artifacts and distortion, and correcting and compensating interference by adopting a point spread function inversion and Wiener filtering method to obtain a corrected image; and after histogram equalization and contrast-limited adaptive histogram equalization are applied to the corrected image, denoising processing is carried out by using a convolutional neural network in combination with sparse representation and a dictionary learning algorithm, and a denoising result is obtained. According to the method, noise in different types of cancer images can be effectively removed, meanwhile, key diagnosis information is reserved, and the image quality and definition are improved.

The invention relates to the field of image processing, in particular to a seamless steel tube defect detection method based on image processing, which comprises the following steps: acquiring an image of a sized steel tube through a high-resolution industrial camera, and preprocessing to enhance the image quality; designing an adaptive filter based on image gradient direction and spatial frequency characteristics, and enhancing defect characteristics; extracting a defect region by adopting adaptive threshold segmentation and morphological operation, and constructing a comprehensive feature vector through a Hu moment and a gray-level co-occurrence matrix; and finally, defect identification is realized by using a support vector machine classifier, and technical support is provided for steel pipe production quality control. According to the method, the adaptive filter is constructed based on the gradient direction and the spatial frequency, and efficient detection and classification of seamless steel pipe surface defects are realized by combining the comprehensive feature vector and the support vector machine classifier.

The invention discloses a low-dose CT reconstruction method and system based on global optimization iteration deep learning, and the method comprises the steps: collecting CT cross section data of a normal dose, and generating low-dose CT data; the method comprises the following steps: establishing a dual-domain analysis compression iteration model, constructing maximum posteriori estimation based on a CT projection chordal graph data composite Poisson noise generation mechanism, subdividing CT projection data into a projection domain and a chordal graph domain, sequentially updating the projection data, the chordal graph data and image data, and constructing an iteration algorithm to realize CT global optimization modeling. Expanding an iterative algorithm into a trained reconstruction network; inputting the paired data into the expanded network for training, and storing a model with the minimum output result loss; a sub-network and an attention mechanism are added for CT data features, jump connection weights are finely adjusted, middle layer neural network parameters are finely adjusted and updated through middle supervision, and a global optimization iteration deep learning model NGACI-Net is obtained. According to the method, the problems that the low-dose CT image quality is poor, the reconstruction efficiency is low, and generalization and interpretability are lacked are solved.

The invention relates to the technical field of camera adjustment, in particular to a camera adaptive adjustment method and system and a storage medium. The method comprises the following steps: acquiring a multi-source environment sensing data set and an initial environment image; performing environment feature recognition on the initial environment image according to the multi-source environment sensing data set to obtain a scene environment feature map; performing semantic scene segmentation on the scene environment feature map to obtain a semantic scene macroscopic feature map; performing time sequence attention target tracking on the scene environment feature map to obtain a target vehicle motion feature map; performing feature constraint fusion on the semantic scene macroscopic feature map and the target vehicle motion feature map to obtain a target vehicle-scene motion semantic map; and performing parameter adjustment on the camera according to the target vehicle-scene motion semantic map to obtain a camera parameter adjustment strategy set. According to the invention, the performance and the image quality of the camera in a changeable environment are improved.

The invention discloses a cigarette packet appearance defect detection method and system based on deep learning, and relates to the technical field of appearance detection.When the system runs, multi-angle and multi-light-source image acquisition is carried out through a high-definition camera and scanning equipment, it is ensured that appearance details of cigarette packets are comprehensively and accurately captured, and the detection accuracy of the appearance defects of the cigarette packets is improved. According to the method, firstly, the image quality and consistency are further improved through image cutting, graying processing, denoising processing, image enhancement and data standardization of the data preprocessing module, a solid foundation is laid for subsequent defect detection, secondly, the defect detection module carries out feature extraction through strong and weak data enhancement and a feature extractor, and the defect detection accuracy is improved. By calculating the similarity of positive and negative samples in a feature space and a total loss function # imgabs0 #, defect identification and optimization are performed in combination with MSE loss, the defect detection precision and robustness of the model are improved, the evaluation module evaluates appearance defects in real time by matching with a preset threshold, and a continuously optimized detection closed loop is formed.

The invention provides a multi-layer optical film detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining a polarization phase difference signal of transmission light and reflected light through multi-angle incidence irradiation of a multi-band programmable laser source, and recording the polarization ellipticity parameter change; then combining confocal microscopic interference and polarization analysis to determine a space coordinate and a stress distribution diagram of a stress abnormal region; continuously irradiating in a photo-thermal coupling environment through a broadband light source, and monitoring interference signals, polarization states and temperature changes to obtain dynamic evolution characteristics; and finally, performing a simulation test on the multi-layer optical film in a system environment with preset optical axis arrangement and light beam energy distribution, obtaining an in-situ polarization state and interference pattern data, and evaluating the optical performance of the film layer. According to the invention, potential defects can be positioned under high resolution, and the reliability and imaging quality of the film layer under complex application conditions can be accurately evaluated.

Provided are a quantification method for microscopic cracks inside 3D printed concrete and a system thereof. Before measuring the microscopic crack images according to relevant standards, firstly, a denoising network, improved attention-guided denoising neural network (IADNet) is adopted. IADNet can extract features from microscopic crack images from different perspectives, perceive noise from multiple levels, and perform denoising processing, greatly improving image quality and enhancing texture details, which is beneficial for training segmentation networks. The combination of IADNet and semantic segmentation algorithm has the ability to finely recognize image information, quantify microscopic crack recognition, overcome the shortcomings of measurement and analysis of microscopic cracks inside 3D printed concrete, and improve construction efficiency and quality.

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 discloses a VR-based unmanned aerial vehicle inspection method and system, a storage medium and a program product, and the method comprises the steps: calculating a low-polygon terrain model, and carrying out the subdivision smoothing processing, and obtaining a three-dimensional terrain image; matching and registering the panoramic image sequence and the three-dimensional topographic image to obtain a point cloud map; generating a virtual mining area environment map; calculating image quality parameters; when the image quality parameter is lower than a preset threshold value, generating a semi-real and semi-virtual fusion display picture; transmitting the semi-real and semi-virtual fusion display picture to a VR helmet for display, and synchronizing the attitude information of the unmanned aerial vehicle to the VR helmet; when it is determined that a fatigue feature appears according to the brain wave data, the unmanned aerial vehicle is controlled to be switched to a preset autonomous flight mode; constructing a virtual anti-collision boundary; and obtaining an optimized flight path according to the virtual anti-collision boundary, and controlling the unmanned aerial vehicle to inspect according to the optimized flight path. The continuity and reliability of the inspection task are improved, and the safety risk of the inspection task is reduced.