962 results about "Image splicing" patented technology

The invention provides a building construction monitoring method, which comprises the steps of obtaining multi-view video image data of a construction site, and performing fusion processing on the multi-view video image data by adopting an image splicing algorithm to obtain a panoramic three-dimensional scene model of the construction site; acquiring an image frame in the panoramic three-dimensional scene model, and analyzing the image frame by adopting a pre-trained work type identification model to obtain position information of operating personnel and equipment of each work type; acquiring position information of the operating personnel of each type of work, and analyzing postures of the operating personnel by adopting a pre-trained human body posture estimation model to obtain action state information of the operating personnel; and acquiring the position information and the action state information of the operating personnel of each type of work, and judging the specific operation state of the operating personnel of each type of work in combination with a pre-established work type operation knowledge base.

The invention relates to an injection molding product defect detection method based on machine vision, which comprises the following steps: collecting material information of a to-be-detected injection molding product in real time, and dynamically matching and adjusting light source parameters according to spectral reflection characteristics of materials to ensure image collection quality; secondly, the collected images are preprocessed, edge features and texture features are extracted, a three-dimensional model is constructed through multi-view image splicing, and three-dimensional defect features are extracted; thirdly, the multi-dimensional features are input into a deep learning model, the defect probability is calculated through feature fusion and forward propagation, and whether the product has defects or not is judged; if the defect exists, further identifying the defect category, and calculating the number and size of the defect; and generating a standardized detection report based on the defect information. According to the method, the image adaptability of products made of different materials is improved through dynamic light source adjustment, the two-dimensional and three-dimensional features are fused, the defect recognition accuracy is improved, and full-process automation from qualitative judgment to quantitative analysis of the defects is achieved.

The invention provides a large-scale satellite three-dimensional image data processing method, medium and system, and belongs to the technical field of satellite three-dimensional image data processing.The method comprises the steps that Gaussian filtering and wavelet noise reduction preprocessing is conducted on an image, and then cooperative computing of a GPU and a CPU is achieved based on a computing task distribution function. Through multi-scale feature extraction and matrix optimization, a stable feature matrix and a variable feature matrix are obtained. A quadtree spatial index is adopted to carry out data partitioning, and SIFT feature extraction and K-means clustering are combined to optimize feature representation. And establishing a stereoscopic image registration model, determining an optimal splicing sequence through a minimum spanning tree algorithm, realizing image splicing by adopting a progressive texture fusion method, and finally generating a high-precision three-dimensional earth surface model. According to the invention, the unification of processing efficiency and precision is realized, and the technical problem that the processing efficiency of large-scale stereoscopic image data is difficult to improve on the premise of ensuring the processing precision in the prior art is solved.

The invention relates to the technical field of visual automatic calibration, and discloses a joint manipulator automatic calibration method and device based on a visual system. The method comprises the following steps: carrying out multi-view image acquisition on a joint manipulator of the five-axis robot to obtain original image data; performing occlusion region extraction on the original image data to obtain a joint occlusion region set; on the basis of the joint occlusion region set, disordered image splicing and mark point feature extraction are carried out on the original image data, and joint feature image data are obtained; constructing an adaptive calibration equation based on the joint feature image data, and solving a target transformation relation between a global visual coordinate system and a local visual coordinate system; multi-joint collaborative calibration and joint chain constraint optimization of the five-axis robot are executed according to the target transformation relation, and joint error correction parameters are generated, high-precision joint error correction parameter calculation is achieved, and the positioning precision and the movement precision of a joint manipulator of the five-axis robot are greatly improved.

The invention discloses a real-time puzzle method based on monocular SLAM (Simultaneous Localization and Mapping) fused with GPS (Global Positioning System), which is characterized in that an unmanned aerial vehicle is utilized to load a camera to shoot an image sequence under the camera and a GPS module is utilized to receive latitude and longitude signals, and high-precision real-time tracking and map construction of an outdoor large scene are realized by optimizing a feature extraction and matching algorithm of monocular ORBSLA3. Generating a sparse point cloud map and a position attitude of the key frame; a GPS module signal is subjected to UTM coordinate conversion, and fusion of the GPS module signal and key frame information is realized through linear search of a difference value; the key frame position attitude is converted to global UTM geographic coordinates through fusion transformation; overlapping region fusion is carried out on key frames with close spatial positions by using a Laplacian Gaussian pyramid to obtain an accurate panorama; according to the method, large-scene image splicing can be realized, the overlapping region splicing effect is good, the splicing real-time performance is good, and the method is suitable for application occasion requirements in multiple fields of outdoor aerial photography, aerial survey and the like.

The invention discloses an offshore wind power blade defect detection method, and belongs to the technical field of wind power blade detection. The detection method comprises the following steps: S01, offshore wind power blade defect detection: identifying the position of a blade defect, outputting a positioning mark frame of the defect on an original image, and providing the coordinate of the center position of the mark frame and the corresponding length and width; s02, leaf image splicing: carrying out step-by-step shooting; splicing each detected image by means of an image splicing technology to form a complete fan blade image; and S03, determining the actual physical position of the blade defect in the single image: in the spliced total image, determining the actual physical coordinate of the blade defect in the original single image corresponding to the fan blade. According to the detection method, multiple detection means such as computer vision, feature extraction, image splicing and edge detection technologies are fused, blade surface defect information can be comprehensively obtained, and the detection precision and accuracy are greatly improved.

The invention discloses an unsupervised deep learning image splicing method for portable fundus color photo equipment. The method comprises the following steps: constructing a multi-scale feature extraction module of an image registration network; the feature matching module is used for constructing an image registration network; the multi-scale feature extraction module and the feature matching module are sequentially connected to form the constructed image registration network; constructing a shallow feature extractor; constructing a feature reconstruction module; constructing a joint generator; sequentially connecting the shallow feature extractor, the feature reconstruction module and the seam generator to form a fusion network; constructing a fusion network; connecting the image registration network and the image fusion network to form an image splicing model; collecting data; preprocessing the data; obtaining a trained image registration network; obtaining a trained image fusion network; forming a fundus image splicing model by using the trained image registration network and the trained image fusion network; and splicing the images by using the eye fundus image splicing model. According to the method, a high-quality fundus spliced image can be efficiently generated.

The invention discloses a large-breadth cross-scale adhesive film defect detection method and a large-breadth cross-scale adhesive film defect detection device. The method comprises the following steps: carrying out coarse focusing on a camera and capturing a glue film image, carrying out coarse scanning on the glue film image and extracting information of a printed image-text in the glue film image by using an image difference method, inputting an output difference image into an anchor frame algorithm, inputting an output fine scanning target area coordinate into a genetic algorithm, and carrying out fine scanning on the glue film image. Outputting an optimized fine scanning path in combination with S curve planning characteristics of the motor; the motion platform is driven to complete path execution by combining double-axis linkage control with the fine scanning path, camera focusing is completed in the path execution process, and a high-resolution image of a defect area is collected and registered; and carrying out image splicing and fusion on the registered images by combining an optimal suture line algorithm with an improved Laplacian pyramid fusion method, and carrying out defect detection on the fused complete adhesive film image. The method has the advantages of high splicing precision and fast processing efficiency, and can effectively retain more detail information.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an image generation method, device and equipment based on theme information and a medium. The method comprises the steps that input information is analyzed to generate theme information and copywriting information, a cue word set is generated, and a figure image set and a background image set are generated; fitting the segmented figure image with the background image to form a head image candidate set, and selecting a head image matching template frame to generate a basic image; decomposing the copywriting to generate a sub-module initial picture set, and adding a gradual change effect to form a sub-module picture set; the adjusted sub-module pictures are obtained based on size adjustment, and a pre-synthesized image is generated through splicing; and identifying the blank area to draw a title text to obtain a final image. Through theme analysis, template matching, image splicing, modular copywriting processing and blank drawing, poster generation efficiency is improved, layout flexibility is enhanced, and visual unification is realized.

The invention relates to the technical field of image processing, and provides an OCT (Optical Coherence Tomography) image splicing method, which comprises the following steps: acquiring image pairs and matching point pairs corresponding to the image pairs, the image pairs being adjacent OCT images; calculating a homography matrix based on the matching point pairs so as to align the image pairs, and outputting preliminary aligned image pairs; constructing a fusion weight map, wherein the fusion weight map comprises a weight value of a pixel position and a region type; and according to the weight value and the region type, carrying out fusion splicing on the pixel positions so as to output a panoramic OCT image. According to the method, partition fusion is achieved by fusing the weight map, the problems of tissue dislocation and artifacts in OCT splicing are solved, and then the problem that the splicing effect of OCT image splicing is poor is solved.

The invention discloses an unmanned aerial vehicle image splicing method based on local distortion and seam guidance, and relates to the technical field of computer image splicing. The technical problem that an image splicing result generates artifacts due to the fact that parallax exists between input images in image splicing and warping characteristics of non-overlapping areas of the images are not fully considered is solved. According to the method, image warping is guided by using a grid, labels are created among pixels in an overlapping region, and a logic function is designed to optimize the minimization of a label energy function; meanwhile, searching an optimal seam in the overlapping region by adopting a seam cutting method; the method focuses on an unmanned aerial vehicle remote sensing image, and solves artifacts caused by parallax from the image warping characteristic; according to the method, the target image is firstly subjected to grid warping processing, then the optimal splicing seam is found by using the seam cutting method and is fused with the reference image, and finally accurate alignment is realized, so that the quality of the spliced image is effectively improved, and a better scheme is provided for unmanned aerial vehicle image splicing.

The invention provides a water body image splicing method and device based on cooperation of an unmanned aerial vehicle and an unmanned ship, and belongs to the technical field of image processing, and the method comprises the steps: determining a first driving path of the unmanned ship based on a shooting region and a preset image splicing proportion, determining a second driving path and a plurality of shooting center points of the unmanned aerial vehicle based on the first driving path; calculating an image offset based on a plurality of water area images collected by the unmanned aerial vehicle at the plurality of shooting center points; the image offset is the offset between the position of the unmanned ship in the image and a plurality of shooting center points; and splicing the plurality of water area images based on the image offset. According to the water body image splicing method and device based on cooperation of the unmanned aerial vehicle and the unmanned ship provided by the invention, the problems that the images deviate and cannot be spliced when a large-area water area is monitored in the prior art can be solved.

The invention provides an image real-time splicing transition method and device based on a pathological slide and a readable storage medium. The method and the device are innovated and improved for solving the problem that existing slide scanning digital splicing traces affect diagnosis. An overlapping area is reserved when adjacent original images are scanned, a weight transition template is generated by using a specific algorithm according to the distance between pixels and an overlapping boundary, and the transition smoothness is flexibly adjusted by controlling adjustable parameters. Overlapping region pixels and the corresponding templates are multiplied and accumulated to replace original data, overlapping region removing splicing is carried out, cyclic image-by-image processing is carried out, and traceless splicing is achieved. Through the optimization, the image definition and the film reading quality are remarkably improved, the misjudgment risk of doctors is greatly reduced, the pathological image analysis application range is powerfully expanded, clinical diagnosis and research are promoted to accurately and efficiently advance, powerful power is injected for pathology development, and the method has important value and wide application prospects in the technical field of medical images.

The invention relates to the technical field of panoramic monitoring, and discloses an image splicing method and system for a low-computing-power camera module array, and the method comprises the steps: collecting a panoramic image of a target scene through an array formed by a plurality of camera modules, configuring an initial priority weight in combination with a key marking instruction of a monitoring user, and carrying out the image splicing through the initial priority weight. A camera module is divided into a key module and a non-key module, timing dynamic detection is performed on the non-key module, real-time dynamic detection is performed on the key module, the weight is updated when a dynamic block is found, trajectory tracking is performed on the dynamic block by adopting a Kalman filtering algorithm, and the motion trajectory of the dynamic block is predicted. According to the scheme, the monitoring resource distribution of the camera module can be dynamically optimized, the response speed of a key area is improved, the real-time performance and accuracy of image splicing are improved, the monitoring efficiency and precision are effectively improved, and the method and the device have the advantages of being high in practicability and easy to popularize. The problem that in the prior art, a low-computing-power camera module array is difficult to achieve efficient and accurate image splicing is solved.

The invention discloses an image registration method based on improved ORB feature extraction and grid feature matching. The method comprises the following steps: inputting an image to be extracted; using an improved FAST algorithm to extract feature points of the to-be-spliced image; establishing a BRIEF descriptor, and obtaining a BRIEF descriptor with a direction; according to the feature point set of the to-be-spliced image, establishing a Delaunay triangular grid taking the feature points as vertexes; judging the similarity of triangles, and obtaining similar triangles formed by the feature points; matching the similar triangle vertexes by using a Hamming distance to obtain an optimal matching feature point pair; obtaining affine matrix parameters by using an SVD method; according to the image registration method provided by the invention, the image quality is ensured, the feature extraction efficiency and the extraction precision can be improved, wrong matching is reduced, the stability is improved, the real-time performance of video stitching is improved, the waste of memory resources is reduced, the equipment cost is reduced, the working efficiency is greatly improved, and the method is suitable for popularization and application. And a better method is provided for image splicing.

The invention discloses an appearance detection multi-camera image splicing method and system and a related device, which are used for improving the overall quality and accuracy of image splicing in a multi-camera vision field non-overlapping scene. The method comprises the following steps: respectively calibrating a main camera and an auxiliary camera through a calibration board in combination with a weighted error minimization model; determining a pixel plane transformation matrix from the auxiliary camera to the main camera; transforming an initial image shot by the auxiliary camera to a pixel plane of the main camera according to the pixel plane transformation matrix; projecting the vision fields of all the cameras into a world coordinate system, and judging whether the projection positions of the vision fields are overlapped or not; if yes, feature points are extracted from the to-be-spliced image, the feature points are screened through a distance threshold value and a quality threshold value, and a feature point pair set is obtained; and calculating a perspective transformation matrix between the to-be-spliced images based on the feature point pair set, and splicing and fusing the to-be-spliced images according to the perspective transformation matrix and the vision field projection position.

The invention discloses a multi-robot cooperative target identification and tracking method in a dynamic environment. The method comprises the following steps: identifying a target person in real time; if the confidence coefficient of the target person is lower than a threshold value and an effective detection frame exists, a re-identification module is started, and identity confirmation is carried out by utilizing a TCN network in combination with time sequence characteristics and a motion mode; if the target is lost, a multi-view collaborative mechanism is started, a global panoramic perception view is constructed through a multi-view image splicing and distributed target fusion technology, and meanwhile, the problem of tracking interruption caused by target shielding is solved by utilizing the multi-robot view complementary characteristic; if the confidence coefficient of the target person is higher than a threshold value, the target position is obtained in real time, an optimized APF and PID control strategy is adopted to establish a closed-loop system from target recognition to motion control, and a tracking strategy is kept. According to the method, the problems of view shielding, target loss and dynamic interference in a complex environment are effectively solved, and high-precision and high-stability target tracking is realized.

The invention discloses a monitoring method and system for identifying crop drought based on leaf morphology, and relates to the field of agricultural drought monitoring, and the method comprises the following steps: selecting a completely unfolded healthy leaf, cleaning with ultrapure water, blow-drying with nitrogen, and fixing on a standard objective table to obtain a standardized leaf sample; based on the standardized leaf sample, a high-resolution scanner is used for obtaining a front gray image and a back gray image of the leaf, and a binary image with a clear vein network is obtained through image splicing and binarization processing; skeletonizing the binary image with the clear vein network, and calculating the vein fractal dimension by adopting a box counting method to obtain a fractal dimension change rate; the method is in a fixed detection area of a standardized leaf sample. Through innovative leaf vein fractal dimension analysis and contact angle dynamic measurement, a crop drought monitoring system is constructed, and in the aspect of leaf vein analysis, skeletonization processing and a box counting method are adopted to realize accurate quantification of a leaf vein network topological structure.

The invention discloses a wide-angle video monitoring system, method and equipment based on multiple cameras and a medium, and relates to the technical field of video monitoring, the wide-angle video monitoring system comprises a multi-camera acquisition module, a variable-focus high-definition camera multi-angle acquisition module, a high-quality image obtained through distortion correction and an image splicing processing module, an improved algorithm is used for accurately matching feature points and fusing the images, and the high-quality image is obtained. The intelligent analysis module detects and tracks a target and analyzes behaviors in real time by means of an improved model, the storage and transmission module performs cluster storage, advanced coding cost reduction and high-speed link transmission, and the control module performs intelligent regulation and control according to multiple information and can also perform remote management. The system has the advantages of multi-angle acquisition without dead angles, good image splicing, accurate intelligent analysis, efficient storage and transmission, adaptability to severe environments, flexible power supply, guarantee of data safety, improvement of monitoring quality and efficiency, meeting of complex scene requirements, and enhancement of practicability, reliability and safety.

The invention provides an image stitching system and method, electronic equipment and a medium, and the system comprises an image sensor which is configured to operate in a periodic mode and outputs interlaced image data. The data processing device comprises a receiving and analyzing module, a calculation module and a storage module, the receiving and analyzing module is used for receiving each row of interlaced image data output by the image sensor and analyzing and acquiring a row of interlaced images and data information corresponding to each row of interlaced image data; the calculation module is used for calculating a storage address of the current row interlaced image; and the storage module is used for writing each row of interlaced images into a corresponding storage address to complete splicing of a plurality of frames of positive sequence images. A receiving module, an analysis module and a calculation module are arranged in the data processing device, image data are received, analyzed and spliced in real time, a CPU does not need to be additionally occupied, and stability and real-time performance are high; meanwhile, all the collected image data are processed in real time and are not transmitted or transferred, so that the problem of frame loss is avoided.

The invention discloses an unmanned aerial vehicle image seamless rapid splicing method based on multi-view geometry, and particularly relates to the technical field of unmanned aerial vehicle image splicing, and the method comprises the steps: extracting feature points of video frames through a deep learning model, and carrying out the inter-frame matching, and obtaining a matching point set; based on the matching point set, solving the three-dimensional coordinates of the 3D map points, determining the initial pose of the key frame and initializing the global map, matching the feature points of the current frame with the 3D map points in the global map, and solving the camera pose of the current frame; a new key frame is selected according to the feature coverage rate, a local optimization window is constructed for 3D map points jointly observed by the newly added key frame and the associated key frame, error correction is carried out on key frame poses and 3D map point coordinates in the window through local light beam adjustment optimization, and a global map is updated; and based on the optimized global 3D map point fitting reference surface, projecting the key frame image to the fitting reference surface, generating a panoramic image by adopting a multi-scale pyramid fusion method, and outputting a final image spliced image.

The invention relates to a robust splicing method for an inner wall image of a drill hole under a multi-interference imaging condition. The method specifically comprises the following steps: S1, expanding an acquired front-view shot inner wall image of the drill hole into a narrow-band image; s2, registering the expanded narrow-band images of the front frame and the rear frame, and determining the vertical offset and the horizontal offset of the reference image and the image to be spliced by adopting a grid acceleration-based template matching method; s3, determining an overlapping region of the reference image and the to-be-spliced image according to the offset, and fusing pixels in the overlapping region to obtain a spliced result image of the inner wall image of the drill hole; and S4, repeating the steps S1 to S3, splicing frame by frame until well logging is completed, and obtaining a panorama of the borehole inner wall image. According to the method, the image splicing problem caused by poor illumination conditions, loss or inaccuracy of reading of an electronic compass and a depth counter and lens rotation disturbance in actual logging operation can be effectively solved, and a smooth borehole inner wall panorama splicing result can be stably obtained.

The invention provides a wafer detection method based on a line-scan digital camera, and an image splicing method and device. The wafer detection method comprises the following steps: controlling a wafer to be detected to move according to a spiral line track; in the moving process of the to-be-detected wafer, image acquisition is carried out on the to-be-detected wafer by using the line-scan digital camera to obtain a plurality of line images output by the line-scan digital camera. And carrying out image splicing based on a plurality of line images output by the line-scan digital camera, and determining a wafer image. Therefore, the to-be-detected wafer can be detected based on the wafer image. Through the method, the to-be-detected wafer can be controlled to move according to the spiral line track, so that the line-scan digital camera can scan all the to-be-detected wafers through one-time scanning, then the wafer image is obtained through image splicing for detection, the to-be-detected wafer does not need to be moved many times, the wafer detection time can be shortened, and the detection efficiency is improved. And the wafer detection efficiency is improved.

The invention discloses a monitoring image processing system and method based on big data, and relates to the technical field of image processing systems.After N large-size seamless panoramic images are generated through an image splicing module, a background model building module builds a background model of a monitoring area through the N panoramic images based on a Gaussian mixture algorithm; when a moving target appears in the background model, the target detection module extracts the interested moving target from the background image through a moving target detection algorithm, and the target tracking module tracks the moving target in the background model through a particle filter tracking algorithm and records a target walking track; and the abnormity warning module performs abnormity analysis on the behavior of the moving target in the binarized foreground image, and when the abnormal behavior of the moving target is analyzed, a warning is sent to the moving target, and warning information is sent to a manager. The processing system can actively analyze the monitoring function, automatically early warns, and improves the monitoring effect.

The invention discloses a dynamic tracking optical scanning system and method, and relates to the technical field of three-dimensional scanning, tracking equipment and scanning equipment are controlled through a central control subsystem to complete three-dimensional model reconstruction, and the central control subsystem comprises a calibration correction module, an image splicing module, a three-dimensional reconstruction module and a display module; the calibration correction module feeds back a correction instruction through a double-target calibration correction method; the image splicing module is used for splicing all binocular correction image sequences based on a transformation splicing algorithm to generate a global image; the three-dimensional reconstruction module generates an object disparity map and a target disparity map in combination with a stereo matching algorithm and a semantic segmentation labeling method, obtains a sparse three-dimensional model of a single time frame by adopting a re-projection transformation method, and generates a local dense three-dimensional model of the single time frame in combination with a dual-feature pose estimation network and a coordinate transformation method; and the display module performs real-time display based on a pose detection updating display strategy, so that synchronous dynamic tracking of pose transformation of the detected object and the scanning equipment in the scanning process is realized.

The embodiment of the invention discloses a three-dimensional modeling method and device applied to a power distribution room and electronic equipment. A specific embodiment of the method comprises the steps of generating an image quality improved image set according to a target image set; performing image splicing on the images after image quality improvement in the image set after image quality improvement; generating a model according to the spliced image and a pre-trained initial three-dimensional model, and generating an initial power distribution room three-dimensional model and model description information; for the model element description information, executing the following processing steps: determining whether a model element matched with the model element description information exists in a pre-constructed model element library or not according to an element type and an element feature vector included in the model element description information; in response to existence, determining the model elements matched with the model element description information as candidate model elements; and performing model element updating on the initial three-dimensional model of the power distribution room. According to the embodiment, the modeling period is shortened, and the computing resource consumption is reduced.

The invention discloses a building engineering multi-dimensional monitoring system based on panoramic imaging, and relates to the technical field of building engineering monitoring and sensing. Comprising a panoramic image acquisition module, an image splicing and panoramic reconstruction module, a multi-source data synchronous acquisition module, an image-data fusion construction module, an image-physical association modeling module and a risk identification and intelligent response module, an image acquisition array formed by high-resolution cameras is installed at key positions of a building structure, images of all the cameras are obtained, and an original image sequence covering a monitoring area is output. According to the invention, through fusion of the panoramic image and the multi-source sensing data, visualization, intelligent identification and risk prediction of the structure state are realized, a closed-loop monitoring system is constructed, the problems of single traditional monitoring means, data splitting and the like are solved, and the safety monitoring intelligent level of constructional engineering is improved.

The invention relates to the technical field of image processing, and provides a high-precision image splicing and correcting method based on a multi-view camera, and the method comprises the steps: recognizing a potential distortion region in an image according to a plurality of view images, and outputting the distortion region; detail features of the distortion areas are extracted, the distortion type of each distortion area is derived by combining geometric consistency of adjacent areas, and local correction parameters are generated; performing accurate correction on each pixel by adopting self-adaptive nonlinear transformation and combining a mapping relation between a pixel position and a distortion degree, and obtaining an image after distortion correction; and after image splicing is carried out on the images after distortion correction, the transition effect of the edge is optimized by combining a multi-scale region fusion technology. According to the invention, the visual unnatural phenomenon at the seam can be eliminated, and the precision and visual experience of the whole image splicing are improved.

The invention discloses a video transmission image stitching data enhancement method based on deep learning, and relates to the field of video image processing. The method comprises the following steps: S1, acquiring and screening images; s2, continuously screening structures and selecting key frames; s3, correcting image distortion; s4, splicing and fusing the images; s5, performing image enhancement output; firstly, a sliding time window mechanism is adopted, multi-dimensional image quality screening is combined, fuzzy, underexposure or severely-shielded inferior frames are accurately removed, and high quality of input key frames is ensured; through intelligent splicing and enhancement of a dynamic adaptive threshold strategy and semantic guidance, the image splicing precision and efficiency of the unmanned aerial vehicle and the multi-view camera in a complex environment are remarkably improved; besides, semantic segmentation guided feature extraction is combined with a multi-band fusion technology, seamless splicing is realized, the quality of an output image is improved, and the reliability of automatic analysis and decision making is remarkably improved.

The invention discloses a rice field insect pest detection method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the following steps: receiving the soil humidity and environment temperature of a rice field, and determining a temperature and humidity abnormal region of the rice field based on the soil humidity and environment temperature of the rice field; acquiring field image data in the temperature and humidity abnormal area of the rice field; processing the field image data in the temperature and humidity abnormal region of the rice field based on morphological closed operation and morphological gradient operation to obtain processed field image data, inputting the processed field image data into a pre-established YOLOv5s + target detection algorithm model, and outputting to obtain a pest and disease damage identification result; and performing image splicing generation based on the field image data in the temperature and humidity abnormal region of the rice field and the pest and disease identification result to obtain a disaster grading map.