416 results about "Large target" patented technology

The invention provides an optical lens, which comprises nine lenses from an object side to an imaging surface along an optical axis: a first lens with negative focal power, a second lens with negative focal power, a third lens with negative focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power, the object side surface of the second lens is a convex surface, and the image side surface of the second lens is a concave surface; the object side surface of the third lens is a convex surface, and the image side surface of the third lens is a concave surface; the object side surface of the fourth lens is a concave surface, and the image side surface of the fourth lens is a convex surface; the object side surface of the fifth lens is a convex surface, and the image side surface of the fifth lens is a convex surface; the sixth lens has negative focal power; the seventh lens has positive focal power; the eighth lens has negative focal power; and the ninth lens has positive focal power. According to the optical lens provided by the invention, through specific surface shape matching and reasonable focal power distribution, the lens has one or more advantages of short focus, miniaturization, large field angle, large aperture, large target surface, low distortion and low sensitivity.

The invention provides a lightweight visible light ship target detection method based on edge feature guidance, and relates to the technical field of ship detection image data processing, and the method comprises the steps: collecting remote sensing satellite images, and carrying out the random distribution of the images after screening and marking, and obtaining a training set and a verification set; the backbone network module comprises a plurality of Conv modules and C3k2 modules which are mutually stacked; the neck module comprises a detail-enhanced convolution module and a hierarchical pyramid module based on dynamic feature aggregation; in the head module, after the features of all detection layers are subjected to independent convolution processing, feature transformation is carried out through a multi-branch detail enhancement convolution module; performing data enhancement on the training set; and obtaining a trained ship target detection model through a back propagation algorithm and a gradient descent optimization method. According to the invention, the lightweight and precision improvement of the detection head are realized, the robustness of the model to the illumination change is enhanced, and the global semantic information and the local detail features are fused to balance the detection of the small target and the large target.

The invention discloses a target tracking method fusing visual perception and motion control. The method comprises the following steps: improving a YOLOv8 model and pre-training the improved YOLOv8 model; s2, preprocessing an image acquired by a camera, inputting the preprocessed image into the YOLOv8 model trained in the step S1, and outputting a target bounding box and a target center pixel coordinate; target center pixel coordinates are transmitted to a development board of a pre-burning algorithm program through a serial port, the development board is designed according to a stepping motor mathematical model, sine acceleration and deceleration serve as the basis, PID control, sliding mode control and fuzzy control algorithms are fused, and code burning is generated after in-loop simulation optimization; and the development board drives a stepping motor to adjust the posture of the holder according to the target center pixel coordinate control instruction, so that the target stops moving after being kept in the view center. The problems that a traditional YOLOv8 model is large in target recognition error under the complex background, PID control is prone to oscillation under the nonlinear working condition, buffeting exists in sliding mode control, and response lags when fuzzy control is independently used are solved.

The invention provides an optical lens, which comprises nine lenses from an object side to an imaging surface along an optical axis: a first lens with negative focal power, a second lens with negative focal power, a third lens with negative focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power, the object side surface of the second lens is a convex surface, and the image side surface of the second lens is a concave surface; the object side surface of the third lens is a convex surface, and the image side surface of the third lens is a concave surface; the object side surface of the fourth lens is a concave surface, and the image side surface of the fourth lens is a convex surface; the object side surface of the fifth lens is a convex surface, and the image side surface of the fifth lens is a convex surface; the sixth lens has negative focal power; the seventh lens has positive focal power; the eighth lens has negative focal power; and the ninth lens has positive focal power. According to the optical lens provided by the invention, through specific surface shape matching and reasonable focal power distribution, the lens has one or more advantages of short focus, miniaturization, large field angle, large aperture, large target surface, low distortion and low sensitivity.

The invention belongs to the technical field of image processing, and discloses a nonferrous metal target detection method and system for a scraped car. According to the method, through deep coupling of a GAN bimodal fusion network and a double-backbone network, a scraped car nonferrous metal target detection model is constructed, so that nonferrous metals in scraped cars are efficiently and accurately sorted. A bimodal fusion network is introduced into a model input layer, a fusion image with infrared thermal saliency and visible light texture features is generated, and the problem of cross-modal information splitting is solved; according to the dual-backbone network, standard convolution is replaced by a three-branch structure of an MGHCM module, large target contours, middle target semantics and small target details are synchronously captured, and self-adaptive shunting and efficient fusion of multi-scale features are achieved in cooperation with dynamic feature routing of a DHFBlock module; and the detection head network is optimized into a rotating frame prediction and cross-modal cross entropy classification mechanism so as to adapt to the placement scene of the metal fragments at any angle.

The invention provides an optical lens, which comprises nine lenses from an object side to an imaging surface along an optical axis: a first lens with negative focal power, a second lens with negative focal power, a third lens with negative focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power, the object side surface of the second lens is a convex surface, and the image side surface of the second lens is a concave surface; the object side surface of the third lens is a convex surface, and the image side surface of the third lens is a concave surface; the fourth lens has negative focal power, and the object side surface of the fourth lens is a concave surface; the object side surface of the fifth lens is a convex surface, and the image side surface of the fifth lens is a convex surface; the sixth lens has negative focal power, and the image side surface of the sixth lens is a concave surface; the seventh lens has positive focal power; the eighth lens has negative focal power; and the ninth lens has positive focal power. According to the optical lens provided by the invention, through specific surface shape matching and reasonable focal power distribution, the lens has one or more advantages of short focus, miniaturization, large field angle, large aperture, large target surface, low distortion and low sensitivity.

The invention provides an optical lens, which comprises nine lenses from an object side to an imaging surface along an optical axis: a first lens with negative focal power, a second lens with negative focal power, a third lens with negative focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power, the object side surface of the second lens is a convex surface, and the image side surface of the second lens is a concave surface; the object side surface of the third lens is a convex surface, and the image side surface of the third lens is a concave surface; the object side surface of the fourth lens is a concave surface, and the image side surface of the fourth lens is a convex surface; the object side surface of the fifth lens is a convex surface, and the image side surface of the fifth lens is a convex surface; the sixth lens has negative focal power; the seventh lens has positive focal power; the eighth lens has positive focal power; and the ninth lens has positive focal power. According to the optical lens provided by the invention, through specific surface shape matching and reasonable focal power distribution, the lens has one or more advantages of short focus, miniaturization, large field angle, large aperture, large target surface, low distortion and low sensitivity.

The invention provides an optical lens, which comprises nine lenses from an object side to an imaging surface along an optical axis: a first lens with negative focal power, a second lens with negative focal power, a third lens with negative focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power, the object side surface of the second lens is a convex surface, and the image side surface of the second lens is a concave surface; the object side surface of the third lens is a convex surface, and the image side surface of the third lens is a concave surface; the image side surface of the fourth lens is a convex surface; the object side surface of the fifth lens is a convex surface, and the image side surface of the fifth lens is a convex surface; the sixth lens has negative focal power, and the image side surface of the sixth lens is a concave surface; the seventh lens has positive focal power; the eighth lens has positive focal power; and the ninth lens has negative focal power. According to the optical lens provided by the invention, through specific surface shape matching and reasonable focal power distribution, the lens has one or more advantages of short focus, miniaturization, large field angle, large aperture, large target surface, low distortion and low sensitivity.

The invention provides an optical lens, which comprises six lenses which sequentially comprise a first lens with negative focal power, a second lens with negative focal power, a third lens with negative focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power from an object side to an image side along an optical axis, the second lens has positive focal power; the third lens has positive focal power; the object side surface of the fourth lens is a concave surface, and the image side surface of the fourth lens is a convex surface; the fifth lens has negative focal power; the sixth lens has positive focal power, and the object side surface of the sixth lens is a convex surface; wherein the curvature radius R5 of the object side surface of the third lens and the curvature radius R6 of the image side surface of the third lens meet the following formula:-1lt; (R5-R6) / (R5 + R6) lt; and 0.9. According to the optical lens provided by the invention, the imaging quality of the optical lens can be improved, the aberration can be reduced, the imaging quality of the optical lens can be improved, and the lens has one or more advantages of miniaturization, large target surface, large aperture, large field angle, high imaging quality and the like.

The invention discloses a low-scattering support system design and optimization method for large-scale target external field measurement, the support system comprises a support and a low-scattering end cap, and the low-scattering end cap is connected with the top of the support; the method comprises the following steps: S1, constructing a bracket geometric model based on a low-scattering shape design formula, and evaluating the bearing capacity of a bracket; s2, performing electromagnetic solution on the bracket by using a non-conformal region decomposition area integral equation method, and optimizing a bracket geometric model; s3, constructing a low-scattering end cap based on a low-scattering shape design formula; and S4, placing the low-scattering end cap at the top of the bracket to form a low-scattering support system, and evaluating the composite backscattering of the low-scattering support system by using a non-conformal region decomposition area integral equation. According to the low-scattering supporting system, high bearing capacity and good low-scattering performance are both considered, and reliable supporting guarantee can be provided for external field measurement of a large target.

The invention relates to a multi-scale adaptive detection method and system for an infrared image of an unmanned aerial vehicle, computer equipment, a storage medium and a computer program product. The method comprises the following steps: preparing and inputting infrared image data; performing reasoning and dynamic adaptive slicing processing on the infrared image data; building a detection framework model; performing model training and optimization based on the sliced infrared image data; and generating an image detection result. The problems of large target scale difference, low contrast, high noise, complex image, blurred target and the like in unmanned aerial vehicle infrared image target detection in the prior art are solved, and the detection performance is remarkably improved. Besides, a YOLOv8 + SAHI + ULSAM integrated model is adopted to carry out target detection on the infrared image of the unmanned aerial vehicle, a target under a complex background can be identified more accurately through the improved YOLOv8 model, the ULSAM module is light in weight, and the calculation burden cannot be remarkably increased.

The utility model provides an all-glass vehicle-mounted day and night confocal optical system and a camera module applying the same, mainly comprising eight lenses, the first lens has negative focal power, the object side surface is a convex surface, and the image side surface is a concave surface, the second lens has negative focal power, the object side surface is a concave surface, and the image side surface is a convex surface. The third lens has positive focal power, the object side surface is a convex surface, the image side surface is a concave surface, the fourth lens has negative focal power, the object side surface is a convex surface, the image side surface is a concave surface, the fifth lens has positive focal power, the object side surface is a convex surface, the image side surface is a convex surface, and the fourth lens and the fifth lens form a bonding lens. The sixth lens has positive focal power, the object side face of the sixth lens is a convex face, the image side face of the sixth lens is a convex face, the seventh lens has negative focal power, the image side face of the seventh lens is a concave face, the eighth lens has focal power, and the image side face of the eighth lens is a concave face. And the picture illumination uniformity and the imaging definition are improved.

The invention discloses an SAR directed target detection method based on multi-scale context sensing, and belongs to the technical field of remote sensing image processing and computer vision. According to the method, firstly, through a scattering characteristic guided multi-scale dynamic characteristic enhancement network, a multi-branch structure and a dynamic fusion mechanism are utilized to enhance characteristic expressions of targets of different scales; and secondly, designing a task-adaptive regional context sensing module, fusing local details and semantic contexts, and improving the target discrimination capability under a complex background. And a decoupling type progressive refining detection head is adopted to predict a target category, a directed bounding box and a direction angle, and the positioning precision is optimized through progressive regression. Semantic consistency loss is introduced in the training stage, and end-to-end optimization is achieved in combination with a multi-task joint strategy. According to the method, the problems of large target scale difference, strong background interference, changeable directions and the like in the SAR image are effectively solved, and the accuracy and robustness of directed target detection are remarkably improved.

The invention discloses an aerial target height estimation method based on satellite-borne radar networking. The radar networking is composed of a radar group consisting of two or more satellite-borne radars. The aerial target height estimation method comprises the following steps: step S1: using the radar group to respectively measure the radial distance and azimuth of the aerial target; step S2: using a position calculation model of the aerial target in the radar array coordinate system to calculate the position of the aerial target in the radar array coordinate system by using the least squares method; step S3: calculating the position of the aerial target in the satellite orbit coordinate system according to the position of the aerial target in the radar array coordinate system; step S4: calculating the altitude of the aerial target according to the position of the aerial target in the satellite orbit coordinate system and the radius of the earth where the aerial target is located. The invention establishes an equation group containing unknown variables such as the target altitude, thereby solving the problem that a single satellite-borne detection radar has a large target tracking pitch angle error and is unable to estimate the altitude of the aerial target.

The invention belongs to the technical field of infrared optics, and discloses a long-wave infrared continuous zooming microscope lens. The lens is provided with two lenses, namely a first lens and a second lens which are sequentially and coaxially arranged between an object plane and an image plane; the first lens is a meniscus positive lens of which the convex surface faces the image side, and the second lens is a meniscus positive lens of which the convex surface faces the image side; the first lens and the second lens can move between an object plane and an image plane so as to carry out high and low magnification switching. The long-wave infrared continuous zooming microscope lens can realize multi-magnification switching, and also has the characteristics of small number of lenses, large target surface and clear imaging; the working wave band is 8-12 [mu] m, the magnification factor is 0.2-0.6, the F number of the image space is 1.5, the number of pixels is 1280 * 1024, and the size of the pixels is 12 [mu] m.

The invention relates to the technical field of target recognition, in particular to a multi-scale marine target detection method, which comprises the following steps of: acquiring a water surface image through a visible light camera; replacing a standard convolutional layer in the backbone network by using depth separable convolution; linear deformable convolution is introduced into the backbone network, and the positions of convolution kernel sampling points are dynamically adjusted; fusing an efficient multi-scale attention mechanism to optimize feature selection; in the prior art, a standard convolution structure with a fixed receptive field is adopted and is difficult to adapt to the violent scale change of a sea surface target from a far tiny buoy to a near large ship, so that the omission ratio of a small target is high and the positioning deviation of the large target is obvious; according to the scheme, linear deformable convolution is introduced, and the position of a convolution kernel sampling point is dynamically adjusted, so that the model adaptively learns offset according to a target spatial scale, and multi-scale features are accurately captured; the feature extraction capability of the model for a large-scale target is enhanced, and the adaptability defect of traditional convolution in the ocean dynamic environment is effectively overcome.

The invention relates to a light and miniature big-target-surface low-light lens, and the lens comprises a front lens group A, a diaphragm B and a rear lens group C, wherein the front lens group A, the diaphragm B and the rear lens group C are sequentially arranged in an incident direction of an optical path. The front lens group A includes a positive meniscus lens A1, a negative meniscus lens A2,a biconcave lens A3 and a biconvex lens A4, wherein the positive meniscus lens A1, the negative meniscus lens A2, the biconcave lens A3 and the biconvex lens A4 are sequentially arranged in an incident direction of the optical path. The biconcave lens A3 and the biconvex lens A4 are in close connection with each other as a first bonding group. The rear lens group C includes a biconcave lens C1, abiconvex lens C2, a biconvex lens C3, a plano-convex lens C4 and a negative meniscus lens C5, wherein the biconcave lens C1, the biconvex lens C2, the biconvex lens C3, the plano-convex lens C4 and the negative meniscus lens C5 are sequentially arranged in an incident direction of the optical path. The biconcave lens C1 and the biconvex lens C2 are in close connection with each other as a secondbonding group. The invention also relates to an imaging method of the lens. The lens is reasonable in design, effectively improves the imaging quality under the requirement of the large target surfaceof the lens, reduces the number of optical lenses, and realizes light and miniature lens. The lens has strong light collecting and light transmitting ability, and the target surface of the lens is large. The lens is high in optical resolution, is large in angle of view, and is large in observable range.

The invention relates to a 1.2-inch intelligent traffic monitoring system lens and a working method thereof. An optical imaging system of the lens is sequentially provided with a first lens to a fourteenth lens from an object space to an image space. Wherein the focal power of each lens adopts a positive and negative combined form, so that optical aberrations such as spherical aberration and the like are greatly reduced. Wherein the fourth lens and the fifth lens form an achromatic doublet lens group, the seventh lens and the eighth lens form an achromatic doublet lens group, the ninth lens and the tenth lens form an achromatic doublet lens group, and the eleventh lens, the twelfth lens and the thirteenth lens form an achromatic triplet lens group. Through reasonable lens matching, an optical system realizes large aperture, large target surface and no defocus in day and night, and through good correction of on-axis and off-axis aberrations, the lens has good imaging quality.

To provide a method of near-field radar image reconstruction from radar data.SOLUTION: The method includes: receiving radar data corresponding to an image; and segmenting the image into multiple sub-images. Each sub-image includes a section of the image. The method further includes, for each sub-image, generating shifted data, by multiplying the radar data by a shifting term. The shifting term accounts for a distance between a center of the image and a center of the sub-image. The center of the sub-image is corrected for a curvature of a wavefront of a radar pulse. The method further includes: generating reduced data by filtering and downsampling the shifted data, such that a total amount of the shifted data is reduced; and reconstructing the sub-image, by back-projecting the reduced data. Performing the shifting by using the shifting term reduces distortion in the reconstructed sub-image.SELECTED DRAWING: Figure 2

The invention belongs to the technical field of information remote sensing, and particularly discloses a port and bridge large-scale target thematic map generation method based on an SAR technology, and the method comprises the steps: obtaining multi-temporal synthetic aperture radar image data of a port region, processing the original signal intensity distribution of the image data through denoising and filtering, and obtaining a smoothed scattering intensity map; according to the scattering intensity graph, extracting echo amplitude and phase information of a target area, fusing multi-polarization channel data, and determining a scattering characteristic parameter set; grouping the scattering characteristic parameter set based on a clustering algorithm, and separating preliminary clustering results of facility classes and residue classes to obtain feature vector representation; matching the feature vector representation with a stored shape template according to a pre-established feature database; the objective of the invention is to solve the problem in the prior art that environment management is difficult to accurately position a business scene due to mixed distribution of facilities and residues in a port area.

The invention relates to a refraction-super hybrid long-focus day and night confocal lens and an image acquisition device, and belongs to the field of optical lenses. The long-focus day and night confocal lens has focus consistency in a visible light band and an infrared band; comprising a first lens, a second lens, a third lens, a fourth lens, a fifth lens, a sixth lens, a seventh lens and an eighth lens which are sequentially arranged from an object plane to an image plane along an optical axis; at least one of the first lens, the second lens, the third lens, the fourth lens, the fifth lens, the sixth lens, the seventh lens and the eighth lens is a metasurface lens, and the other lenses are spherical lenses. By adopting the technical scheme of folding and super mixing, large-target-surface day and night common-focal-length imaging is realized, the use of glued lenses is reduced, the complexity of the system is reduced, the assembly difficulty is reduced, and the risk that a structural member generates stray light is reduced.

The present invention relates to the technical field of pavement engineering. Disclosed are a pavement distress detection method and apparatus, a device and a medium. The method comprises: carrying out distress detection on a pavement radar image respectively on the basis of a well-trained large-object detection model and a well-trained small-object detection model, to correspondingly obtain a large-object detection result and a small-object detection result, respectively; and fusing the large-object detection result and the small-object detection result to determine a distress detection result of the pavement radar image, wherein the large-object detection model comprises a deformable convolution layer, a dual-path detection head, and a soft non-maximum suppression operation layer, and the small-object detection model comprises a multi-scale convolutional layer, a deformable ROI pooling layer, a dual-path detection head, a balanced line loss function, and a soft non-maximum suppression operation layer. The structure and corresponding function of a dual-path object detection model are controlled in a targeted manner, and detection results of the two paths are fused, such that the reliability of a final distress detection result is guaranteed.

The invention discloses a target detection method based on a Bayesian network-conditional variation auto-encoder, and relates to the field of target detection. Firstly, denoising processing is performed on an original image through a Bayesian network; then, logarithmic intensity is generated based on the original image through a conditional variational auto-encoder; and inputting the preprocessed image and the logarithmic intensity into a Kalman filter for image reconstruction. And finally, inputting the reconstructed image into a YOLOv10 network to detect a large target, a medium target and a small target. According to the method, the Bayesian network and the conditional variation auto-encoder are fused into image reconstruction, and the YOLOv10 network is improved. The Bayesian network is used for reducing noise and uncertainty in an image, and the conditional variation auto-encoder enhances reconstruction of an image structure and texture. A parallel multi-attention module is arranged in the YOLOv10 network, attention of a channel and space is enhanced in the feature extraction stage, and the target detection precision and speed of the YOLOv10 network are improved.

The invention relates to a small-sized light-weight large-target-surface infrared athermalization optical system, and the optical system consists of a first meniscus positive lens, a meniscus negative lens, a second meniscus positive lens and a third meniscus positive lens which are sequentially and coaxially arranged from an object side to an image side, the second meniscus positive lens and the third meniscus positive lens are the same lens, the first meniscus positive lens, the second meniscus positive lens and the third meniscus positive lens are all made of chalcogenide glass IRG206, and the optical system ensures that edge imaging is clear and the image surface diameter reaches 20mm through optimal arrangement of focal power and optical materials of all the lenses and intervals among all the lenses, so that the image quality is improved. The long-wave infrared imaging device can adapt to a 1280 * 1024 array and a long-wave uncooled infrared detector with the pixel size of 12 microns, and has the advantages of high imaging quality, small size and light weight.

The invention relates to the technical field of remote sensing image processing and computer vision, in particular to a mining area earth surface feature remote sensing semantic segmentation method and device, electronic equipment and a storage medium. The high-resolution remote sensing image is a high-resolution No.2 original image containing blue light, green light, red light, near-infrared and panchromatic wave bands. A remote sensing semantic segmentation model of the surface features of the mining area is constructed, and a ResNeSt module and an SE channel attention module are introduced into an encoder, so that the feature response and discrimination capability of the model to slender and curved boundaries of key elements such as a dam body and a reservoir area of the mining area is effectively enhanced. And meanwhile, multi-scale context information is aggregated in combination with an ASPP module, so that the problems of boundary fracture and detail loss caused by large target scale difference are effectively solved, and the extracted mining area edge contour is more continuous and complete and is remarkably superior to a traditional segmentation method.

This invention relates to the field of large target RCS measurement technology, specifically to a two-dimensional fast measurement method for the RCS of large targets in the angular and frequency domains. The method constructs an equally spaced full-sampling measurement matrix P to obtain a sparse matrix R. Based on the sparse matrix R and the full-sampling signal, the target's rotation angle is scanned to obtain the measured value y. Finally, the full-sampling signal is reconstructed based on the measured value y to obtain the target's full-sampling RCS. The rotation-scanning sparse sampling method ensures that the sparse sampling frequency points are the same for each sampling angle, thus improving testing efficiency.

The present application provides an optical lens (110), a camera module (100), and an electronic device (10). The optical lens (110) comprises a catadioptric lens (111), a lens group (112) and a light folding member (113). An incident light ray enters a first transmission surface (111a) of the catadioptric lens (111) and is refracted therethrough, then is reflected by a first reflection surface (111b) and a second reflection surface (111c) in sequence, and is refracted through a second transmission surface (111d) and is emitted; after passing through the lens group (112), the light ray is reflected multiple times in the light folding member (113) and then is emitted. The catadioptric lens (111) can achieve two-time light reflection and large aperture, the lens group (112) can achieve light ray concentration and adjustment, and the light folding member (113) can achieve multi-time light reflection. The optical lens (110) has a long back focal magnification light path and a relatively large equivalent focal length, and can be used in combination with an image sensor (120) having a large target surface, thereby achieving high-precision and high-resolution shooting. A folded light path utilizing the catadioptric lens (111) and the light folding member (113) can achieve small height and volume of the optical lens (110) and a relatively small shoulder height. The camera module (100) comprises the image sensor (120), a focusing motor (130), an anti-shake motor (140) and the optical lens (110). The electronic device (10) comprises a device housing (200) and the camera module (100).

The application discloses a multi-target pose real-time online estimation method, system, device and medium, and aims at real-time aerial images. On one hand, the ground target pose is quickly estimated through block detection and head-to-tail interconnection. On the other hand, the ground target is tracked in real time online in combination with a data association thought. The problems of small target volume, large target quantity, easy to be shielded, dynamic non-cooperation and limited calculation capacity of embedded equipment in the aerial images are effectively overcome. The efficiency of aerial multi-target pose estimation is improved. The aerial ground target online real-time estimation in the airborne embedded environment is realized. The multi-target, multi-dimensional and strong real-time requirements of ground target detection and sensing in the fields of disaster rescue and military monitoring are met.

The utility model discloses a scanning device for three-dimensional model information acquisition, and relates to the technical field of three-dimensional scanning, and the scanning device comprises a mainframe box which is internally provided with a lifting driving unit; the lower sliding column is inserted into the mainframe box, and a lower sliding groove is formed in the side face; the upper sliding column is rotationally connected to the upper end of the lower sliding column, an upper sliding groove is formed in the side face of the upper sliding column, and the upper sliding groove communicates with the lower sliding groove; a lower movable groove is formed in the lower sliding column; an upper movable groove is formed in the upper sliding column, the lower movable groove and the upper movable groove form an annular sliding groove, and an annular chain is wound in the annular sliding groove; the lifting driving unit drives the chain to move in the annular sliding groove. The sliding rod is inserted into the lower sliding groove and connected with the chain, and a scanning unit is arranged at the tail end. The sliding column on the mainframe box is divided into the lower sliding column and the upper sliding column, and the upper sliding column can rotate by 90 degrees, so that the scanning unit can scan a large target and a small target in different postures, and the scanning requirements of various different targets are met.