1435 results about "Image plane" patented technology

The invention discloses a railway tunnel foreign matter detection method based on radar point cloud and RGB image fusion. The method comprises the following steps: acquiring a laser radar point cloud and an RGB image in a railway tunnel; obtaining an RGB image by using an adversarial network model GAN to judge the abnormal occurrence state of the foreign matter in the railway tunnel, and if the abnormal state of the foreign matter occurs, projecting the obtained laser radar point cloud to an image plane to obtain a dense depth map; processing through a sliding window to obtain data slices which are approximately square; and carrying out data feature extraction, fusion and foreign object target detection on the data slices of the RGB image and the dense depth map by using a fusion target detection network based on the OfficientNet-FPN, and outputting a foreign object target information detection result containing a target category, a position, a confidence coefficient and a distance. According to the railway tunnel foreign matter detection method based on radar point cloud and RGB image fusion, high precision, high efficiency and high reliability of railway tunnel foreign matter detection are realized.

The embodiment of the invention provides an optical detection device which comprises an imaging assembly and a photoelectric detection device, and the imaging assembly is used for forming an image on an image plane of a to-be-detected area of a wafer. And the photoelectric detection device is used for receiving images and is arranged at the image plane. The photoelectric detection device comprises a light beam separation assembly and a sensor assembly, the light beam separation assembly is used for dividing a continuous first light signal in an image into multiple beams of second light signals, and the light beam separation assembly comprises a microlens array. The sensor assembly is used for receiving multiple beams of second optical signals, and each pixel unit of each sensor in the sensor assembly is used for receiving one beam of second optical signal in the multiple beams of second optical signals. The sensor assembly comprises a single-pixel sensor, and the single-pixel sensor corresponds to one micro lens in the micro lens array; and / or, the sensor assembly comprises a multi-pixel sensor, the multi-pixel sensor corresponds to a plurality of microlenses in the microlens array, and spatial segmentation is beneficial to improving the signal-to-noise ratio.

The invention provides an optical lens, which comprises eight lenses with focal power and sequentially comprises a first lens with negative focal power, a second lens with positive 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 imaging surface along an optical axis, 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 third lens has negative focal power, the object side surface of the third lens near the optical axis is a convex surface, and the image side surface of the third lens is a concave surface; the fourth lens has positive focal power; 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 positive focal power; the seventh lens has negative focal power; and the object side surface of the eighth lens is a convex surface, and the image side surface of the eighth lens is a convex surface. The optical lens provided by the invention has one or more advantages of short focus, large field angle, high imaging quality and the like.

The invention relates to a semantic simultaneous localization and mapping method and system based on Gaussian splashing. The method comprises the following steps: firstly, collecting a frame of RGB-D image, modeling a scene into a 3D semantic Gaussian field containing a plurality of 3D semantic gausses according to the RGB-D image, and rendering the 3D semantic gausses by using a tile rasterization technology to obtain 2D image plane gausses; rendering results of RGB color, depth and semantic features are extracted from the 2D image plane in a Gaussian mode, and the semantic features are decoded into semantic tags; constructing a mapping and tracking loss function by using an RGB color rendering result, a depth rendering result, a semantic tag and a truth value, and jointly optimizing a camera pose and a semantic Gaussian field based on a tracking stage and a mapping stage; and repeating the steps for each new frame of RGB-D image to complete the construction of the incremental semantic Gaussian map. Compared with the prior art, the method has the advantages of realizing robust camera tracking, real-time high-quality rendering, accurate 3D semantic reconstruction and the like.

The invention discloses a digital human rendering method based on Gaussian splashing and multi-scale characteristic field distillation, and belongs to the field of three-dimensional human body digital reconstruction. According to the method, feature extraction is carried out through a Vision Transformer encoder, based on an SMPL model, through a cross-modal parameter estimation module and dynamic human body modeling of three-dimensional Gaussian splashing and semantic feature rendering, three-dimensional Gaussian is projected to a two-dimensional image plane to calculate a covariance matrix and color mixing, and after a rendered color image and an initial feature field image are output, a three-dimensional image is obtained. A student feature map is obtained through a convolution acceleration module, a teacher feature map is obtained after feature extraction is carried out through a two-dimensional basic model, the constructed model is trained, and optimization training is completed through comprehensive total loss function calculation; according to the method, the problems of fuzzy semantics, detail missing, low rendering efficiency and inaccurate human body-scene separation in the existing method are effectively solved, so that more efficient, fine and robust monocular or multi-view human body three-dimensional reconstruction is realized.

The invention provides a three-dimensional target marking method and device based on a point cloud data-assisted two-dimensional image, and the method comprises the steps: fusing precise three-dimensional point cloud information obtained by a laser radar and two-dimensional image data collected by a camera based on a point cloud data and image data cooperation three-dimensional marking method; and a dynamic high-precision three-dimensional target marking system is constructed. Specifically, a space-image relationship is established through three-dimensional point cloud information and a two-dimensional image plane, and three-dimensional information of a target is mapped into a two-dimensional image, so that geometric and semantic information labeling is carried out on the target in the two-dimensional image, information loss of two-dimensional visual data in dimensions such as depth, attitude and space structure is made up, and the accuracy of the three-dimensional information labeling is improved. And meanwhile, when the three-dimensional point cloud is unmeasurable, a three-dimensional target labeling mode is dynamically adjusted in combination with two-dimensional image labeling information. According to the method, through dynamic high-precision three-dimensional target labeling, the obtained image-label can be used for training and reasoning of a three-dimensional target detection network, and the method has a wide application prospect.

The invention provides a coal face mining auxiliary method based on visual and point cloud data fusion modeling, and the method comprises the steps: collecting the visual image data of a coal face, collecting the three-dimensional point cloud data through a point cloud sensor, and obtaining the motion state information through an inertial sensor; based on internal and external parameters and IMU data of joint calibration, mapping point cloud data to an image plane, establishing a geometric mapping relationship between three-dimensional points and pixels, generating an attribute point cloud model containing geometry, color and semantics, and generating a high-precision global three-dimensional digital twinning model; and identifying and tracking equipment instances in the digital twinborn model, and resolving motion parameters of key components based on a URDF kinematics model and a principal component analysis algorithm to support remote control decisions. According to the invention, high-precision real-time sensing and three-dimensional visualization of the working space size, the equipment connection relation and the movement position of the coal face can be realized, and the remote control precision and the response efficiency of unmanned mining are remarkably improved.

The invention discloses a material movement behavior monitoring method and system based on monocular vision, and relates to the field of material movement monitoring, and the method comprises the steps: firstly collecting a material movement video through a monocular high-speed camera, and extracting a continuous state sequence of a material on a two-dimensional image plane through employing an instance segmentation and time sequence tracking technology; the method is characterized in that a time sequence deep learning model is introduced to analyze a state sequence, and a key event that a material collides with a working surface of equipment is automatically identified, so that the collision moment and contact point coordinates are accurately obtained. In this way, the collision contact point is used as a geometric anchor point for three-dimensional space back calculation, a two-dimensional image track is reversely projected and reconstructed into a three-dimensional space motion track in combination with internal parameters of a camera and physical parameters of an equipment reference plane, then key kinetic parameters such as the speed and the recovery coefficient are calculated, and low-cost and high-precision automatic online monitoring is achieved.

The invention discloses a corn detection method based on visual identification, and relates to the technical field of corn ear detection, and the method comprises the steps: collecting corn images of a corn, including an RGB image, an NIR image and a Depth image; registering the corn image based on the transformation matrix to respectively obtain a registered RGB image and a registered NIR image; creating a blank image to obtain a binary mask image; based on the binary mask image, segmenting the Depth image, registering the RGB image and registering the NIR image to respectively obtain a Depth cluster area, an RGB cluster area and an NIR cluster area; constructing an image plane corresponding to a camera visual angle under a standard attitude, and obtaining an aligned RGB image and an aligned NIR image based on the image plane, the Depth cluster area, the RGB cluster area and the NIR cluster area; and inputting the aligned RGB image and the aligned NIR image into a double-branch deep learning fusion network to obtain a detection result. The problem that the existing corn ear detection method is high in subjectivity, low in efficiency and easy to be interfered by the environment and omission and misjudgment are caused by ear attitude diversity is solved.

The invention discloses a compact internal focusing type standard photographic lens and a photographic device. The photographic lens sequentially comprises a first lens group with positive focal power, an aperture diaphragm, a second lens group with positive focal power and a third lens group with negative focal power from an object side to an image side, in the focusing process, the second lens group moves along the optical axis, and the first lens group and the third lens group remain unchanged relative to the position of the image plane. The first lens group, the second lens group and the third lens group satisfy the following conditional expressions: 1 < = F1 / F < = 2; 0.2 < = F2 / F < = 1; -3 < = F3 / F < = 0; wherein F1 represents the composite focal length of the first lens group, F2 represents the composite focal length of the second lens group, F3 represents the composite focal length of the third lens group, and F represents the focal length of the photographic lens. The compact internal focusing type standard photographic lens provided by the technical scheme of the invention is beneficial to optimization of optical performance, focusing mechanism, positive and negative focal power balance and simplification of design and manufacturing of the lens.

To provide technology suitable for performing highly precise measurement of optical characteristics.SOLUTION: An evaluation method for evaluating an optical characteristic of a projection optical system is provided. The evaluation method includes a measurement step of illuminating a test reticle having a test pattern disposed on an object plane of the projection optical system, and measuring an amount of positional deviation of an image of the test pattern formed on an image plane of the projection optical system, and an estimation step of estimating the optical characteristic of the projection optical system based on a result of the measurement, wherein in the estimation step, the optical characteristic is estimated using a polynomial model including a regularization term.SELECTED DRAWING: Figure 2

The invention discloses a power transmission line external damage prevention detection method, device and equipment and a medium, and the method comprises the steps: constructing a power transmission line external damage prevention detection model which is used for recognizing and positioning a hidden danger target corresponding to a power transmission line; obtaining a monitored image and point cloud data of the power transmission line; inputting the monitored image into the power transmission line external damage prevention detection model to identify and position a hidden danger target, and outputting two-dimensional pixel coordinate data of the hidden danger target in an image plane coordinate system; determining a spatial distance between the power transmission line and the hidden danger target based on the monitored image, the two-dimensional pixel coordinate data and the point cloud data; and based on the spatial distance, evaluating the external damage risk of the power transmission line. Automatic monitoring and early warning of the power transmission line are achieved, the response speed and processing efficiency of external damage prevention of the power transmission line are greatly improved, and a powerful guarantee is provided for safe and stable operation of the power transmission line.

The invention discloses a visual large model and point cloud projection fused 3D target labeling method and system, and relates to the technical field of 3D target automatic labeling, and the method comprises the steps: carrying out the pixel-level semantic segmentation of a group of collected images and point clouds through a visual large model, and obtaining a segmentation image of a target pixel region and the semantic label of each pixel; generating a point cloud with a semantic tag by using projection of the point cloud to an image plane and semantic mapping; the method comprises the following steps: extracting foreground point clouds belonging to the same semantic tag, then performing clustering processing, obtaining a point cloud cluster belonging to the same semantic tag, traversing the point cloud cluster, fitting a 3D bounding box, combining parameters of a 3D target box obtained by fitting with the same semantic tag, generating 3D target annotations of the same semantic tag, then performing processing, and obtaining the 3D target annotations of the same semantic tag. Obtaining a final labeling result file; according to the 3D target labeling method, efficient and accurate 3D target automatic labeling is realized, the labeling cost is remarkably reduced, and the quality and consistency of labeled data are improved.

The invention relates to the technical field of computer vision and image processing, in particular to a cross-day-and-night boundary target thermo-optic joint detection method, which comprises the following steps of: firstly, acquiring a visible light image and an infrared image and unifying the visible light image and the infrared image to an image plane reference system; extracting topological features and multi-scale energy features from the input, fusing the topological features and the multi-scale energy features to generate a feature map, and outputting a central heat map, target size regression and sub-pixel offset by adopting anchor-frame-free detection; implementing optimal transmission correction according to an evidence field obtained by normalization of a cross reconstruction residual field to obtain a correction heat map and an initial candidate; triggering a fixation area by using the shape correction heat map and the initial candidate, executing super-resolution and secondary detection in the area, and performing affine reprojection and primary detection fusion to form an updated heat map and a fusion candidate; and in combination with uncertainty and topological consistency, a final detection set is output by using a conditional random field and non-maximum suppression. The method is stable in low-contrast, small-target and strong-interference scenes.

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 arrangement comprises a projection system (22) for microlithography and a wall element (28) for thermally shielding the projection system (22) from an illumination system (10). The projection system (22) has an object field that can be illuminated by the illumination system (10) and optical elements (M1-M6) for imaging the object field onto an image plane (21). The wall element (28) also includes a passive shielding surface element (30) arranged in a transition region between a last beam section (41) of the illumination system (10) and a first beam section (42) of the projection system (22). According to the invention, the arrangement further comprises an active cooling element (31) positioned in the transition region, with a cooling surface (32) that extends at least partially along a surface area of ​​the passive shielding surface element (30).The active cooling element positioned in the transition area can significantly reduce the rate of change of the heat flow from the lighting system to the projection system.

The invention relates to an overlay measurement system and a measurement method, and belongs to the technical field of chip manufacturing. Interference scanning focus measurement and independent focusing imaging of double industrial cameras are combined, an overlay measurement system accurately positions focal planes of different photoetching layers on a wafer by using interference signals through one-time vertical scanning, and then the two cameras are driven to be synchronized to image plane positions of the respective focal planes, so that the alignment precision of the wafer is improved. Therefore, one-time and synchronous high-definition imaging and overlay error calculation of two layers of patterns exceeding the optical focal depth are realized, and the measurement efficiency is remarkably improved.

Aspects of the method, apparatus, non-transitory computer readable medium, and system include estimating extrinsic camera parameters of a plurality of cameras based on detection of corners in a camera calibration arrangement by projecting an image of the detected corners onto an image plane of at least one of the plurality of cameras. The aspects further include incrementally tuning a location estimate for the detected corners based on pixels of the projected image, and adjusting the extrinsic camera parameters of the at least one of the plurality of cameras based on the tuned location estimate. The aspects further include generating a virtual image based on the pixels of the projected image mapped to a virtual image plane.

The invention relates to the technical field of building geometric reconstruction and crack detection, in particular to a three-dimensional reconstruction and damage detection method for a building in an intelligent driving environment, and the method comprises the steps: collecting a building video in a road environment during intelligent driving, and reconstructing a sparse point cloud of the building through an SfM algorithm; inputting each frame of time sequence image into a pre-trained semantic segmentation model to generate a crack highlight image; according to the sparse point cloud, initializing a corresponding parameterized 3D Gaussian ellipsoid, and optimizing parameters of the 3D Gaussian ellipsoid; mapping the 3D Gaussian ellipsoid to a two-dimensional image plane through a camera model, and generating a rendered image through a differentiable rendering algorithm; and comparing the rendered image with the crack highlight image, iteratively calculating the overall loss, and transmitting the overall loss through a back propagation gradient until a preset convergence condition is reached, thereby obtaining a final 3D model which simultaneously expresses the overall structure of the building and crack details. According to the invention, accurate environment data can be provided for the automatic driving system.

The invention relates to an indoor target positioning method and system based on machine vision and deep learning, and belongs to the technical field of target positioning. The method comprises the following steps: acquiring positioning data transmitted by a plurality of long-distance radio beacons deployed indoors in real time, and anchoring the positioning data to an indoor three-dimensional coordinate grid model node to obtain a global space reference framework; a real-time video image is obtained and preprocessed, and an LW-DETR algorithm is adopted to detect initial position information and size information of a target on an image plane; calculating a distance and an azimuth angle between a target and each beacon by combining beacon signal characteristics to obtain spatial position associated data; performing spatial registration and fusion on the multi-source data in combination with a global spatial reference frame to obtain target position information; and tracking and predicting the motion trail and speed information of the target, and integrating to obtain the final predicted position of the target. According to the method, the hardware collaborative dependence can be reduced, the anti-interference capability in an extreme environment is enhanced, and the indoor target positioning stability and precision are remarkably improved.

The invention provides a Bayesian fusion-based 4D millimeter wave radar point cloud enhancement and target detection method, and the method comprises the steps: 1, projecting a 4D millimeter wave radar point cloud to an image plane, carrying out the matching of an image instance segmentation result, and obtaining an image semantic category and image segmentation confidence; 2, constructing a class conditional probability density model based on the physical characteristics of the radar points; based on a Bayesian probability framework, carrying out weighted fusion on the image segmentation confidence and the class probability density to obtain fusion confidence; 3, taking the fusion confidence and semantic category codes as augmented features, and performing feature splicing on the augmented features and the 4D millimeter-wave radar point cloud to generate a semantic enhanced point cloud; and sampling the semantic enhanced point cloud by adopting a sampling method fusing confidence guidance, and inputting the sampled semantic enhanced point cloud into a detection network for training and reasoning. According to the method, the technical problems of unidirectional dependence, shallow geometric matching and insufficient utilization of radar physical characteristics in the existing image and radar point cloud fusion technology are solved.

An aspect of the disclosure provides An earth observation apparatus to be carried by a moving aerial platform or satellite for obtaining images of the surface of the earth, the apparatus comprising: an optical train having an optical field of view for imaging a region of the surface of the earth and being configured to form an image of the region at an image plane; an image sensor disposed at the image plane providing an imaging field of view; a view adjuster configured to control the optical train to: provide forward motion compensation for a stare time; and to displace the image, relative to the imaging field of view, in an across-track direction in a sequence of discrete displacement steps during each stare time; wherein the image sensor comprises a plurality of active areas, each comprising an area array detector and the active areas being spaced apart by inactive areas at the image plane wherein each active area captures a frame of image data for each discrete displacement step thereby to capture a plurality of frames for each discrete displacement step and the plurality of frames captured for each discrete displacement step are displaced in the across-track direction, relative to the imaging field of view, from the plurality of frames captured for the next discrete step.

The invention discloses a joint calibration method based on a rotary laser radar and a camera, and the method comprises the following steps: S1, carrying out the kinematic modeling of the camera, the laser radar and a motor rotating shaft based on a DH parameter method; s2, collecting synchronous laser radar point cloud data and camera image data of a motor rotating shaft at different angles; s3, processing the laser radar point cloud data; s4, processing the camera image data; s5, projecting the extracted laser point cloud depth continuous edge features to an image plane based on a current external parameter estimation value, and matching and associating the extracted laser point cloud depth continuous edge features with the image edge features extracted in the step S4; s6, constructing a projection residual equation about the external parameters according to the successfully matched feature pairs; and S7, the residual equation is linearized. According to the joint calibration method based on the rotary laser radar and the camera, high-precision and high-robustness automatic calibration is completed in an environment without an auxiliary target through kinematics modeling of a system and an advanced feature extraction matching technology. The invention further discloses a joint calibration device and system based on the rotary laser radar and the camera, and a storage medium.

The invention belongs to the technical field of surveying and mapping, and particularly relates to a multi-source space-time parameter online self-calibration method and system of a mobile measurement system. The method comprises the following steps: acquiring three-dimensional point cloud data, a two-dimensional image and state data and spatio-temporal data of a mobile measurement system; respectively extracting a first geometric feature of the corrected three-dimensional point cloud data and a second geometric feature of the two-dimensional image, and projecting the first geometric feature to a two-dimensional image plane to establish a target feature pair; constructing a joint optimization model of a unified space-time reference, and taking the target feature pair as a feature constraint to optimize the to-be-optimized state variable; and executing online optimization solution and parameter updating of the joint optimization model based on the sliding window. The method overcomes the defects that in the prior art, the offline calibration process is tedious, parameter dynamic drift cannot be adapted, the precision is not high due to space-time parameter separation solution in online calibration, and the robustness is poor in a complex scene.

In a biometric identification system, a transparent body can have a curved platen surface. The curved platen surface can be removably contacted by an anatomy of a user. The anatomy can have an anatomical pattern. An illumination source can illuminate the curved platen surface with an illumination beam. The illumination beam can propagate away from the curved platen surface as a return beam having a beam pattern shaped to correspond to the anatomical pattern. An imaging lens can focus the return beam to form a real image of the beam pattern at an image location. Field-curvature-correcting sensor optics can include a sensor. The sensor can sense the real image of the beam pattern at the image location. The field-curvature sensor optics can include a planar sensor and an image-flattener lens. The field-curvature sensor optics can include a curved, convex sensor.

Detection of a moving body region, generation of a high-density point cloud, and the like can be achieved in a preferable manner.A processing unit performs a process that forms a first depth image by projecting a LiDAR point cloud on a camera image plane, a process that forms a second depth image by using a camera image according to an optical flow, and a process that compares the first depth image and the second depth image to detect a moving body region or a non-moving body region. For example, the processing unit further performs a process that generates a high-density point cloud by projecting LiDAR point clouds corresponding to non-moving body regions of a plurality of frames on an identical coordinate system and sequentially merging the LiDAR point clouds.

The wide-angle lens comprises a first lens, a second lens, a third lens and a fourth lens which are sequentially arranged from an object plane to an image plane along an optical axis, the first lens is a glass spherical lens with negative focal power, the object side face of the first lens is a convex face, and the image side face of the first lens is a concave face. The second lens is a plastic aspheric 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 third lens is a plastic aspheric lens with positive focal power, the object side surface of the third lens is a concave surface, and the image side surface of the third lens is a convex surface. The fourth lens is a plastic aspheric lens with positive focal power, the object side surface of the fourth lens is a convex surface or a concave surface, and the image side surface of the fourth lens is a convex surface. The glass spherical surface and the plastic aspheric surface are matched and combined, the resolution is improved, the total axis length is smaller than 10.5 mm, the aperture F can reach 1.6, the field angle FOV can reach 183 degrees, the relative illumination can reach 50%, and the lens has the advantages of being large in aperture, ultra-large in field angle, small in size and high in illumination.

The invention provides a lens optical fiber array coupling system and a chromaticity measurement system and method, and belongs to the field of chromaticity measurement, and the lens optical fiber array coupling system comprises an imaging lens which is used for obtaining incident light of an object to be measured and guiding the incident light to an array optical fiber; the end face of the array optical fiber is located on the first image plane of the imaging lens, the normal of at least part of the optical fiber end face in the array optical fiber has a deflection angle relative to the normal of the first image plane, and the deflection angle is angle deflection performed by taking the main light ray of the light received by the corresponding optical fiber position as a target reference; and the imaging spectrometer is used for receiving the light information sent by the array optical fiber. By setting the deflection angle of the array optical fiber, the effective receiving area of the optical fiber is maximized, and the change of the transmission efficiency of light of different wavebands is avoided.

Waveguide combiners with multiple image planes are described herein. In an example, an apparatus includes a first optical element configured to receive light, a substrate having an input surface and an output surface and configured to propagate the light received by the first optical element along a propagation path within the substrate, and a second optical element configured to output the light propagated along the propagation path. The input surface of the substrate is coupled to the first optical element. The second optical element includes a first diffraction grating coupled to the output surface and characterized by a first focal distance and a second diffraction grating coupled to the first diffraction grating in a stacked arrangement and characterized by a second focal distance.

The invention discloses a teleconverter lens and an imaging system. The teleconverter lens comprises a first group, a second group and a third group which are sequentially arranged from an object plane to an image plane along an optical axis, the first group comprises a first lens, a second lens, a third lens, a fourth lens and a fifth lens; the second group comprises a sixth lens, a seventh lens, an eighth lens and a ninth lens; the third group comprises a tenth lens, an eleventh lens, a twelfth lens and a thirteenth lens; wherein the first lens, the third lens, the sixth lens, the seventh lens, the ninth lens, the eleventh lens, the twelfth lens and the thirteenth lens are all positive focal power lenses; the second lens, the fourth lens, the fifth lens, the eighth lens and the tenth lens are negative focal power lenses. According to the technical scheme, the number of the lenses in the optical system and the focal power matching mode are reasonably set, so that the imaging effect of the optical system can be improved, and the high-definition multiplying effect is achieved.