491 results about "Monocular vision" patented technology

The invention discloses an end-to-end monocular visual odometer method fusing space-time semantic information. According to the method, continuous image sequence frames are collected through a color monocular camera, and a multi-information fusion end-to-end deep learning framework is constructed; a heterogeneous training domain is adopted to set various data set course sharing parameter fusion training, continuous image sequences are input, and the end-to-end deep learning framework is dynamically coupled with hidden state feature vectors output historically, so that a feature mapping relation of time sequence perception is formed; and interpretable feature decoupling of the static background elements and the dynamic entity objects in the scene is realized. After iterative feature fusion, the system outputs sparse depth and camera motion poses which conform to scene geometric constraints, so that a camera trajectory estimation model with high robustness and strong generalization ability in a complex environment is constructed. According to the method, the positioning precision and stability of the monocular vision odometer are remarkably improved.

The invention discloses a field personnel positioning method cooperating with multi-view images and spatial vectors of an unmanned aerial vehicle, and the method comprises the steps: detecting a personnel target in real time through a pre-trained lightweight model, obtaining a surrounding radius in combination with a self-adaptive surrounding radius algorithm, and achieving the collection of a multi-view image sequence; carrying out quality quantitative analysis on the multi-view image sequence by adopting a multi-source fixed weight quality evaluation algorithm; a DBOF coordinate system is established, and coordinate conversion is completed; extracting high-frequency matching points based on a cross-image bidirectional matching mechanism, and performing composite distortion correction on the high-frequency matching points; carrying out weighted least square method solution on the multi-view observation equation set to generate a three-dimensional space coordinate of the target point in a DBOF coordinate system; and introducing a robust iterative optimization strategy to eliminate abnormal values, and outputting geographic coordinates. On the basis of low-cost monocular vision, the real-time performance and robustness of personnel positioning in a complex field environment are improved through a dynamic surround sampling and geometric resolving mechanism, and spatial data support is provided for field rescue.

The invention relates to the technical field of settlement observation, in particular to a non-contact monocular vision high-precision automatic settlement monitoring method, which comprises the following steps: firstly, performing target monitoring by using a YOLOv10s model, improving the real-time reasoning speed and recognition precision of target recognition, and solving the target recognition problem under a complex background; secondly, a digital image processing technology and an edge-based least square ellipse fitting algorithm are combined to realize accurate positioning of the target; thirdly, deriving based on a camera imaging principle, and introducing an improved world coordinate calculation method to solve world coordinates corresponding to the pixel coordinates so as to obtain three-dimensional world coordinates; and finally, calculating a settlement value of the monitoring area by taking the first frame image as a reference, predicting a future settlement value by using a deep learning model, and making an early warning for structure safety in advance. According to the invention, the installation demand and the calculation overhead are reduced, and the settlement monitoring precision is improved.

The invention relates to a tunnel surrounding rock three-dimensional deformation monitoring system integrating monocular vision and millimeter wave radar. The tunnel surrounding rock three-dimensional deformation monitoring system comprises in-tunnel sensing equipment, a tunnel opening terminal, a database and application server and a remote monitoring end. The invention also relates to a tunnel surrounding rock three-dimensional deformation monitoring method fusing monocular vision and millimeter wave radar. The method comprises the following steps: S1, setting a monitoring target; s2, multi-modal data acquisition is carried out; s3, visual and radar target detection; s4, performing visual and radar heterogeneous data matching; s5, solving pixel displacement and radial displacement; s6, calculating the three-dimensional deformation of the surrounding rock; and S7, alarm judgment and result output. According to the invention, the precision, the real-time performance and the automation level of tunnel surrounding rock deformation monitoring can be obviously improved, and the method is especially suitable for scenes with complex environment and high safety risk in the early stage of tunnel excavation; the invention aims to provide an efficient, accurate and automatic monitoring means capable of adapting to the early stage of excavation for tunnel surrounding rock deformation.

The invention belongs to the technical field of computer vision, and discloses a human motion capture method based on mask perceptual graph convolution and skeleton prior, and the method comprises the steps: obtaining an input image and a corresponding human mask; extracting features of the image and the mask and coding; constructing a mask perception graph convolutional network, constructing an adjacent matrix of the graph convolutional network by using mask information, and applying mask constraint loss to enhance the expression ability of the human body region features of the image; a skeleton prior decoupling network is constructed, skeleton and vertex information of an SMPL model is used as prior, a cross attention mechanism is combined, and multi-modal data enhancement of SMPL skeleton node features is guided through image features; and finally outputting a three-dimensional joint position coordinate and a shape grid vertex coordinate. According to the method, the local feature consistency is enhanced through mask perception image convolution, the geometric consistency is improved in combination with skeleton prior, and the precision of three-dimensional human body motion capture under monocular vision is effectively improved.

The invention provides a crack three-way measurement research method and system based on multi-coordinate system fusion, and relates to the technical field of earth and rockfill dam monitoring. The method comprises the following steps: shooting concentric circle target images at multiple angles through a monocular camera, extracting feature points through preprocessing, and correcting projection distortion of non-parallel shooting; establishing a mapping relation between pixels and a three-dimensional coordinate system, and optimizing camera pose parameters; reconstructing a three-dimensional coordinate point cloud based on the multi-view data, screening outliers and fitting plane optimization data; and registering the three-dimensional point cloud to a world coordinate system through a coordinate system conversion algorithm, and comparing two measurement results to calculate the three-way displacement of the crack. According to the method, concentric circle target constraint feature precision is utilized, projection correction and plane fitting are combined to compensate monocular vision depth deficiency, high-precision displacement detection is achieved, and engineering monitoring requirements are met.

The invention discloses a visual language navigation method and system based on a monocular camera and a language instruction, and the method comprises the steps: S1, feature field construction: employing a 3DGS to construct a 3DGS feature field by employing visual information obtained by a monocular RGB-D camera; s2, implicit partial complementation: inferring the representation of a missing region through a context feature relationship to generate a complete feature map; s3, waypoint prediction: generating an aerial view feature map based on the 3DGS feature field, and predicting nearby navigable waypoints through a waypoint predictor; s4, based on active perception of uncertainty, when the robot is uncertain in navigation decision, more visual information is acquired and missing visual information is supplemented by rotating a camera; and S5, constructing a topological map based on the constructed panoramic feature map and the predicted path points, and carrying out navigation decision making by using a panoramic visual language navigation planning model in combination with a language instruction. The problem that monocular vision information is incomplete is effectively solved, and the success rate and efficiency of navigation are remarkably improved.

The invention discloses a monocular vision depth estimation method and system suitable for a night scene, and aims to overcome the limitation problem of a basic model. According to the method, daytime images are synthesized into highly vivid night images, then a daytime-night data set is formed, and a low-light-parameter efficient fine tuning strategy is provided for adjusting a pre-trained basic model to adapt to a night scene. Finally, the method not only can effectively cope with challenges in a night scene, but also realizes robust depth estimation performance, thereby encouraging further exploration and innovation in the field.

The invention belongs to the technical field of ship management, and provides a combined intelligent ship water diversion system integrating differential positioning, a laser radar and a camera. In the shore end RTK-inertial navigation depth fusion positioning module, extended Kalman filtering fusion is carried out on data acquired by shipborne equipment, so that continuity of accurate positioning is realized; the construction of a color high-precision map is realized through a laser radar-monocular vision color high-precision modeling module, details such as wharf fender materials and signboard characters can be better distinguished, and the problem of shoreline modeling defects is solved; visualization of obstacles is achieved through the multi-scene dynamic obstacle detection and fusion module, meanwhile, through cooperation of multi-source data, the precision and stability of ship positioning are greatly improved, higher precision and reliability are achieved, and the precision of a guide instruction is improved; through cooperation of the 5G private network and the visualization module, the data transmission efficiency is improved, the timeliness is improved, and transmission delay can be avoided.

The invention discloses a robot joint layer sensing system fusing ToF and monocular vision, and belongs to the field of robot sensing, the system adopts an Eye-in-Hand architecture, and a ToF camera and an RGB camera are integrated; the quality of the depth map is improved by combining bilateral filtering and Kalman filtering; the six-degree-of-freedom pose estimation of the target is realized based on SURF feature matching and KD-Tree accelerated ICP registration; and planning a grabbing path in combination with an improved RRT algorithm and hierarchical collision detection. According to the robot joint layer sensing system fusing ToF and monocular vision provided by the invention, spatial alignment and fusion are carried out on two types of visual information, respective limitations are made up, and the robustness and precision of the whole sensing system are improved; in a single-arm and double-arm grabbing experiment, the positioning precision of the system reaches + / -3mm, and the dynamic scene grabbing success rate is high. The method is suitable for the fields of industrial automation, logistics sorting and the like.

The invention belongs to the technical field of robot positioning and three-dimensional mapping, and discloses a high-precision map reconstruction method and system based on monocular vision, a medium and equipment. Scene image data are acquired through a monocular image acquisition module, after feature extraction and matching are performed on each frame of image, attitude information acquired by an inertial measurement unit is fused, and pose calculation of a robot is completed through a sparse vision SLAM system; meanwhile, an image dense depth map is generated by a monocular depth estimation model based on an attention mechanism, and the image dense depth map is converted into a single-frame color dense point cloud in combination with image RGB color information. According to the invention, a loose coupling fusion strategy is adopted to carry out spatial registration on a single-frame colored dense point cloud and a robot pose at a corresponding moment, multi-frame data fusion is completed through point cloud splicing and optimization, and a globally consistent three-dimensional dense point cloud map is constructed to realize scene modeling. The method has the characteristics of high robustness and high reconstruction precision, and can be effectively applied to three-dimensional map construction in an outdoor complex environment.

The invention belongs to the related technical field of bridge hoisting, and discloses a monocular vision-based bridge hoisting pose real-time measurement method and system, and the method comprises the steps: calibrating the relative position between an optical calibration plate and the upper surface of a prefabricated bridge before hoisting operation: shooting a plurality of calibration images of the upper surface of the prefabricated bridge; obtaining a calibration plate coordinate system and an image coordinate system projection transformation matrix of any image; calculating projection coordinates of the vertex of the upper surface of the prefabricated bridge in the calibration plate coordinate system; on the basis of the multiple calibration images, shape features of the upper surface of the prefabricated bridge are obtained, and the relative position relation is calculated; and measurement in the hoisting process: obtaining the pose of the prefabricated bridge according to the real-time image and the relative position relation obtained through calibration. According to the method, the relative position mapping relation between the optical calibration plate and the prefabricated bridge is accurately established by utilizing multi-view image analysis and geometric constraint calibration before hoisting operation, so that the pose of the prefabricated bridge is accurately measured in real time on the basis of monocular vision.

The invention belongs to the technical field of conveyor coal flow monitoring, and aims at solving the problem that an existing coal flow monitoring method is insufficient in precision. According to the belt conveyor coal flow monitoring method based on the inspection robot and the monocular vision, coal flow images are dynamically collected through the inspection robot, accurate segmentation of a coal flow area is achieved through a deep learning algorithm, and a coal flow three-dimensional point cloud is reconstructed in combination with a monocular depth estimation technology; and finally, qualitative and quantitative analysis of the coal flow is realized. Compared with a traditional method, the method has the advantages of non-contact measurement, full conveying belt coverage, high calculation efficiency, adaptability to complex environments and the like, and reliable data support can be provided for a coal mine intelligent transportation system.

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 relates to the technical field of positioning of planar robots, in particular to a wheel type odometer and monocular vision fused SLAM positioning system with three driven omnidirectional wheel sensors. According to the method, firstly, internal reference calibration is conducted on a sensor, then the pose of a vehicle body is calculated according to data of a wheel type odometer sensor, joint initialization of a wheel type odometer and a monocular camera is conducted after timestamp synchronization is completed, and the pose of the vehicle body is calculated through vision-wheel type odometer tight coupling nonlinear sliding window optimization. A dynamic plane constraint self-adaptive method based on local geometric features and meta learning is added, a mixed strategy based on motion component decomposition and residual entropy dynamic adjustment is designed, back-end optimization is carried out, and whether a loop exists or not is detected to optimize a track and reduce errors.

The invention relates to a multi-modal perception fusion target following method and system. The method comprises the following steps: adopting an improved KCF algorithm to realize a closed-loop process of target tracking, multi-scale space construction, feature fusion, response calculation, optimal scale decision and model updating; designing a four-level shielding processing mechanism fusing motion prediction and depth verification, and realizing tracking recovery in a shielding scene through shielding judgment, motion prediction, fine search and template protection; constructing a target distance mapping function fusing geometric distortion correction and attitude compensation, and realizing high-precision distance estimation based on monocular vision; laser radar point cloud information is integrated, an obstacle threat degree model is constructed, and cooperative path planning of following and obstacle avoidance is realized in combination with an improved TEB algorithm; and designing a linear velocity control law and an angular velocity control law based on the distance deviation and the azimuth angle deviation, and driving the robot to complete target following motion. According to the invention, high-precision and robust following of the robot to the target can be realized.

The invention belongs to the technical field of visual angle follow-up vehicle panoramic look-around, and particularly relates to a visual angle follow-up vehicle panoramic look-around system based on dynamic prediction, which is characterized in that monocular vision and laser radar time sequence data are fused, and a dense and stable dynamic target depth sequence is generated through dynamic target initial perception, cross-modal time sequence alignment and inter-frame depth correlation modeling; through environment interaction modeling and target behavior intention prediction double-branch parallel processing, a dynamic self-adaptive accurate track is output in combination with a weighted fusion strategy, and the prediction weight dynamic adjustment requirement of a complex motion scene is adapted; after a virtual view angle image is preliminarily synthesized based on the predicted trajectory, deviation detection and coordinate dynamic correction are realized through inter-frame target consistency verification, and artifact area positioning and pixel-level restoration are synchronously completed; multi-modal preprocessing and algorithm tasks are deployed according to hardware, and a model lightweight optimization and inter-frame prediction result multiplexing strategy is combined, so that low-delay operation of the system in a high-resolution panoramic image output scene is ensured.

The invention relates to a monocular vision-based map construction method, system and equipment. The monocular vision-based map construction method comprises the following steps of: acquiring a current frame image, a key frame image and a sparse point cloud map; extracting image information of the current frame image, and determining a first camera attitude; determining a prior depth map of the current frame image on the key frame image; constructing a first Gaussian model according to the first camera attitude, the image information, the sparse point cloud map, the prior depth map and the current frame image, and optimizing the first camera attitude to obtain a second camera attitude; wherein the Gaussian model comprises Gaussian distribution of a plurality of scenes; and obtaining a second Gaussian model according to the sparse point cloud map, the prior depth map, the image information and the second camera attitude, and adding the second Gaussian model to the Gaussian map according to the second camera attitude. The method can improve the positioning and map building precision, and is applied to the technical field of computer vision and robots.

The invention provides a monocular vision and sparse IMU rehabilitation action whole body posture estimation method and system, and the method comprises the steps: synchronously collecting video data and inertial data of human body rehabilitation actions through a monocular RGB camera and a plurality of IMUs, and cutting and zooming an image to a preset resolution; extracting a key point thermodynamic diagram from continuous N frames of images by using a sliding window and a residual neural network, and calculating 2D key point pixel coordinates of each frame; splicing the N frames of 2D key point pixel coordinates, the rotation matrix of the IMU and the acceleration signal into an input sequence; cross-modal time sequence modeling is carried out on an input sequence through time Transform, and after high-dimensional features are extracted, weighted average is carried out through a convolutional layer, and 3D relative key point coordinates of the last frame are output through a regression head. According to the method, by fusing monocular vision and sparse IMU cross-modal data, the problem of visual information loss caused by limb self-shielding is effectively solved, and the defect that a traditional pure vision method is insufficient in precision in rehabilitation actions is overcome.

This invention discloses a lane departure warning method and system for use in nighttime scenarios. Developed primarily based on monocular vision and deep learning methods, the system includes: using an on-board camera to capture images of the road ahead and using the Res2Net50-VHA network to detect lane marking feature points on both sides of the current lane; using a Kalman filter to track lane marking feature points in preceding and following frame images; applying a combined fitting method of quadratic polynomials and linear equations to fit the upper and lower portions of the lane markings; calculating relevant parameters, including road curvature, lane centerline equation, vehicle lateral distance from the lane centerline, and yaw angle; and combining these parameters to determine the vehicle's driving state and implement a lane departure warning strategy. The algorithm employed in this invention boasts high recognition accuracy, high computational efficiency, and strong anti-interference capabilities, and the program outputs intuitive, real-time visualizations.

The invention discloses a rapid robust monocular vision inertial positioning method and system. The method comprises the following steps: configuring IMU and monocular camera sensor parameters; preprocessing the IMU data; iMU attitude initialization is carried out; image features of the monocular camera are extracted, abnormal matching points are removed, and IMU pre-integration is carried out; monocular vision initialization is carried out; odometer attitude optimization: judging whether the feature points are mismatched or not based on an IMU pre-integration result, constructing a feature point re-projection error jacobian matrix, and performing blocking and diagonalization processing on the re-projection error jacobian matrix to respectively optimize inverse depths and image frame attitudes of the feature points; selecting a key frame based on a key frame identification rule after the current frame attitude update is completed; and judging according to the current frame, and removing a certain frame in the sliding window to reserve a sliding window space for adding the latest frame. According to the method, the inverse depth of the feature points and the image frame attitude are optimized respectively based on the counterweight projection error Jacobian matrix partitioning and diagonalization processing, and the calculation complexity is reduced.

The invention relates to the technical field of multi-person posture estimation, in particular to an interactive scene data synthesis and key point visibility updating algorithm based on monocular vision, which comprises the following steps of: 1, arranging single-person picture data under monocular shooting vision, processing an input single-person picture under monocular shooting vision by using a GrondingDINO visual language open type target detection model, and obtaining a target detection result; and the Person is used as a retrieval keyword to identify a character individual in the picture. Through fine processing of the steps, especially introduction of target matching, target position adjustment and key point visibility updating methods, the quality of the synthesized image is greatly improved, the fusion degree of the target and the background is optimized through accurate matching and natural target pasting, and the image quality is improved. The space consistency and the visual naturalness of the synthetic image are enhanced, the problem of overfitting is effectively avoided through the improvement, the diversity of training data is enhanced, and therefore the generalization ability of the model is improved.

The invention provides an oil taking port positioning method and system based on monocular vision and laser positioning. The method comprises the steps that laser point cloud data and a monocular image near an oil taking port are acquired; constructing a three-dimensional model of the oil taking port based on the laser point cloud data, and projecting the three-dimensional model of the oil taking port into a two-dimensional slice image according to an acquisition position label of the monocular camera; matching the two-dimensional slice image with the monocular image to determine the two-dimensional feature position of the oil extraction port; calculating the feature distance between the oil taking port and the camera based on the matched two-dimensional feature position of the oil taking port and the focal length label and the size label of the monocular image; on the basis of the position parameters of the oil taking port in the laser point cloud data, the feature distance is combined, and initial three-dimensional coordinates of the oil taking port are generated; and unifying the pixel coordinates of the monocular vision and the laser point cloud coordinates into the same coordinate system, and carrying out preliminary three-dimensional coordinate data fusion to obtain the final three-dimensional coordinates of the oil extraction port. The positioning precision of the transformer oil taking robot on the oil taking port is improved.

The invention relates to a visual obstacle avoidance method and device, and the method comprises the steps: S1, arranging a monocular camera on a moving carrier, and enabling an image collected by the monocular camera to comprise a preset range at the front lower part of the moving carrier; s2, collecting a sample image through the monocular camera and calibrating the sample image to obtain a relational expression of the distance between a target in the image and the mobile carrier; s3, acquiring a formal image through the monocular camera, and identifying an obstacle in the formal image; and S4, determining the distance between the obstacle and the mobile carrier through the relational expression. According to the invention, the monocular camera is arranged on the mobile carrier and only collects the image in the limited range of the front lower part, and the distance information with the target obstacle can be obtained from the collected image, so that the obstacle avoidance judgment is realized, and compared with the existing monocular vision that the depth-of-field information cannot be obtained, so that the obstacle distance cannot be accurately judged, and the obstacle avoidance accuracy is improved. Good application prospects are realized.

The invention provides a three-dimensional target detection method based on monocular vision, and belongs to the field of three-dimensional target detection for automatic driving, and the method comprises the following steps: designing an improved backbone network Faster Net +, carrying out image feature extraction, constructing a multi-dimensional feature adaptive fusion module, and adaptively selecting and fusing high-dimensional and low-dimensional features; a feature enhancement attention module is introduced on a multi-scale feature layer extracted by the feature pyramid network, interaction between feature channels and correlation between different coordinates are considered at the same time, a target area is highlighted, and irrelevant background information is inhibited; introducing an effective target detection head network according to the three-dimensional target detection network model, and performing parameter learning on the network model by using a training data set; after training is finished, a test image is input, and the positions and categories of different types of targets in the image are determined by using the three-dimensional target detection network model.

The invention discloses a monocular vision image deblurring method for a low-altitude miniature unmanned aerial vehicle, which belongs to the field of miniature unmanned aerial vehicle image processing, and comprises the following steps: preprocessing a blurred image to obtain a preprocessed blurred image and a blurred kernel estimation result; constructing a deep reinforcement learning model based on an attention mechanism and memory playback, and dividing the preprocessed blurred image into overlapped sub-image blocks to obtain a plurality of blurred image blocks and corresponding blurred kernel blocks; inputting the blurred image blocks and the corresponding blurred kernel blocks into a deep reinforcement learning model, performing channel-level fusion on the attention weight map, the blurred image blocks and the blurred kernel blocks, performing deblurring processing according to a dual-network structure, and outputting deblurred sub-blocks consistent with the input size; and based on the deblurred sub-blocks, eliminating the discontinuity of the boundaries of the sub-blocks through weighted fusion of overlapped regions to obtain a reconstructed image. According to the method, the problem of edge fault easily occurring in a traditional blocking method is solved, and the processing speed and the image quality in a complex scene are both considered.

The invention discloses a PCB soldering paste printing three-dimensional defect detection system based on multispectral imaging, and relates to the field of machine vision detection, and the system comprises a multispectral image acquisition module, a four-channel industrial camera based on RGB and near infrared, and a tunable LED light source array; acquiring reflection characteristic data under different penetration depths through a wavelength switching mechanism; the motion control trigger module is used for realizing positioning based on an XYZ three-axis objective table driven by a servo motor in cooperation with feedback of an encoder; a pulse width modulation signal is adopted to coordinate the moving speed of a camera shutter and a platform; according to the method, RGB and NIR four-channel imaging is combined with Beer-Lambert law modeling, so that double verification of material component quantitative analysis and three-dimensional shape reconstruction is realized, and limitation of monocular vision is avoided; structured light projection and binocular stereo matching technologies are adopted, cross-frame data alignment is realized in cooperation with an ICP algorithm, and detail defects can be better detected.

The invention discloses an intelligent bridge assembling method. The method comprises the following steps that a fixed target and a movable target are installed on a pier column and a cover beam correspondingly; measuring equipment is arranged at the measuring position, and the distance D1 from the measuring position to the horizontal direction of the fixed target is obtained; obtaining the pixel width P1 of the fixed target image, and dividing the actual width size of the fixed target by the pixel width P1 to obtain the conversion coefficients of the fixed target in the X and Z directions; moving the cover beam to obtain the pixel width P2 of the moving target; according to the D1, the P1 and the P2, the real-time distance D2 between the moving target and the measuring position in the horizontal direction is obtained; establishing a three-dimensional coordinate system by taking the central point of the fixed target as an original point, and acquiring three-dimensional coarse coordinates of the movable target; the accurate three-dimensional coordinates of the moving target are obtained through depth coordinate replacement, and assembling is carried out according to the accurate three-dimensional coordinates. The laser ranging technology is fused to make up the defect that the monocular vision technology is low in precision in the horizontal direction, and high-precision bridge assembling is completed.

The invention discloses a compact stripe monocular three-dimensional measurement system and method based on a mechanical grating, and belongs to the technical field of measurement sensing. The invention aims to solve the problems that the existing three-dimensional measurement technology depends on expensive active devices, the structure is complex, and single-frame high-density fringe projection and dynamic measurement are difficult to realize. The system comprises a dense fringe projection device, a monocular acquisition module and a processing unit. The compact stripe projection device is composed of an active light source, a mechanical grating and an optical lens, and is used for projecting a single-frame compact stripe formed by the mechanical grating to the surface of an object to be measured. And the monocular acquisition module is used for acquiring a deformed stripe image which is subjected to height modulation by the surface of an object. And the processing unit is used for synchronously controlling projection and acquisition and resolving the acquired single-frame deformed stripe image based on a pre-established system calibration model so as to reconstruct a three-dimensional point cloud coordinate of the object. The passive mechanical grating is adopted to replace traditional digital projection elements such as DLP, MEMS and DOE, and monocular vision and a single-frame reconstruction algorithm are combined, so that the system has the advantages of low cost, small size, single camera and the like, and has wide application prospects in the fields of miniaturized 3D imaging modules, extremely small endoscopes, portable 3D imaging equipment, dynamic 3D imaging and the like.

The invention provides a 3D anti-collision detection method and system based on monocular vision, and the method comprises the steps: obtaining the image data of a front environment collected by a vehicle-mounted monocular camera in real time, and carrying out the preprocessing of the image data, and obtaining the standardized image data; generating an optimized absolute depth map by fusing a first depth estimation result and a second depth estimation result based on the standardized image data; detecting a 3D obstacle based on the optimized absolute depth map and scene semantic information to obtain related information of at least one target obstacle; and performing collision risk assessment according to the related information, and triggering early warning when the risk meets an early warning condition. The invention provides a high-precision and high-robustness monocular 3D anti-collision detection scheme, monocular depth estimation based on deep learning, semantic segmentation and a dynamic safety model are combined, and an end-to-end anti-collision system with physical significance is formed.