25 results about "Automatic target detection" patented technology

The present invention relates to the technical field of data simplification, in particular to a point cloud data simplification method for power line towers, and a system. The simplification method comprises: collecting point cloud data of power line towers, and carrying out data processing; performing data feature extraction on the processed data; classifying the point cloud data on the basis of the extracted features; and simplifying the classified point cloud data by means of using a point cloud simplification algorithm. The present invention introduces a deep learning technology to automatically learn and extract rich semantic features in point cloud data, thereby providing efficient feature representations for subsequent classification and simplification; by using extracted advanced semantic features and by means of a deep learning algorithm, the present invention can perform accurate classification and recognition on power line towers, thereby achieving automatic target detection and recognition; the present invention achieves intelligent simplification of point cloud data of power line towers by means of an intelligent merging strategy, so as to greatly reduce the data volume while reserving key features, thereby improving storage and transmission efficiency.

The invention provides a target distance estimation method and system based on optical characteristics of a holder, and the method comprises the steps: carrying out the automatic target detection and tracking shooting of a to-be-detected target at a current visual angle, and obtaining a target video of the to-be-detected target; meanwhile, the cradle head posture and the camera focal length are collected in real time; performing target detection according to the target video to obtain key feature points of the target; performing distortion correction on the key feature points through image coordinate mapping to form a unified image coordinate system; combining the image coordinate system with the attitude information of the holder, and mapping the image coordinate system to a three-dimensional virtual space to construct a virtual coordinate system; and calculating the target distance of the to-be-measured target according to the geometric position of the to-be-measured target in the virtual coordinate system. According to the invention, the target distance can be accurately estimated in a complex scene and at different visual angles based on the holder system with a dynamic attitude adjustment function, and the method has the technical effects of real-time performance and robustness.

The invention discloses a target detection method and device based on a visual language large model. The method comprises the following steps: constructing a target detection data set for training; analyzing the image by using area detection and target center point regression, and extracting center point coordinates of a target; and taking the central point as a prompt, and inputting a segmentation model to obtain an initial segmentation mask of the target. The initial segmentation result is verified by means of a visual language large model, the initial result is subjected to reasoning analysis by means of a text prompt function guide model, and false positive and adhesion targets are recognized; and removing target adhesion and false positive objects by using an improved watershed method. Compared with the prior art, the scheme of the invention effectively alleviates the problem that the existing automatic target detection method fails under the complex conditions of fuzzy target gray distribution, adhesion and the like, and can improve the reliability and accuracy of target detection.

The invention is suitable for the field of computer vision and artificial intelligence, and provides an automatic target detection and tracking method and system based on deep learning, and the method comprises the steps: obtaining a video stream and a scene structure diagram, extracting a target position bounding box, a category label and an appearance feature vector frame by frame, and calculating a behavior parameter and a priority score; generating a detection frame interval and a tracking algorithm complexity parameter according to the priority score, and initializing a special tracker to realize automatic tracking; when the target is lost, generating a space-time probability graph based on historical data and the scene structure graph; determining retrieval parameters according to the priority scores, and performing directional detection on the screened space-time regions; and after the target is retrieved, the identity is associated and parameters are redistributed to recover tracking. According to the method, through differentiated resource allocation, accurate loss prediction and directional retrieval, tracking precision, efficiency and robustness are considered, and the problems of target tracking interruption, resource waste and the like in a complex scene are effectively solved.

A simulation radar fort device suitable for automatic target detection and strike teaching adopts double-steering-engine independent driving, connecting rod transmission and a gear reduction mechanism to realize horizontal scanning motion of an ultrasonic radar and response aiming motion of a fort. The radar scanning mechanism converts steering engine rotation into sector scanning through a connecting rod; the fort aiming mechanism adopts a reduction gear set to reduce the output angle of a steering engine, accurate angle adjustment is achieved, and quick response from radar discovery to fort aiming is achieved. Through combination of machinery and control, the device not only realizes spatial decoupling of scanning and aiming, but also can be used as a teaching platform to visually demonstrate angle transmission, error compensation and triangulation ranging principles in target positioning, and is suitable for teaching practices of courses such as automation and mechatronics.

The application is suitable for the field of computer vision and artificial intelligence, and provides an automatic target detection and tracking method and system based on deep learning, which comprises the following steps: acquiring a video stream and a scene structure graph, extracting a target position bounding box, a category label and an appearance feature vector frame by frame, calculating a behavior parameter and a priority score; generating a detection frame interval and a tracking algorithm complexity parameter according to the priority score, and initializing a special tracker to realize automatic tracking; when the target is lost, generating a space-time probability graph based on historical data and the scene structure graph; determining a search parameter according to the priority score, and performing directional detection on the screened space-time region; after the target is found back, associating the identity and reassigning the parameter to restore the tracking. Through differentiated resource allocation, accurate loss prediction and directional recovery, the application takes into account the tracking accuracy, efficiency and robustness, and effectively solves the problems of target tracking interruption and resource waste in a complex scene.

The application discloses a kind of underwater imaging quality evaluation methods based on UTOD, belong to underwater detection and imaging technical field, this method includes obtaining triangle target feature, and the image obtained is processed by human eye visual system HVS simulation module;Underwater triangle direction discrimination threshold UTOD model is constructed, and UTOD model is used to distinguish the direction of triangle target;Contrast-sharpness comprehensive index UICS is constructed, including underwater sharpness index and underwater contrast index;Based on four options forced choice 4AFC test, the direction discrimination correct probability corresponding to different UICS values is counted, and the relationship curve of UICS and correct probability is fitted by psychometric function Weibull;Triangle target of different size is repeatedly tested, and UTOD curve is generated.The application realizes automatic target detection and direction identification, and improves the accuracy, stability and objectivity of evaluation index.

The application discloses an unmanned aerial vehicle target detection optimization method and system based on privileged information knowledge migration, which comprises the following steps: analyzing a label file of an unmanned aerial vehicle target detection data set, extracting categories, boundary boxes and instance segmentation labels, generating a three-channel privileged information graph aligned with RGB images in the unmanned aerial vehicle target detection data set at a pixel level by using a privileged information processing method; splicing the RGB images and the three-channel privileged information graph in the channel dimension to form a six-channel image; constructing a teacher model and training the same; freezing the parameters of the trained teacher model, training a student model based on the RGB images by using a knowledge distillation loss function, and obtaining a trained student model; and automatically detecting a target of an RGB image to be detected by using the trained student model, and outputting a target category and a boundary box position. The application avoids dependence on complex multi-modal hardware, is simple and low in cost, and improves target detection performance.

The ice particle abrasive jet flow generation system of the present application comprises an ice particle preparation device, a high-pressure water jet device and an ice particle abrasive jet flow control device, the ice particle preparation device comprises an ice particle preparation cavity, a liquid nitrogen injector and a dye water injector, the ice particle abrasive jet flow control device comprises a dynamic monitoring cavity, a camera, a PTU controller and an interactive platform, the ice particle preparation device and the high-pressure water jet device respectively deliver ice particles and high-pressure jet water into the dynamic monitoring cavity, the ice particles and the high-pressure jet water can be mixed, forming ice particle abrasive jet water for deep sea mining, and the ice particles as abrasives can avoid causing pollution of seawater, in addition, after collecting image data through the camera, the optimized YoLo v11 algorithm is introduced for automatic target detection and capture of the ice particles in the abrasive jet flow in the dynamic monitoring cavity, and according to the development of the abrasive jet flow and the evolution process of the ice particles, the flow and pressure of the liquid nitrogen injector and the dye water injector are feedback adjusted to realize precise control of the ice particle abrasive jet flow.

This invention discloses a model-assisted semi-automatic target detection and annotation method for power line inspection, belonging to the fields of machine vision and power line inspection. The method includes: obtaining a basic annotation set using the power transmission and distribution images to be annotated; training a detector based on a reference annotation set, the basic annotation set, and the corresponding images to obtain a predicted annotation set for the images to be annotated; comparing the differences between the annotations in the basic annotation set and the predicted annotation set according to the intersection-union ratio (IUU) calculation results and target category determination, and classifying them into different annotation sets; sequentially determining whether each annotation in the annotation set is acceptable and correcting the basic annotation set; further training the detector until convergence; and updating the reference annotation set and the corresponding images. This invention combines model prediction with manual judgment to achieve efficient acquisition of high-quality target detection and annotation data.