11 results about "Automatic target detection" patented technology

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 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.

This invention relates to the field of image data processing, specifically to an intelligent monitoring system for reservoir dam safety based on image recognition. The system includes: a data acquisition module for continuously acquiring video images and sensor time-series data at fixed intervals; a model training module for training a specialized AI algorithm model; a real-time inference module for automatic target detection; a data structuring module for outputting standardized recognition results; a data fusion and verification module for performing source fusion and cross-verification on data; a risk assessment module for analysis and outputting accurate risk assessment results; an early warning triggering module for triggering corresponding level early warnings based on the assessment results; a message push module for pushing messages to management personnel through multiple channels; and an event handling module for synchronously recording the entire process of early warning events, tracking the handling progress, and forming a closed-loop business process.

Disclosed embodiments may include a method for automatic object detection, identification, and tracking. This may include receiving video data from one or more sensors, processing the video data to detect one or more objects using a first group of one or more machine learning models, tracking characteristics of the one or more objects over time, classifying, using a second group of one or more machine learning models, each of the one or more objects based on the tracked characteristics, comparing each of the classifications to a database of known objects, predicting an estimated identity of each of the one or more objects based on the comparison, and predicting a future path of motion for each of the one or more objects based on the estimated identity and the tracked characteristics.

The application provides a cerebrospinal fluid cell microscopic image detection method and related device; multi-scale features of a cerebrospinal fluid cell microscopic image are extracted, and the multi-scale features are fused to obtain fused features; the fused features are aligned to obtain aligned features, the aligned features are subjected to frequency band modulation enhancement to obtain enhanced features, the enhanced features are fused to obtain unified features, and target enhanced features are obtained from the unified features according to a pre-set output size; detection is performed according to the target enhanced features, and a detection result is obtained; by introducing feature enhancement, different-scale effective information can be more fully fused, the features of a specified detection layer are subjected to targeted enhancement, the problems of missed detection and false detection that are prone to occur under a high-confidence requirement are improved, and the detection stability and reliability are improved; the cerebrospinal fluid cell morphological image input by a user can be subjected to automatic target detection, thereby reducing the burden of manual film reading and labeling and improving the efficiency of image analysis and result output.

Disclosed embodiments may include a method for automatic object detection, identification, and tracking. This may include receiving video data from one or more sensors, processing the video data to detect one or more objects using a first group of one or more machine learning models, tracking characteristics of the one or more objects over time, classifying, using a second group of one or more machine learning models, each of the one or more objects based on the tracked characteristics, comparing each of the classifications to a database of known objects, predicting an estimated identity of each of the one or more objects based on the comparison, and predicting a future path of motion for each of the one or more objects based on the estimated identity and the tracked characteristics.

This application discloses an automatic target detection method for live-line power distribution monitoring based on image recognition. The method includes: acquiring images of the work site and preprocessing them to obtain standardized images; extracting multi-scale feature maps using a shared backbone feature extraction network; detecting candidate regions for protective equipment and key human body points using a dual-branch network; constructing a human posture chain functionally associated with the protective equipment based on the key points; calculating an initial association score between the candidate regions and key points; analyzing the structural integrity of the protective equipment to obtain structural feature vectors; calculating a posture consistency index based on the posture chain; and finally fusing the above parameters to obtain functional association credibility, thereby determining the protective equipment wearing status. This method achieves accurate detection of the protective equipment wearing status, adapts to complex work scenarios, reduces false positives and false negatives, and improves the automation and intelligence level of safety monitoring for live-line power distribution operations.