WiMi Hologram Cloud Develops A Hybrid Augmented Intelligent Vision System

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider, announced the development of a hybrid augmented intelligence vision system based on computer vision technology, deep learning, and augmented reality. The system can automatically recognize, track and classify objects and provide real-time determination, prediction, and decision-making. In addition, the system can use augmented reality for information augmentation and interaction.

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The hybrid augmented intelligent vision system consists of four parts: data acquisition and pre-processing, feature extraction, model training, and real-time recognition and decision-making. The process is divided into two main stages: the perception of objective world information, recognition, and understanding; the other is the process of autonomous learning, reasoning, and decision-making.

Data acquisition and pre-processing: Target and environment data are collected by vision sensors. Multiple sensors are combined to acquire environmental information from various perspectives with rich data sources. This reduces the recognition errors caused by environmental changes in a single sensor, making the system’s sensing capability more comprehensive and improving the reliability and stability of the system. The collected data will be serially processed, denoised, and filtered, and other multi-level data processing to reduce interference and improve accuracy. And the information acquired by multiple sensors will be fused, multimodal information fusion, analysis, and judgment from numerous perspectives to improve the system’s perception capability and recognition accuracy. For example, image recognition can be combined with the distance information measured by LiDAR for more accurate target detection.

Feature extraction: The system uses convolutional neural network-based algorithms to extract features from the pre-processed data, and these extracted features will be used for training models and real-time recognition.

Model training: The system conducts training of machine learning algorithms based on the extracted features. The training data comes from various data collected in the actual scene. The system can continuously self-optimize through adaptive learning algorithms to improve the system’s adaptability to complex environments and intelligence levels.

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Real-time recognition and decision-making: The system analyzes and identifies the data collected in the actual scene in real-time and makes predictions and decisions according to the pre-trained machine learning model. Through autonomous decision-making and planning functions,  intelligent control is realized. The system can also be combined with augmented reality technology to realize the enhancement and guidance of human-computer interaction.

The hybrid augmented intelligent vision system can be applied to autonomous driving, smart manufacturing, medicine, and virtual reality. In autonomous driving, the system can automatically identify road signs, traffic signs, other vehicles, and pedestrians to achieve automatic driving and intelligent navigation and improve traffic safety. In intelligent manufacturing, the system can realize automated inspection and analysis of product manufacturing processes and quality to improve production efficiency and reduce product defects. The system can realize rapid recognition and analysis of physiological signals and human body data in the medical field, improve precision and accuracy, and thus provide better medical services. In virtual reality, the system can be combined with AR technology to achieve real-time replacement, enhancement, and virtual display of scenes and objects, providing users with a more immersive experience.

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