Machine vision technology refers to the technology that uses computers and various sensors and other equipment to simulate the ability of the human visual system to realize the understanding, interpretation and processing of images and videos. With the continuous development of computer vision and artificial intelligence technology, machine vision technology has been widely used in various fields, and has shown the following development status and trends:
1. Algorithm optimization and deep learning: with With the rise of deep learning algorithms, the research and application of machine vision technology increasingly rely on the training of large-scale data and the optimization of deep neural networks. Deep learning algorithms have achieved remarkable results in target detection, image classification, image segmentation and other fields, and have become the core of machine vision technology.
2. Multi-modal fusion: Machine vision technology is no longer limited to single image information, but is increasingly fused with other sensor data (such as sound, text, etc.). Multimodal fusion can provide more comprehensive and accurate information, helping to improve the performance and robustness of machine vision systems.
3. Real-time applications and embedded systems: With the improvement of computing power and the popularization of low-cost computing devices, machine vision technology is gradually applied to real-time systems and embedded devices. For example, in fields such as autonomous driving, industrial automation, and smart homes, machine vision technology can process image and video data in real time, providing intelligent decision-making and control capabilities for the system.
4. Combination of edge computing and cloud computing: Machine vision technology usually requires a large amount of computing resources for image processing and model training, and the combination of edge computing and cloud computing can achieve flexible deployment and distribution of computing resources. type processing. Edge computing pushes computing power to terminal devices to achieve low-latency and high-efficiency image processing; cloud computing provides powerful computing and storage capabilities to support large-scale data processing and analysis.
5. Expansion of application fields: The application fields of machine vision technology are becoming more and more extensive, covering many fields such as medical imaging, drones, intelligent transportation, security monitoring, and human-computer interaction. In the future, machine vision technology is expected to play an important role in more fields, such as agriculture, environmental protection, logistics, etc.
In short, with the development of artificial intelligence technology and the increasing demand for applications, machine vision technology will continue to make major breakthroughs and progress, and play an increasingly important role in various fields. At the same time, machine vision technology is also facing challenges, such as data privacy and security, algorithm interpretability and other issues that need further research and solution.