The development trend of machine vision in the field of automation is increasingly hot and exciting. With the continuous advancement of technology and the expansion of applications, machine vision is becoming an important part of automation solutions.

Here are some of the trends in machine vision in the field of automation:

Deep Learning and Neural networks: Deep learning and neural networks are increasingly used in machine vision. By training using large-scale data sets, deep learning models can achieve tasks such as highly accurate image classification, object detection, and segmentation. The continuous development and improvement of neural networks will further improve the performance and efficiency of machine vision systems.

Real-time performance and speed: As automation systems become more demanding, machine vision requires real-time performance and high speed. To achieve this, new algorithms and techniques are proposed, including hardware acceleration and the use of dedicated processors to increase the speed of image processing and analysis.

3D vision and spatial perception: Traditional machine vision is mainly concerned with 2D image processing and analysis, but in the field of automation, it is very important for the three-dimensional perception and spatial positioning of objects. Therefore, the development of 3D vision and depth perception technology will become an important direction in the future. For example, based on technologies such as structured light, time flight and stereo vision, accurate three-dimensional reconstruction and position detection of objects can be achieved.

Multimodal fusion: Multimodal fusion refers to the integration and analysis of information from different sensors and data sources. Machine vision systems can combine data from other sensors (such as liDAR, infrared cameras, etc.) to improve the accuracy of perception and decision making. Through multi-modal fusion, more comprehensive and accurate environmental perception and analysis can be achieved.

Augmented Reality and virtual reality: The use of augmented reality (AR) and virtual reality (VR) technologies in machine vision is also growing rapidly. AR and VR can provide an intuitive interface and interaction for automated systems, helping operators better understand and control automated processes. These technologies can be used for applications such as training, operational guidance, and fault diagnosis.

Autonomous decision making and feedback control: Machine vision systems can not only perform image processing and analysis, but also integrate with other automation components to achieve autonomous decision making and feedback control. By combining with machine learning and control algorithms, machine vision systems can make decisions based on real-time data and environmental changes, enabling higher levels of automation and intelligence.

Edge computing and cloud platforms: With the development of the Internet of Things, machine vision systems need to process and analyze large amounts of images and data. The rise of edge computing and cloud platforms provides powerful computing and storage capabilities for machine vision. Edge computing enables real-time performance and low-latency image processing, while cloud platforms can provide efficient data management and analysis.

Adaptability and flexibility: The needs of the automation field are diverse, and the machine vision system needs to have a certain degree of adaptability and flexibility to cope with different application scenarios and tasks. For example, through automatic learning and transfer learning techniques, machine vision systems can adapt and recognize different objects and scenes in different environments.

Trends in machine vision automation include advances in deep learning and neural networks, real-time performance and speed, 3D vision and spatial perception, multimodal fusion, augmented and virtual reality, autonomous decision making and feedback control, edge computing and cloud platforms, and adaptability and flexibility. These trends will further drive innovation and application of machine vision technology, bringing more possibilities and opportunities to the field of automation.