What are the characteristics of machine vision?
1. Machine vision is a comprehensive technology that includes digital image processing, mechanical engineering, control, electric lighting, optical imaging, sensor technology, analog and digital video, computer hardware, and human-machine interface technology. These technologies are parallel in machine vision and must be applied in a coordinated manner to form a successful industrial machine vision application system.
2. Machine vision places greater emphasis on practicality, requiring the ability to adapt to harsh environments in industrial production, reasonable cost-effectiveness, universal industrial interfaces, operability by ordinary workers, high fault tolerance and safety, and the ability not to damage industrial products. It must also have strong versatility and portability.
3. For machine vision technicians, it is not only necessary to have the professional ability to study mathematical theories and develop computer software, but also to have the comprehensive ability of optics, mechanics and electronics integration.
4. Machine vision emphasizes real-time performance and requires high speed and high precision. As a result, many technologies in computer vision and digital image processing are currently difficult to apply to machine vision. Their development speed far exceeds their actual application speed in industrial production.
What are the directions of machine vision?
What are the development trends of machine vision technology? This article analyzes five major directions in the development of machine vision.
1. The applications of 3D imaging and robotic arms will continue to expand.
Industrial automation is driving smarter factory production lines and replacing manual labor, reducing manpower. Machine vision is already widely used for quality control inspection, but new markets are emerging with the advent of integrated solutions combining 3D sensors and robotic picking. Robotic picking systems can randomly grasp objects regardless of their position and orientation. 3D vision systems can identify a large number of randomly placed parts, such as suitcases and parts boxes. Due to the robot's dynamic processing capabilities, complex objects can be selected from different orientations and stacks. Combining artificial intelligence ( AI ) with picking operations enables autonomous part selection, improving productivity and cycle time, and reducing the need for human-machine interaction during the process.
2. Increased application of deep learning
The advent of 5G data networks has enabled autonomous vehicles to perform cloud-based machine vision computations. Massive Machine Type Communication ( mMTC ) allows for the processing of massive amounts of data in the cloud for machine vision applications. Deep learning algorithms using convolutional neural network classifiers can rapidly perform image classification, object detection, and segmentation. The development of these new artificial intelligence and deep learning systems is expected to increase in the coming year.
3. A significant increase in the number of robots.
According to the International Federation of Robotics , 2018 was a record year for robot sales, with industrial robot sales increasing by 31% . Trends such as human-robot collaboration, simplified use, and process learning have helped drive the adoption of robots in industrial automation. In the future, industrial robots will be easier and faster to program using intuitive interfaces. Human-robot collaboration will support flexible production of small batches with high complexity. The reduced complexity of use will enable the widespread adoption of robots and vision systems in the medium to long term.
4. Hyperspectral imaging analysis and detection technology
Next-generation modular hyperspectral imaging systems offer chemical material performance analysis in industrial environments. Chemical color imaging visualizes the molecular structure of materials through resulting images in different colors. This allows chemical composition analysis within standard machine vision software. Typical applications include plastic detection in meat production, detection of different recyclable materials, and quality control in pellet inspection. The main challenge for these systems is processing the required amount of data and speed, but advancements in faster processing, better algorithms, and camera calibration continue to make them a hot topic in 2019.
5. Thermal imaging is becoming increasingly common in industrial inspection.
Thermal imaging cameras have traditionally been used in defense, security, and public safety, and thermal imaging technology is widely used for detection. For many industrial applications, such as the production of components in the automotive or electronics industries, thermal data is crucial. While machine vision can see production problems, it cannot detect thermal anomalies. The combination of thermal imaging and machine vision is an evolving field that enables manufacturers to discover problems that are invisible to the naked eye or standard camera systems. Thermal imaging technology provides non-contact, precise temperature measurement and non-destructive testing, representing a significant direction for development in machine vision and automation control.
Industrial automation technologies related to machine vision are driving further changes in manufacturing. Machine vision is applicable to all industries, but it is particularly important in high-standard, highly regulated sectors such as food and beverage, pharmaceuticals, and medical device manufacturing. There are several reasons why companies are turning to factory automation technologies. The difficulty in recruiting workers is forcing companies to automate production lines, replacing manual labor with machines, which improves production line efficiency, utilizes resources more effectively, and increases productivity. It is projected that the demand for machine vision-related technologies will continue to deepen and grow across various fields in 2019.
