Machine vision is applicable to all industries, but it is particularly important in high-specification and high-supervision industries such as food and beverage, pharmaceutical and medical device manufacturing. Enterprises turn to factory automation technology for a variety of reasons, enterprise recruitment is difficult to force enterprise production line automation, to achieve machine replacement manual, improve production line efficiency, more effective.

First, the application of 3D imaging and manipulator will continue to expand

Industrial automation, promote the factory production line more intelligent, can replace manual, reduce labor force. Machine vision for quality control detection has been widely used, but with the emergence of 3D sensors and robot pickup integrated solutions.

Second, the application of deep learning increases

The arrival of 5G data networks provides cloud-based machine vision computing capabilities for self-driving cars. Massive Machine Type Communication (mmTC) allows processing of large amounts of data in the cloud for machine vision applications. Depth using convolutional neural networks classifier.

The number of robots is increasing

According to the International Federation of Robotics, 2018 was a record year for robot sales, with industrial robot sales up 31%. Trends such as human collaborative robotics, simplified use and process learning have helped drive the use of robots in industrial automation. In the future, industrial robots will be easier and faster to program using intuitive interfaces. Man-machine collaboration will support flexible production with low volume and high complexity. The reduced complexity of use will allow robots and vision systems to be widely used in the medium to long term.

Hyperspectral image analysis and detection technology

The next generation of modular hyperspectral imaging systems provides analysis of chemical material properties in industrial environments. Chemical color imaging visualizes the molecular structure of a material through the resulting images of different colors. This allows the chemical composition to be analysed in standard machine vision software. Typical applications include detection of plastics in meat production, detection of different recyclable materials and quality control of bubble test. The main obstacles to such systems are the amount and speed of data required to process them, but developments in faster processing, better algorithms and camera calibration still make them a hot topic in 2019.

Thermal imaging industrial testing is becoming more and more popular

Thermal imaging cameras are traditionally used for national defense, security and public safety, and thermal imaging technology is widely used in detection. For many industrial applications, such as parts production in the automotive or electronics industries, thermal data is crucial. While machine vision can see production problems, it cannot detect thermal anomalies. Combining thermal imaging with machine vision is a growing field, which allows manufacturers to spot problems that are not visible to the naked eye or standard camera systems. Thermal imaging technology provides non-contact precision temperature measurement and nondestructive testing, which is the development direction of machine vision and automation control field.