Five major directions for the development of machine vision

Machine vision is suitable for all industries, but it is especially important in high-specification, highly-regulated industries such as food and beverage, pharmaceutical, and medical device manufacturing. There are many reasons why companies are turning to factory automation technology, and the difficulty of recruiting workers forces enterprises to automate their production lines and realize the replacement of manual labor by machines, including improving production line efficiency, using resources more effectively and improving productivity. It is estimated that the demand for machine vision-related technologies in various fields is expected to continue to deepen and grow in 2020.

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

Industrial automation is driving factory production lines to become smarter and can replace manual labor and reduce labor. Machine vision for quality control inspections has been widely used, but with the advent of integrated solutions for 3D sensors and robotic picking, new markets are being explored. Regardless of the position and orientation of the part, the robotic picking system can pick up the object at random. The 3D vision system can recognize randomly placed parts such as suitcases and parts boxes in large numbers. Due to the robot's dynamic processing, complex objects can be selected in different orientations and stacks. Combining artificial intelligence (AI) with pick-and-pick operations enables autonomous part selection, increasing productivity and cycle times, and reducing the need for human-machine interaction in the process.

Second, the application of deep learning has increased

The advent of 5G data networks provides autonomous vehicles with the ability to perform cloud-based machine vision computing. Massive Machine Type Communication (mMTC) allows large amounts of data to be processed in the cloud for machine vision applications. Deep learning algorithms using convolutional neural network classifiers can quickly perform image classification, object detection, and segmentation. In the coming year, the development of these new AI and deep learning systems will increase.

Third, the number of robots has increased significantly

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 collaborative robots, ease of use, and process learning are helping to drive the use of robots in industrial automation. In the future, industrial robots will be easier and faster to program with an intuitive interface. Human-robot collaboration will support flexible production of small batches and high complexity. The reduction in complexity of use has led to the widespread use of robots and vision systems in the medium to long term.

4. Hyperspectral imaging analysis and detection technology

The next-generation modular hyperspectral imaging system provides analysis of chemical material properties in industrial environments. Chemical color imaging visualizes the molecular structure of materials through the resulting images of different colors. This allows chemical compositions to be analyzed in standard machine vision software. Typical applications include the inspection of plastics in meat production, the inspection of different recyclable materials, and the quality control of bubble pill inspection. The main obstacle to this type of system is the amount and speed of data required, but the development of faster processing, better algorithms, and camera calibration still makes it a hot topic in 2019.

Fifth, thermal imaging industrial testing is becoming more and more popular

Thermal imaging cameras have traditionally been used in defense, security, and public safety, and thermal imaging technology is widely used in 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 issues, it can't detect thermal anomalies. Thermal imaging, combined 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 non-destructive testing, which is the direction of development in the field of machine vision and automation control.