Five Directions for the Development of Machine Vision Technology

Over the past few years, areas such as artificial intelligence (AI), big data, 3D imaging, and robotic process automation have made unprecedented advancements. What does the future hold for the development of machine vision technology? Here is an analysis of the five main trends for machine vision technology development.

1. 3D Imaging and Robot Arm Applications Will Continue to Expand
   Industrial automation is driving factory production lines to become more intelligent, reducing the need for human labor. Machine vision has been widely used for quality control inspections, but with the emergence of 3D sensors and robotic arm integration solutions, new markets are opening up. Robotic picking systems can grab objects randomly, 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 dynamic handling of robots, complex objects can be selected from different orientations and stacks. Combining artificial intelligence (AI) with picking operations can enable autonomous part selection, increasing productivity and cycle times while reducing the need for human interaction in the process.

2. Increased Applications of Deep Learning
   The arrival of 5G data networks has provided autonomous vehicles with the ability to execute cloud-based machine vision calculations. Massive Machine-Type Communication (mMTC) allows for large volumes of data to be processed in the cloud for machine vision applications. Deep learning algorithms using Convolutional Neural Network (CNN) classifiers can quickly classify images, detect targets, and segment them. Over the coming years, the development of these new AI and deep learning systems will increase.

3. Significant Increase in the Use 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 collaborative robots, simplified usage, 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 using intuitive interfaces. Human-robot collaboration will support small-batch, high-complexity flexible production. The reduced complexity of use will lead to widespread adoption of robots and vision systems in the medium to long term.

4. Hyperspectral Imaging for Analytical Detection
   The next-generation modular hyperspectral imaging systems offer chemical material performance analysis in industrial environments. Chemical color imaging visualizes the molecular structure of materials through different colored resulting images. This allows chemical composition analysis to be conducted with standard machine vision software. Typical applications include plastic detection in meat production, detection of different recyclable materials, and quality control for tablet inspection. The main barrier to this type of system is the amount of data and speed required for processing, but faster processing, better algorithms, and camera calibration advancements keep this topic relevant.

5. Increased Adoption of Thermal Imaging for Industrial Inspection
   Thermal imaging cameras are traditionally used in defense, security, and public safety, where thermal detection is widely applied. Thermal data is critical for many industrial applications, such as automotive or electronics component production. While machine vision can detect production issues, it cannot detect thermal anomalies. Combining thermal imaging with machine vision is an evolving area, allowing manufacturers to detect problems invisible to the naked eye or standard camera systems. Thermal imaging provides non-contact precision temperature measurement and non-destructive testing, which is a significant development direction for machine vision and automation control.

Industrial automation technology related to machine vision is driving more changes in manufacturing. Machine vision is applicable to all industries but is particularly important in highly regulated sectors like food and beverage, pharmaceuticals, and medical device manufacturing. The drive towards factory automation technology is influenced by various factors, including labor shortages, which are pushing companies toward production line automation, as well as goals to improve production efficiency, use resources more effectively, and increase productivity. The expectation is that the demand for machine vision-related technologies will continue to grow across different fields in the future.