Over the past year, fields such as artificial intelligence (AI), big data, 3D imaging, and robotic process automation have experienced unprecedented growth. In the coming year, 2019, machine vision technology applications will continue to flourish, and new machine vision trends are expected to lead industry applications.
3D Imaging and Bin Picking in industrial automation are driving factories to become smarter and can replace manual labor, reducing the need for human workers. Machine vision for quality control inspection is already widely used, but with the emergence of 3D sensors and robotic pick-and-place solutions, new markets are opening up. Robotic picking systems can randomly grasp objects regardless of their position and orientation. 3D vision systems can identify large quantities of randomly placed parts, such as suitcases and parts bins. Due to the dynamic handling capabilities of robots, 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 times, and reducing the need for human-machine interaction in the process.
Cloud-based Deep Learning
The arrival of 5G data networks provides autonomous vehicles with the ability to perform cloud-based machine vision computations. Massive Machine Type Communication (mMTC) allows for the processing of large amounts of data in the cloud for machine vision applications. Deep learning algorithms using convolutional neural network classifiers can quickly perform image classification, object detection, and segmentation. The development of these new AI and deep learning systems will increase in the coming year.
Robotics
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, 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 with intuitive interfaces. Human-robot collaboration will support flexible production of small batches with high complexity. The reduction in complexity makes robots and vision systems widely usable in the medium to long term.
Hyperspectral Imaging
Next-generation modular hyperspectral imaging systems provide chemical material property analysis in industrial environments. Chemical color imaging visualizes the molecular structure of materials through resulting images of different colors. This allows chemical composition to be analyzed in standard machine vision software. Typical applications include plastic detection in meat production, detection of different recyclable materials, and quality control of pill inspection. The main obstacle for these types of systems is handling the required data volume and speed, but advancements in faster processing, better algorithms, and camera calibration still make it a hot topic in 2019.
Thermal Imaging for Industrial Inspection
Traditionally used in defense, security, and public safety, thermal imaging technology is widely used for detection.
For many industrial applications, such as component manufacturing in the automotive or electronics industries, thermal data is crucial. While machine vision can identify production problems, it cannot detect thermal anomalies. The combination of thermal imaging and machine vision is a growing field, enabling manufacturers to detect problems invisible to the naked eye or standard camera systems. Thermal imaging technology provides non-contact, precise temperature measurement and non-destructive testing, which is a developing trend in the field of machine vision and automation control.