Machine vision houses integration of multiple systems, devices, and processes seamlessly transmitting and transferring the correct predictions that is required. Here’s more…
Fremont, CA: Machine vision is related to image processing, artificial intelligence, and pattern recognition. This technology is used for examining natural objects and materials and manufacturing processes to detect defects and improve quality, operating efficiency, and the safety of both products and processes. Machine vision houses integration of lighting, mechanical handling video cameras, optics, image sensors, signal processing, computer systems architecture, image processing, human-computer interface, industrial engineering, control systems, and quality assurance methods. Machine Vision is a branch of Systems Engineering, and thus, its application requirements play a vital role in the design of practical vision systems.
Robots functioning in industries need visual feedback. With machine vision, they can navigate, identify parts, collaborate with humans, and combine information from other sensors to improve their location information. The industrial applications of the robots include inspection, assembling, quality control, locating parts, transporting parts, and more. Depending on the application, the vision system can either be scene-related or object-related. In the scene-related vision, the camera is mounted on the mobile robot for mapping, localization, and obstacle avoidance applications. In object-related vision systems, the camera is mounted on the end of the robot’s arm near the active tool. High accuracy is an essential requirement. Besides being equipped with high-resolution cameras, they are also armed with optical calibration.
The first step is image distortion and deformation correction. While performing navigation tasks, the robots build a 3D model of the environment around them. Objects without texture may present a challenge when RGB cameras are used to perform 3D modeling. Active lasers that are sensitive to area reflections are also a challenge. The calibration process performs the mapping between the sensor’s 2D image and the 3D space. 3 RGB cameras positioned at different locations and orientations to create a 3D space. Physical markers are great support, and they exist in the images or are projected on the scene. To detect pairable features, a feature extraction algorithm is utilized. A modern deep learning classifier or classical, gradient-based algorithm trained with features set are valid options as well.
Compared to RGB, Time of Flight (TOF) cameras are active cameras. By transmitting a short light pulse, they measure the delay of the reflected pulse. By this, a 3D image is created with the depth information. The machine vision system faces challenges such as noise, low resolution, inaccuracy, and sensitiveness to external light. To handle such problems, algorithms use high rate scene re-capturing. Structured light, an additional passive system, transmits a sequence of different patterns on the environment, which helps in tracking movements inside the environment. Light coding, which is an evolution of structured light, replaces the patterns sequence. Since the lights are always on, it is less sensitive to light timing accuracy.
A pattern of points is generated by a set of laser emitters or scanning laser beams. The curvature of the surface is revealed by the location emitted on the receiver. Machine vision faces the challenge of the surface, not reflecting well; 3D models display the holes in such locations. To fill the missing data, the algorithm uses time-sequenced laser information. Machine vision helps in achieving and maintaining efficiency, and organizations are leveraging this technology to increase the accuracy of manufacturing.