The machine vision market is booming and needs to be explored in the future.
The application of vision and hearing on the machine brings together the whimsy of many great scientists and is slightly smaller. At the beginning of the twentieth century, the film evolved from silent to sound, condensing the efforts of many inventors, including Edison, to realize the storage, release and transmission of sound waves on powerless devices, but until a hundred years later, machine vision and machine hearing were still a hard bone. There are still many places where machine vision needs to be applied, and there is a constant need to develop and explore.
Floor quality inspection applications, supermarket loss prevention applications, parasitic identification, etc., are just preliminary applications of machine vision. Machine vision from the perspective of AI, its development has a lot to do, drones, unmanned vehicles, robot navigation are inseparable from 3D vision Technology, it is very meaningful to engage in cutting-edge scientific research in this field.
Machine vision technology accelerates intelligent shooting of drones
The main purpose of the drone's gesture self-timer is the recognition function of machine vision, followed by the positioning function. The motion capture technology involves dimensional measurement, object location and orientation measurement in the physical space. The data can be directly understood by the computer. As the film and games continue to develop, the application of motion capture technology becomes more mature.
Machine vision and motion capture technology is equivalent to giving the machine a human visual system. The drone gesture self-timer is only a tiny application of machine vision and motion capture technology. Currently, there are not many drone companies and products that master this technology. More typical is Dajiang. Their Elf Phantom 4 series and Royal Mavic all have the function of gesture self-timer. The newly released P4A also has the function of gesture self-timer. It seems that Dajiang has completely mastered this technology and used it. The fire is pure.
Machine vision helps autopilot
At present, visual sensors and machine vision technology are widely used in various advanced assisted driving systems. Among them, the perception of the driving environment is one of the important components of the advanced assisted driving system based on machine vision.
The perception of the driving environment mainly relies on visual technology to perceive the road information, road condition information and driver status when the vehicle is driving, and provides the basic data necessary for the decision-making of the assisted driving system. among them,
Road information mainly refers to static information outside the vehicle, including: lane lines, road edges, traffic signs and signal lights;
The traffic information mainly refers to the dynamic information outside the vehicle, including: obstacles in front of the road, pedestrians, vehicles, etc.;
The driver's status belongs to the in-vehicle information, which mainly includes: driver's fatigue, abnormal driving behavior, etc., to avoid the safety accident caused by the vehicle by reminding the driver of unsafe behavior.
Using machine vision technology to perceive the driving environment, you can obtain static information and dynamic information inside and outside the vehicle to help the driver assistance system make decision-making judgments.
Machine vision technology makes robots more stable
Machine vision is an important direction for the development of robots and one of the key factors to improve the level of robot intelligence, which helps to automate robot work.
Robot-aware environments typically rely on a variety of contact and non-contact sensors. Machine vision is based on a bionic angle. For example, a simulated eye is image acquired by a visual sensor and image processed by an image processing system after acquisition. Identification. In addition, the robot with machine vision can sense the changes of the external environment in time, and facilitate the adjustment of the control system of the intelligent robot, which improves the flexibility of the robot and the adaptability to changes in the external environment.