What is machine vision inspection? What are the future development trends?

   In industrial or production environments, machine vision typically uses a single camera or multiple cameras to automatically inspect and analyze objects. The data obtained are then used to control processes or production activities. In a typical application such as an assembly line, after an operation is performed on a part, a camera is triggered to capture and process an image. Cameras can be programmed to check the location, color, size or shape of a target object, or the object's presence. It can also view and decrypt standard or 2D matrix barcodes and even read printed characters.

After the product is inspected, a signal is typically generated to determine what to do with it. Parts may be rejected into containers or branch conveyors, or passed through more assembly operations, with the system tracking their inspection results. Machine vision systems can often provide more information about objects than simple sensors. Typical applications of machine vision include: quality assurance, robot/machine guidance, testing and calibration, real-time process control, data acquisition, machine monitoring, sorting/counting.

Many manufacturers use automated machine vision instead of human inspectors because it is better suited for repetitive inspection tasks. It's faster, more objective, and allows for continuous, uninterrupted work. Machine vision systems can inspect hundreds or even thousands of parts per minute and provide more consistent and reliable inspection results than human inspectors.

By reducing defects and increasing yields, machine vision systems help manufacturing companies track products, components and improve compliance, thereby saving costs and improving profitability.

The future development trends of machine vision inspection have the following directions:

1. Light source and imaging: High-quality imaging in machine vision is the first step. Since problems such as surface reflection and refraction of objects made of different materials will affect the extraction of features of the measured object, light source and imaging can be said to be the first step to overcome in machine vision inspection. A difficult situation. For example, in the current scratch detection of glass and reflective surfaces, many problems are stuck in the integrated imaging of different defects.

2. Feature extraction in heavy noise, medium and low contrast images: In heavy noise environments, it is often difficult to identify true and false defects, which is why many scenes always have a certain false detection rate. However, this is done through imaging and edge features. The rapid development of extraction has led to various breakthroughs.

3. Identification of unexpected defects: In applications, some specific defect patterns are often given and machine vision is used to identify whether they have occurred. But what is often encountered is that many obvious defects have been missed because they have not occurred before, or the patterns of occurrence are too diverse. If it were a human being, although he was not asked to detect this defect in the operation process document, he would notice it and have a greater chance of catching it. However, the "wisdom" of machine vision in this regard is currently difficult. breakthrough.

4. Embedded solutions are developing rapidly, smart cameras have outstanding performance and cost advantages, and embedded PCs will become more and more powerful. The modular general-purpose software platform and artificial intelligence software platform will reduce the technical requirements of developers and shorten the development cycle.