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The Limitations of Machine Vision Technology in Industrial Manufacturing

Ⅰ. Advantages of machine vision system technology

The computer vision system has the ability to watch and explain, and it can automatically complete multiple tasks without manual intervention. Therefore, business users can enjoy the following benefits:

1. A faster and simpler process

The machine vision system can perform monotonous and repetitive tasks at a faster speed, making the whole process simpler.

2. Accurate results

The machine never makes mistakes. Similarly, unlike humans, machine vision systems with image processing capabilities can't make mistakes. Ultimately, the products or services provided are not only fast, but also of high quality.

3. Cost reduction

As the machine assumes the responsibility of performing tedious tasks, errors will be minimized, leaving no room for defective products or services. Therefore, the company can save a lot of money, otherwise the money will be spent on repairing defective processes and products.

No technology is perfect. The same principle applies to machine vision systems. Despite the current limitations of computer vision systems, it can provide companies with huge opportunities to increase revenue sources, achieve productivity goals, and simplify work processes.

Ⅱ. The limitations of the application of machine vision technology in industrial manufacturing

1. Constrained by the ambient light source

Different light sources will cause different imaging quality and effects, directly interfere with the detection of the detection algorithm, and may cause misjudgment of the product; a single visual guidance technology cannot guarantee the accuracy of obstacle detection in the path, and the decision-making control layer often needs to integrate multiple sensors Information collected.

2. Restricted by the performance of hardware equipment

The camera's lens distortion correction, calibration differences, and limited viewing angle range; installation conditions and site restrictions, and requirements for sensor fusion schemes; the dark current of each pixel is different, and the response to photons is inconsistent, which will cause spatial and pattern noise in the camera; the limitation of parameter setting of CCD line scan camera lens.

3. Restricted by computing resources on the end

The large-scale and complex model architecture of industrial products needs to rely on powerful computing capabilities. When the memory on the device terminal is difficult to meet, it needs to use the model cloud offline training and then deploy to the device terminal; image data transmission still needs to adjust and optimize the model parameters for specific task targets, which will generate additional engineering overhead, and the real-time performance is poor.

4. Restricted by the diversity of detection objects

There are many kinds of defects on the surface of the object, the mechanism of defect generation is unknown, and the description of the defect is insufficient; it is difficult for the machine vision system to extract features from the data.

5. Restricted by cost and benefit economy

The development of the core components of industrial cameras such as vision sensors and the underlying vision software requires a large investment cost.