Machine vision simulates human visual functions using computers to enable machines to acquire and interpret visual information. It consists of two fundamental components: "vision" and "perception." "Vision" involves capturing external information through imaging, converting it into digital signals fed back to the computer, which requires a complete set of hardware solutions including lighting sources, cameras, image acquisition cards, and visual sensors. "Perception" refers to the computer's processing and analysis of these digital signals, primarily achieved through software algorithms.

1. Machine Vision Systems  
Machine vision has broad industrial applications, with core functions including measurement, inspection, recognition, and positioning. The industry chain can be divided into three segments: upstream component market, midstream system integration and equipment manufacturing market, and downstream application market. Upstream providers include hardware and software suppliers such as lighting sources, lenses, industrial cameras, image acquisition cards, and image processing software. Midstream players consist of system integrators and complete equipment manufacturers. Downstream applications are extensive, covering key markets such as electronics manufacturing, automotive, printing and packaging, tobacco, agriculture, pharmaceuticals, textiles, and transportation.

2. Trends in the Machine Vision Industry
In the field of machine vision, the defect detection function is one of the most frequently utilized functions. It mainly detects various information on the product surface. In modern industrial automated production, in each process of continuous large-scale production, there is a certain defective rate. Although the ratio is small when viewed individually, when multiplied together, it becomes a bottleneck that makes it difficult for enterprises to improve the yield rate. Moreover, after completing the entire process, removing the defective products will incur much higher costs (for example, if there is a positioning deviation in the solder paste printing process and this problem is not discovered until the online test after chip mounting, the rework cost will be more than 100 times the original cost). Therefore, timely detection and defective removal are extremely important for quality control and cost control, and they are also an important foundation for the further upgrading of the manufacturing industry.

1) High accuracy: Human vision has a resolution of 64 gray levels, and has poor resolution for small targets; machine vision can significantly increase the gray levels, and can observe targets at the micrometer level.
2) Fast speed: Humans cannot clearly see fast-moving targets, while the machine's shutter time can reach the microsecond level.
3) High stability: Machine vision solves a very serious problem for humans - instability. Manual inspection is a very boring and exhausting industry. No matter how you design the reward and punishment system, there will be a relatively high rate of missed inspections. However, machine vision inspection equipment has no fatigue problem and no emotional fluctuations. As long as it is what you write in the algorithm, it will be executed carefully every time. In quality control, it greatly improves the controllability of the effect.
4) Integration and retention of information: The information obtained by machine vision is diverse and traceable. Relevant information can be conveniently integrated and retained.

3. Machine vision technology has developed rapidly in recent years.
1) Image acquisition technology has advanced significantly.
The solid-state devices such as CCD and CMOS have become increasingly mature, the size of image-sensitive components has been continuously reduced, the number of pixels and data rate have been constantly increased, the improvement speed of resolution and frame rate can be described as constantly evolving month by month, the product series has become increasingly diverse, and parameters such as gain, shutter and signal-to-noise ratio have been continuously optimized. Through core test indicators (MTF, distortion, signal-to-noise ratio, light source brightness, uniformity, color temperature, comprehensive evaluation of system imaging capability, etc.) to comprehensively select the light source, lens and camera, many previous imaging difficulties have been continuously overcome.

2) The fields of image processing and pattern recognition have developed rapidly.
In terms of image processing, with the extraction of high-precision edge information from images, many previously indistinguishable low-contrast defects that were mixed in background noise and difficult to detect directly have begun to be identified.

In pattern recognition, it can be regarded as a marking process. Based on a certain degree of measurement or observation, the patterns to be recognized are classified into their respective patterns. In image recognition, the methods that are most frequently used are decision theory and structural methods. The basis of the decision theory method is the decision function. By using it to classify and recognize pattern vectors, it is based on the temporal description (such as statistical texture); the core of the structural method is to decompose objects into patterns or pattern primitives, and different object structures have different primitive sequences (or called strings). By using the given pattern primitives to determine the encoding boundary of the unknown object and obtain the string, the category of the object can be determined based on the string. In feature generation, many new algorithms are constantly emerging, including features based on wavelets, wavelet packets, fractals, as well as binary decomposition analysis; and related to support vector machines, deformation template matching, linear and nonlinear classifier design, etc. are constantly expanding.

3) Breakthroughs brought by deep learning
Traditional machine learning mainly relies on humans to analyze and establish logic in feature extraction. In contrast, deep learning simulates the brain's operation through multiple layers of perceptrons and builds deep neural networks (such as convolutional neural networks) to learn simple features, establish complex features, learn mappings and output. During the training process, all levels are continuously optimized. In specific applications, such as automatic ROI area segmentation; punctuation positioning (flexibly detecting unknown flaws through realistic vision); re-detecting flaws in noisy images that cannot be described or quantified such as orange peel flaws; and distinguishing true and false flaws in glass cover plate detection, etc.

4) Development of 3D Vision
3D vision is still in its infancy. Many applications are using 3D surface reconstruction, including navigation, industrial inspection, reverse engineering, mapping, object recognition, measurement and classification, etc. However, the issue of accuracy limits the application of 3D vision in many scenarios. Currently, the most widely implemented application in engineering is the measurement of standard parts' volume in logistics. It is believed that there is great potential in this area in the future.

4. To replace manual inspection completely, there are still many difficulties for machine vision to overcome:
1) Light source and imaging
Clear imaging in machine vision is the first step. Due to issues such as reflection and refraction on the surfaces of different material objects, which all affect the extraction of the characteristics of the measured object, the light source and imaging can be said to be the first major challenge for machine vision inspection. For example, in the detection of scratches on glass and reflective surfaces, etc., the problem often lies in the integration of different defects into imaging.

2) Feature extraction in low-contrast images with heavy noise
In environments with heavy noise, the distinction between genuine and fake defects is often difficult. This is why there is always a certain rate of false detections in many scenarios. However, through the rapid development of imaging and edge feature extraction, various breakthroughs have been continuously achieved in this area.

3) Identification of Unintended Defects
In applications, specific defect patterns are often provided, and machine vision is used to determine whether these defects actually occur. However, a common situation is that many obvious defects are missed due to either their infrequent occurrence or the diverse patterns of their occurrence. If it were humans, although they might not be instructed to detect this defect in the operation process document, they would notice it and have a higher chance of catching it. However, the "intelligence" of machine vision in this regard is still difficult to surpass at present.

5. Future Trends of Machine Vision Systems
1) Embedded solutions are developing rapidly, with intelligent cameras demonstrating significant advantages in performance and cost. Embedded PCs will become increasingly powerful.
2) Modular general-purpose software platforms and artificial intelligence software platforms will reduce the technical requirements for developers and shorten the development cycle.
3) 3D vision will be applied to more scenarios.