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The annual “Double Eleven” shopping festival came to an end last month. According to data from China Merchants Securities, this year’s “Double Eleven” online e-commerce transaction volume reached 1,150.7 billion yuan, a year-on-year increase of 13.43%, showing strong consumption resilience. Compared with previous years, consumers are more rational in shopping. Some people in the industry pointed out that quality consumption, green consumption and rational consumption have gradually become the mainstream, and “Double 11” reflects the changes in the consumption concepts of Chinese people.

In order to keep up with the increasing demands of consumers and the pursuit of higher quality, today’s manufacturing field needs to make efforts in quality. Consumers generally choose to return products without hesitation when they buy damaged products or expired food. According to Zebra’s 2022 Global Consumer Survey, there is a huge trust gap between consumers and retailers. Therefore, it is particularly important to maintain the trust of consumers, and the occurrence of returned goods will damage the goodwill of the brand or retailer.

Companies are increasingly relying on enterprise-grade computer vision and machine vision solutions to meet this challenge. According to Analysys analysis, with the continuous deepening of the digital transformation of industry and transportation, and the gradual implementation of cutting-edge technology applications, the growth rate of China’s computer vision market will gradually increase, and the market size is expected to reach 76.7 billion yuan in 2024. This figure is not surprising, as for many businesses the strong adoption of AI (AI) and automation are great options to increase supply chain speed, improve inventory and order accuracy, and improve quality control. As manufacturers and logistics providers race to increase throughput, a new generation of machine vision systems is also providing an easy way to speed up inspection of goods without compromising accuracy Strengthen order fulfillment capabilities.

Machine Vision Gets a New Look

A basic function of machine vision is to determine whether parts or products on the production line meet standards by using information extracted from digitized images to compare with rules, and to automatically remove items that fail the test from the production line.

Given the subtle variations that can exist between individual parts or products, and the small-scale defects that manufacturers and warehouse operators need to be aware of, it makes sense that machine vision systems should be the tool of choice for inspections performed on the production line. They can capture and analyze images much faster than human workers can. And, with increased resolution, in some cases far beyond the range of human vision, powerful machine vision cameras are able to see things that are too small or invisible to the human eye.

At the same time, they also have a more sophisticated afterimage mechanism, which is helpful when monitoring defect patterns and finding solutions. Key personnel are able to see reported defects, determine the source, and quickly investigate the cause to minimize further waste or delays in fulfillment. What’s more, the absence of employees touching parts on the production line is a key benefit of machine vision, preventing potential damage and eliminating the time-intensive process of manual inspection, freeing employees to do more strategic work Task.

Customizing and maintaining machine vision programs used to require costly investment and expert programmers. This complexity “discourages” some factory managers and engineers, who believe that despite the rising importance of quality control, machine vision is too expensive and challenging to implement. In many cases, only large enterprises can easily implement machine vision in their Industrial operations.

But today, unlike in the past, machine vision has taken on a new lease of life. Over the past few years, various factors have made machine vision more accessible and usable for different types of workflows and businesses. Initially used in the electronics and automotive industries, the increased availability of machine vision solutions is propelling the technology to expand into new areas including surveillance, medical and pharmaceutical, food and beverage, and robotics.

Empowerment of deep learning

Thanks to deep learning, smaller businesses can also more easily set up, deploy and run machine vision systems without the need for specialized personnel. As deep learning matures, it is deployed more frequently and is expected to displace more traditional manufacturing applications that employ rule-based programming.

While this represents a huge step forward in accessibility, AI technology will still make mistakes. However, as AI technology continues to mature, it will also become smarter. The more it learns, the more accurate and reliable its results become. For these algorithms to work, increased computer processing power is necessary. In addition, thanks to increased chip performance and reduced size, today’s AI systems are small enough to operate in relatively limited spaces. This is another key factor in the increasing accessibility of machine vision.

Realizing the future of automation

Operations orchestration is the goal of many businesses, requiring the coordination of real-time intelligent technology, inventory and human labor to gain a competitive advantage. According to the “National Recruitment in the Third Quarter of 2022 The Ranking of the 100 Occupations That Are Greater Than Job-seekers” shows that 39 of the 100 occupations belong to manufacturing and related personnel. Compared with the previous quarter, the shortage of workers in the manufacturing industry continued. While many believe that automation comes at the cost of fewer jobs for people, it can actually address growing labor shortages while improving productivity and accuracy. In the new era ushered in by machine vision, employees can more easily oversee and operate automated systems without requiring a higher level of computer science education.

Machine vision can now be integrated into nearly every step of the manufacturing process, enhancing data collection to improve track and trace, speeding up fulfillment picking and packing, and guiding workers and Robots through the integration of the internet of Things (IoT). High-performance scanners and cameras are key to advancing AI and IoT capabilities in warehouse environments.

While machine vision systems still require advanced camera technology, the associated equipment can be managed more easily with software that can be seamlessly integrated into overall factory operations. For example, machine vision in such environments leverages much of the same core imaging technology as stationary industrial scanners.

In order to keep up with the pace of development of the manufacturing and logistics industries, the industry needs to continue to improve. With the evolution of technology, the application of machine vision has become a necessary condition in order to be at the forefront in the face of rapidly growing consumer and business needs. Integrating advanced technologies into a single overall solution is an important step in the process of realizing industrial automation.