Machine vision and AI work together to accelerate the industrial process

The progress of AI capabilities has expanded the scope of machine vision capabilities. At the software level, the homologous bottom model can generalize to meet the needs of multi-application fields and multi-functions, and reduce the development threshold and cost.

Machine vision is used by AI as an “eye” to obtain underlying data and assist in human-computer interaction.

Machine vision enterprises represented by Lingyunguang, Opte and Tianzhun Intelligence have seized the AI race track, cooperated with upstream and downstream enterprises to open up the product ecology, and continuously enriched the algorithm tools and application scenarios.

Lingyunguang imparts the decision-making module of intelligent algorithm into the production system of “end, edge and cloud”, forming an intelligent collaborative closed-loop system of output and feedback of “end, edge and cloud”, and realizes better coordination of “eye, brain and hand” with the help of industrial artificial intelligence.

In November 2022, facing the new demand of intelligent manufacturing in the lithium industry, Ling Yunguang launched a deep learning platform F.Rain, which is independently developed for industrial quality inspection scenarios. Based on “machine vision +AI”, the platform integrates 200,000 + and 100,000 + defect samples of lithium battery electrodes and cells, and the detection accuracy rate is up to 98.5% and 99.5%.

From the hardware into the software, based on the self-developed algorithm constantly updated iteration, in lithium, 3C has obtained good application effect.

Tianzon Technology Co., Ltd. has become a gold partner of Nvidia’s Jetson product line solutions in 2021, creating an AI edge computing platform based on Nvidia’s embedded GPU, which is deeply applied to various scenarios in the field of intelligent networking.

Huawei Pangu CV generalization application has been industrialized practice

Huawei Pangu CV Grand model has been widely used in industry, logistics, design and other fields.

Pangu Grand Model includes three levels: L0 (basic grand model), L1 (industrial pre-training grand model) and L2 (inference model). The model only uses one pre-training and carries out generalization replication and downstream task fine-tuning on top of the basic grand model, including CV, NLP and scientific computing.

Among them, Pangu CV large model can be applied to industrial quality inspection, logistics warehouse monitoring, fashion assisted design and other fields. It has excellent generalization ability and efficient sample screening ability, which can save more than 80% labor labeling cost, small sample/zero sample ability, low threshold AI development and other advantages.

The model can be applied to the development scheme of railway TFDS. Based on prior template matching, the small sample fault location and identification can be achieved with an accuracy of 98% to 99%.

In the field of intelligent mining, the model can cover the main business of mining, excavation, machinery, transportation and communication, reducing the underground safety accidents by more than 90 percent.

Multiple factors promote the rapid growth of machine vision market demand

In the long run, the aging population and the rising labor price will bring machines to replace people, and machine vision equipment will gradually replace human labor.

In the medium term, the downstream application of machine vision is broad, and the permeability continues to increase.

In 3C field, end customers’ demand for machine vision has expanded from mobile phones to tablets, earphones, watches, etc.

In addition to stirring, machine vision is applied in coating, rolling, winding, shell and other processes in the field of lithium electricity, and vision technology is also applicable to 4680 and other new batteries;

Demand for wafer defect detection equipment and photovoltaic wafer sorting equipment in semiconductor and photovoltaic fields has increased significantly.

In the short term, the recovery of fixed asset spending in manufacturing and the acceleration of domestic substitution will accelerate the release of machine vision equipment demand.

According to the data of China Machine Vision Industry Alliance, the market size is expected to maintain 25% growth in the future, and exceed 39 billion in 25 years.

The end:

Although the integration of machine vision and artificial intelligence has many potential advantages in technology research and development and innovation, it also brings certain risks and challenges.

For example, in the aspect of technology implementation, there are problems such as unstable system performance and inconsistent data quality, so the risk of technology research and development and innovation needs to be comprehensively considered.