From two dimensions, the first is technology: the rapid increase of computer processing, the decrease of storage cost, and the development of cloud computing, the Internet of things, and other technologies, which greatly reduce the AI application cost. Secondly, demand: with the development of the requirement of consumers’ personalization and product quality upgrading, the complexity of the manufacturing industry has been greatly increased, in terms of the organizational form, quality inspection, warehousing, and logistics. As the system becomes more and more complex, the learning curve will be slower, and the human ability will become the bottleneck restricting the progress and application of technology.
In the traditional industry, people’s decision-making and feedback are still the core, which devaluated the system in large part. The change that artificial intelligence brings to the industry is to get rid of the limitation of human knowledge and provide the quantifiable basis for decision support and collaborative optimization.
Now we would like to discuss the application of artificial intelligence in different scenarios, including production line, quality inspection, and warehousing logistics.
1. Application of Artificial Intelligence in the Production Line
For example, a production line sends out an incident alarm suddenly, and the machine can diagnose by itself, finding out where the problem has occurred, what is the reason and can also tell us how to solve the issue.
For example, in a power grid, if we apply conventional methods, there is only an 80% possibility that the grid problem can be reliably located. However, an advanced giant company has used deep learning technology to learn historical events, and through the relays distributed in the grid, people can better diagnose what problems and where the power grid is. Indeed, learning algorithms have been embedded in breaker products.
2. Application of Artificial Intelligence in Quality Inspection
Now there are many factories traditionally using the manual workforce to do a quality inspection. The quality inspectors need to spend more than 10 hours a day on the production line. In many factories, this job will be rotated once every two or three months, because the naked eye really can’t bear long-term inspection. Why the quality check could not be done with the help of technology before? The main reason is that the traditional visual equipment has a high rate of mistakes. It’s about 20 percent or even 30 percent.
The most important ability of artificial intelligence is its ability to learn. For example, AI will make the same mistake as the traditional system for the first time. But it will not make the same ones the second time and the third time. Facing the same question or similar question, next time AI will make a very accurate result. However, the traditional system will make the same mistake next time unless the program is modified.
With the use of deep learning and neural network, computers can quickly learn to do automatic detection work. Now as the interaction of AI, the misjudgment rate will reach 3% — 4%, and will gradually reduce to the minimum.
3. Application of Artificial Intelligence in Warehouse Logistics
There are many loops in warehousing logistics, from warehousing sorting, location management, loading and unloading, outbound sorting to material transportation. The whole oops involve sorting robots, loading and unloading robots, warehouses, AGV cars, forklifts, etc. Computer vision is used for perception and map positioning, and machine learning and deep learning are used to realize path planning and obstacle avoidance. Through mathematical programming and other operational research optimization algorithm and genetic algorithm, the warehouse on and off-shelf management is realized.
Ant colony algorithm is used to coordinate multiple sorting robots. Based on artificial intelligence technology, the overall coordination of shelves, goods, robots, can be more quickly realized, so do product out of storage and warehouse shelf planning. In factory warehousing, various types of automatic assembly lines, automatic distribution, warehousing, and distribution robots have been gradually applied. Every material can have the optimal path and take the shortest time to deliver.
Common Labeling Tools in Manufacturing Industry
2D Bounding Boxes, 3D Bounding Boxes, Semantic Segmentation, Video Tracking
Common Labeling Types
- Object Recognition
- Object Tracking in Video
- Semantic Segmentation
Data, Algorithms, and Processing are Three indispensable Elements of AI.
Data is the starting point.
It makes sense for AI to start with data and take advantage of learning itself. In scalable data and the high-speed era, it is very convenient to use data to train artificial intelligence.
The original data is generally acquired through data collection, and the subsequent data cleaning and data annotation are equivalent to processing the data, and then transferred to the artificial intelligence algorithm and model for invocation.
If the data used in artificial intelligence training is not sufficiently diverse and unbiased, problems such as artificial “AI bias” may arise.
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Aware of those challenges, ByteBridge moves a big step forward through its automated data collection and labeling platform. It provides high-quality training data for the machine learning industry.
- ML-assisted capacity can help reduce human errors by automatically pre-labeling
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy
- Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output
- All results are thoroughly assessed and verified by a human workforce and machine
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%.
In addition, we can provide personalized annotation tools and services according to customer requirements.
- Clients can set labeling rules, iterate data features, attributes, and task flows, scale up or down, make changes.
For example, you can choose a Bounding Box and Classification Template on the dashboard:
Clients can monitor the labeling progress and get the results in real-time on the dashboard.
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
We can provide personalized annotation tools and services according to customer requirements.
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: firstname.lastname@example.org
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2 How the Data Labeling Service Empowers Robotics in 2021? — Part1
3 How the Data Annotation Service Empowers Robotics in 2021? — Part2
4 How Data Labeling and Annotation Services Empower Logistics in 2021?