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Keymakr video and image annotation services were developed to meet the demands of projects of any complexity. Our annotation team is completely in-house and consists of industry experts to ensure the optimum results for your computer vision AI.

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How Robotics AI Developers Handle Bias

POSTED 05/12/2022

Modern factories use robots at each stage of the manufacturing process. Robots help ensure the correct assembly of products and the safety and efficiency of the assembly line. However, further improvements are possible with #AI only. Automated robotic systems can act intelligently in a dynamic manufacturing environment.

We can't imagine the modern production process without the use of robots. They keep assembly lines safe and efficient, ensuring products are assembled correctly while minimizing the resources involved. However, further development in this direction is possible only in combination with #AI.

Automated robotic systems provide competent, coordinated actions even in dynamic conditions.

Logically, the use of AI in robotic systems is developing intensively. One of the key problems that 80% of robotics AI developers face is biased training data. The Keymakr team has successfully solved similar problems of various levels of complexity since 2015.

This article focuses on the critical points that you need to navigate before starting any such project.


Exciting potential!

Just think of the possibilities for manufacturers with the prospect of autonomous robots in factories and assembly lines. At least because of this, it makes sense to consider options for including robots in your business:

Environmental sensing: Robotic systems need the right conditions for safe and unhindered movement in a rapidly changing industrial environment. It is necessary to provide the possibility of planning the trajectory of motion, taking into account the avoidance of obstacles. At this stage, you can not succeed without the use of #image and #videoannotation. The task of these processes is to recognize and define objects by coordinating the movement vector. With a properly implemented design, assembly-line operation is safe and efficient.


Inventory and logistics: it's hard to believe, but smart robots are already indispensable today in organizing an extensive system of intra-warehouse logistics. The robots are able to sort packages for delivery, ensuring that products are separated from other materials. Listing, inventory control, optimization of storage space, and sorting products according to a given filter are just some of the tasks that can be confidently delegated to #AI. And yes, we can even use such a big word as manufacturing revolution.


Quality control: after several hours of work, the human eye ceases to perceive incoming information as well as at the beginning of the working day. For this reason, it can be difficult for production workers to identify minor defects equally effectively throughout the workday. #AI systems are well taught to detect the most negligible errors! AI models should be trained with thousands of annotated training images to achieve this option.


Predictive maintenance: The ability of the AI ??system to detect tiny imperfections is also indispensable for equipment maintenance. AI models are trained on machine images. This thoughtful approach allows early detection of wear and signaling to engineers that preventive action is needed to replace a critical part of the production line.


Complex Environments

Factories and assembly lines are dynamic and chaotic environments. If AI models are poorly trained in a busy and chaotic environment of factories and assembly lines, they can threaten safety and efficiency. Therefore, it is especially important to train the AI ??system on accurate, unbiased images and video data.


Specific bias challenges

Developers in this sector face several challenges when building training datasets:


Image and video quality. Poor picture and video quality negatively affects the performance of AI models. The key to solving this problem is collecting and creating high-quality training data that reflects actual conditions.


Diverse manufacturing locations: production conditions vary significantly even within the same country. In this case, the variety of training data will be very important. Such a flexible approach to the formation of training data will help to competently and quickly reconfigure character set recognition systems and assembly line schemes in different conditions.


Cultural differences: Manufacturing standards and practices can also vary greatly from country to country. Artificial intelligence systems must be trained to work effectively in a variety of work cultures.

Overcoming these bias issues can be difficult for any AI company. Keymakr is an annotation provider with experience collecting varied datasets and creating new images and videos for AI training.