Defective products can be a disaster for businesses. Errors in production can lead to customer dissatisfaction and the loss of future sales. Furthermore, faulty products can be dangerous, causing harm to customers and exposing businesses to litigation. Therefore, reducing the number of production mistakes and improving the quality of final products is a core goal for many companies.
However, this can be a challenge in any organisation. Functional quality control processes often require significant investments in staffing and increased management focus. As a result, many businesses have turned to machine learning and computer vision to help transform production quality.
AI models can spot production defects with a much higher success rate than humans. However, developers need quality AI training data so that this technology can be successful across a variety of industries. Crafting powerful datasets starts with finding the right video or image annotation tool.
This blog will look at industries in which quality control AI are beginning to play a major role. In each case we will show how annotation tools, like Keylabs, can streamline computer vision model development.
Making sure food and beverages are safe
Quality control is understandably of the utmost importance in the food and beverage sector. Mistakes in productions can lead to unsafe food and drink making its way to customers. If customers become ill from unsafe products then companies can suffer reputational damage and may have to pay compensation.
AI supported systems can inspect food and beverage production lines automatically. Machine learning powered cameras can be trained to identify defects in packaging or in key production processes. As a result companies can stop unsafe practises and stop faulty products from getting to shops and consumers.
Annotation speed is an important part of creating a dataset for computer vision models. Keylabs allows developers to speed up the annotation process with interpolation and task sharing features. Object interpolation tracks and locates objects through multiple frames of video. Keylabs also allows multiple annotators to work on one piece of training video, seamlessly merging annotations when the task is completed.
Automating the inspection process
Solar photovoltaic systems (solar PV) have become more efficient in recent years. As a result the demand for this essential technology is also increasing. However, quality inspection is essential for solar PV to be reliable. Small defects, or micro cracks, in panel surfaces can significantly reduce energy production, and make the panels vulnerable to bad weather.
AI models can inspect solar arrays before they are installed and throughout their working life. Annotated training images help computer vision models to learn how to spot almost invisible defects. This technology can be used on the production line or for regular inspections.
Managing a large data annotation project for solar PV inspection AI can be a daunting challenge. Keylabs gives developers a unique range of project management options. Workforce analytics help managers to give tasks to annotators who are performing well. Annotators and verifiers are also linked together. This creates a more efficient workflow.
Computer vision for circuit boards
Mistakes in circuit board production can lead to defective products. Keeping faulty circuit boards out of circulation is essential for many industries. As a result, developers are turning to AI to identify defects that may be missed by humans.
These systems are trained with annotated images of circuit boards, some of which feature defects. AI circuit board inspection can make the production process faster whilst increasing final product quality.
The Keylabs annotation tool is flexible. Keylabs workflow optimisation is fully customisable and allows AI innovators to incorporate their own code and control their annotation project.