How does AI image re­cog­ni­tion work?

AI image re­cog­ni­tion is a tech­no­logy that uses ar­ti­fi­cial in­tel­li­gence (AI) to identify, analyse and cat­egor­ise objects, people, text and activ­it­ies in images. We’ll explain exactly how AI image re­cog­ni­tion works and what areas of ap­plic­a­tion there are.

Image re­cog­ni­tion is an area of ar­ti­fi­cial in­tel­li­gence that already offers a wide range of possible ap­plic­a­tions for very different areas. For example, objects such as plants can be iden­ti­fied or you can search for products on the internet using photos. AI can also recognise people and then search for suitable profiles on social media. This is based on image re­cog­ni­tion, which we’ll explain in more detail in this article.

What is image re­cog­ni­tion and how does it work?

Image re­cog­ni­tion refers to the ability of computers to auto­mat­ic­ally recognise objects and people, as well as text and other elements in images and videos, and to classify them based on un­der­ly­ing training models. As a result, the AI knows, for example, that a cat is a cat. In the field of ar­ti­fi­cial in­tel­li­gence, the basis for the analysis is provided by machine learning, which can be used to train AI models to recognise and classify different data.

The AI generally works as follows:

• Col­lec­tion of data: AI requires multiple inputs in the form of image data. These images are often cat­egor­ised in advance so that the system learns patterns and re­cog­nises them later.
• Pre-pro­cessing: In order to train the system as well as possible, the images are prepared, for example, by adjusting the size and colours of the image data or removing effects.
• Ex­trac­tion of features: In the next step, the system extracts relevant char­ac­ter­ist­ics, known as features, from the image data. These include, for example, shapes, edges or colours.
• Model training: The processed data is then used to train a neural network. The aim here is for the model to learn to assign the extracted features to specific cat­egor­ies.
• Clas­si­fic­a­tion: Once the system has been trained, the model can analyse new, unknown images. Based on this and the learned patterns, objects or people are now re­cog­nised and assigned to cat­egor­ies.
• Fine-tuning and use: Later on, the model is refined more and more during use. This allows more precise ad­just­ments to be made for the desired area of ap­plic­a­tion, for example in the field of medical dia­gnostics, where scans from radiology are examined.

Which ap­plic­a­tion areas are the most important in the field of AI image re­cog­ni­tion?

AI image re­cog­ni­tion is now used in many in­dus­tries and everyday use cases — often without consumers being directly aware of it. The most important areas include:

• Health­care: In a field where accurate analysis of disease patterns or causes is crucial, AI image re­cog­ni­tion can assist in making medical diagnoses. This is used in radiology, for example, to analyse X-rays or MRI scans.
• Security and sur­veil­lance: When it comes to security tech­no­logy, AI is used to monitor public places. For example, AI uses facial re­cog­ni­tion at airports to scan whether people clas­si­fied as criminals or wanted persons are present in the flight area. AI image re­cog­ni­tion can also be used to restrict access to buildings.
• Mobility: Autonom­ous driving would not be possible without AI-supported image re­cog­ni­tion. AI re­cog­nises key factors such as traffic signs, other vehicles, people on the road and obstacles, and uses this in­form­a­tion to steer the vehicle. This is ensured by the fact that cameras and other sensors con­stantly provide input that must be processed in real time for a smooth drive.
• Industry: Image re­cog­ni­tion is used for quality control in the pro­duc­tion of goods and parts. This allows defective goods or parts to be detected and removed at an early stage in the pro­duc­tion process. Analyses are possible at a level of detail that is sometimes difficult for the human eye to see.
• Ag­ri­cul­ture: In this area, image re­cog­ni­tion using AI helps to identify the degree of maturity of plants, nutrient re­quire­ments or signs of pest in­fest­a­tion. Ag­ri­cul­tur­al busi­nesses often use drones for this purpose, which can cover large areas without moving other machines and thus con­trib­ut­ing to soil com­pac­tion, for example.
• Retail: Here, ar­ti­fi­cial in­tel­li­gence helps to make ordering goods more efficient, for example, by re­cog­nising products that are running low and auto­mat­ic­ally trig­ger­ing new orders. Some retailers also use AI to register products that have been selected, so that an automatic booking process is triggered at the end of the purchase. This elim­in­ates checkout times and makes the shopping ex­per­i­ence more efficient.

What are the op­por­tun­it­ies and risks of AI image re­cog­ni­tion?

Image re­cog­ni­tion ensures more efficient processes in many areas, as AI takes on many tasks that humans and machines can only perform with dif­fi­culty or in­ad­equately. In addition to the op­por­tun­it­ies, however, there are also risks as­so­ci­ated with the use of AI. These relate in par­tic­u­lar to the data basis and the training of ar­ti­fi­cial in­tel­li­gence, as these determine the quality of the analyses and later the results.

Op­por­tun­it­ies for image re­cog­ni­tion

• Greater ef­fi­ciency and better accuracy: The speed of analysis and the precision of the evaluated data can speed up processes and improve results, as manual eval­u­ation takes longer and can be subject to human error.
• In­nov­at­ive strength and new process stages: The use of AI enables new tech­no­lo­gies such as autonom­ous driving to be widely used. Image re­cog­ni­tion can also be used to automate key steps in man­u­fac­tur­ing processes or ag­ri­cul­tur­al pro­duc­tion.
• Per­son­al­ised customer ex­per­i­ences: Image re­cog­ni­tion through AI can in­di­vidu­al­ise the shopping process offline and online, not only improving the customer ex­per­i­ence, but also serving customer needs more ac­cur­ately, resulting in more sales.
• Improved safety en­vir­on­ments: In different locations, AI can react faster and more ac­cur­ately to changes in public spaces, ensuring safer in­fra­struc­ture at key trans­port­a­tion hubs or public places.

Risks of AI image re­cog­ni­tion

• Data pro­tec­tion and privacy: AI can improve public safety, however, privacy is often invaded by as personal data is collected and analysed — sometimes without the knowledge or consent of the in­di­vidu­als concerned. This in­form­a­tion can fall into the wrong hands and be misused for criminal purposes.
• Dis­crim­in­a­tion and training bias: AI systems always analyse new data on the basis of the data with which they were trained. For example, if training is pre­dom­in­antly carried out with light-skinned people, this can have a negative impact on dark-skinned people. This can lead to problems when accessing security-relevant areas, for example.
• Lack of trans­par­ency: AI image re­cog­ni­tion systems are complex, and the un­der­ly­ing training is difficult to un­der­stand. This can mean that decisions based on the results of the systems are not trans­par­ent. Decisions in the areas of law en­force­ment can therefore produce critical results under certain cir­cum­stances.
• Loss of human skills: The more AI and AI image re­cog­ni­tion replace human skills, the greater the risk of neg­lect­ing key skills. This can lead to a loss of human (spe­cial­ist) knowledge, for example in autonom­ous driving or medical diagnosis.
• Vul­ner­ab­il­ity to misuse: Where large volumes of data are stored and analysed, there are potential entry points for misuse by cy­ber­crim­in­als. For example, they could exploit AI image re­cog­ni­tion to track in­di­vidu­als, or ma­nip­u­late or com­pletely disable security systems.

Con­clu­sion: AI image re­cog­ni­tion must be used re­spons­ibly

The op­por­tun­it­ies arising from image re­cog­ni­tion are huge across all in­dus­tries and offer con­sid­er­able op­tim­isa­tion potential for a wide range of areas. However, due to the as­so­ci­ated risks, it’s important that the systems are used with the highest security standards in order to prevent misuse and, at the same time, comply with ethical standards. Trans­par­ency and di­ver­si­fic­a­tion of the database must also be taken into account when training AI. This will ensure that the tech­no­logy brings more benefit than harm in the long run.