One of the typical applications of deep learning in artificial intelligence (AI) is image recognition. Familiar examples include face recognition in smartphones. AI is expected to be used in various areas such as building management and the medical field. In this article, we will discuss the applications of AI in image recognition.
AI-based image recognition is a technology that uses AI to identify written characters, human faces, objects and other information in images. The accuracy of recognition is improved by having AI read and learn from numerous images. Image recognition is a form of pattern recognition, while pattern recognition refers to the overall technology that recognizes objects that have a certain meaning from various data, such as images and voice.
AI image recognition uses machine learning technology, where AI learns by reading and learning from large amounts of image data, and the accuracy of image recognition is improved by learning from continuously stored image data.
Here are four typical inference models using AI image recognition.
This inference model detects people, objects, and vehicles in images. People detection checks for congestion on streets and in open spaces, and the behavior of people at work in construction sites. Vehicle detection is used to check for congestion on highways.
This technology detects the skeletal structure and posture of the human body by recognizing information about the head, neck, hands, and other parts of the human body. Deep learning technology is used to detect not only parts of the human body, but also optimal connections between them. In the past, skeletal structure and posture detection required expensive cameras that could estimate depth, but advances in AI technology have made detection possible even with ordinary monocular cameras.
This technology recognizes the eyes, nose, mouth, and other information from 2D or 3D image information and checks against a database of pre-registered facial information to authenticate a specific person. Since the outbreak of the COVID-19 disaster, some products can now recognize people even with their masks on, while others can measure temperature.
This technology identifies diseased locations from medical images (CT or MRI), such as cerebral aneurysms. In recent years, it has become possible to obtain high-resolution CT and MRI data. By having AI learn from large amounts of stored high-resolution image data, the accuracy of the technology to identify diseases has also improved dramatically.
Specific systems are built by using the above inference models, either alone or by combining several of them.
What are some specific systems that use AI image recognition technology? Here are five typical applications of AI image recognition technology.
This system uses biometric authentication technology based on AI image recognition to control access to buildings. The access control system uses biometric authentication technologies such as facial recognition, iris recognition, and fingerprint recognition to identify individuals and allow them to enter and exit without touching the authentication device. Since each biometric authentication has its own strengths and weaknesses, some systems combine multiple biometrics for authentication.
This system combines vehicle, object, and people detection to detect intrusions in designated areas. Intrusion detection system is used to detect vehicles violating parking regulations, trespassing at railroad crossings, trespassing in restricted areas and other intrusions.
This system uses images from security cameras, which have been used to detect crimes, to proactively detect people behaving suspiciously on trains. The introduction of the suspicious behavior detection system is expected to prevent terrorism and other crimes before they occur.
The suspicious behavior detection system detects mental states based on minute tremors of the human body. AI image recognition is used in technologies such as to quantify and automatically classify behavior patterns.
This system uses AI cameras and other devices to detect vehicles and monitor road traffic conditions. Road conditions such as increased traffic can be indicated in real time by using road signs. AI image recognition is also used in technologies that measure road surface conditions and how poor visibility is in bad weather.
At factory production lines, quality is determined by visual inspection. The quality of a product is determined based on whether there are defects, such as whether the components on a printed circuit board are mounted properly, or whether there are scratches on the exterior of an industrial product.
Food products such as meat and fish, in particular, are unlike industrial products in that no two food products have the same shape. This makes it difficult to apply conventional rules to determine whether they meet quality requirements, and AI is attracting attention as a more effective way for quality control.
Contec offers edge AI computers for implementing AI image recognition systems.
This general-purpose Industrial Edge AI Computer is equipped with an NVIDIA® Jetson™ module and prioritizes practical use. You can use it as an on-site inference environment in various environments.
This is a high-performance FA computer that you can use either as a learning environment or as a high-performance inference environment. It comes with a choice of large-capacity RAID storage, high-capacity 1000 W power supply, and expansion slots for high-end GPU cards.
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