Three examples are provided to illustrate how the integration of optoelectronic technology and artificial intelligence enables users to achieve their goals more quickly and reliably.

View, identify, allocate - and execute in real-time as accurately as possible. Many target tasks based on visual image recognition may sound as simple as this, whether it's quality inspection, sample evaluation, or license and rights allocation, such as access inspection.

If artificial intelligence is added, the processing speed of target tasks can be increased, and ideally, the detection rate can also be greatly improved. The specific identification and evaluation depend on your business needs, as image processing systems have (almost) no limitations.

Why does the Optoelectronics Society influence many applications of artificial intelligence?

They enable artificial intelligence to be "seen" first and then analyzed. With the help of high-sensitivity sensors and camera systems, things that are initially invisible become visible in complex test sequences. Imaging technology provides high-resolution and high contrast images. By combining deep learning algorithms, more information can be extracted from images. This has led to a large number of unexpected research results in many application fields, such as scientific research, industrial manufacturing, healthcare, and the public sector.

example1-Through artificial intelligence, the analysis of samples in medicine becomes more reliable

In recent years, the application of artificial intelligence in the medical field has become increasingly promising. However, compared to other industries, some restrictions aim to protect patients in the best possible way. Therefore, the US Food and Drug Administration（FDA）In the2018A team of experts was established in the year to review and approve the use of deep learning algorithms in medical technology, which is an important step for the healthcare industry. But what are the benefits of artificial intelligence for healthcare?

Imagine there is a microscope in front of you, with a glass slide containing medical samples placed on it. After imaging the sample, the image displayed on the doctor's screen is incredibly stunning1500The large number of pixels in the ten thousand pixel range ensures high-quality images. It is precisely in this large number of pixels that you are searching for, for example, an obvious but small deviation, a so-called micro malignant tumor, which can serve as an indicator of a tumor disease. Usually their size is only300 x 80Microns, that is, only0.3 x 0.08Millimeters. In contrast, the hair thickness of a normal person is less than100Microns.

It may sound like a challenging diagnostic task, but it is an important core skill in the daily work of pathologists. On average, pathologists need about two minutes to analyze samples under traditional professional microscopes. And this is exactly where artificial intelligence can provide assistance and improve work efficiency.

JenoptikDeveloped a microscope camera with a search engine company to enable clinical doctors to work more accurately and efficiently. By marking key areas, pathologists can more easily identify key sample areas and conduct targeted and careful examinations.JenoptikThe microscope camera has been enhanced with artificial intelligence software. It can highlight specific areas on the sample that are highly correlated with disease detection. This is because the system has learned the pathological features of each micro malignant tumor from previous sample measurements and displayed these features in real-time on the image.

Meanwhile, through deep learning algorithms, the testing time of medical samples under a microscope can be reduced120Half in seconds to61In seconds. In addition, studies have shown that the probability of detecting diseases such as lymph node cancer increases91%In contrast, traditional methods such as immunohistochemistry have a detection rate of only83%.

example2-Artificial intelligence can ensure the quality of mass-produced products is qualified

In the production process, a decisive factor affecting efficiency and added value quality is to minimize the quantity of waste products as much as possible, so that the proportion of error free production parts and the finished product rate are close to each other100%For example, high-speed and online inspection cameras are used in many production areas to detect defects on the surface or shape of products during the production process. High resolution camera systems can not only detect product defects, but also provide accurate, high contrast images. Here, the combination of visual image processing and artificial intelligence has also improved the quality of error detection. In addition, errors can not only be detected more reliably, but also classified more accurately, making later adjustments and responses more informative.

for example-Welding head and screw head

The quality control of welds is very complex. This is because even seemingly good welding points show differences, which is why product defects are often difficult to identify. For example, the quality of screw heads is also similar. In addition, only a very small number of parts have defects. The deformation and dirt during the production process also pose additional challenges to the imaging inspection program.

With the help of neural networks and deep learning algorithms, cameras can quickly and clearly identify whether production parts meet the specified quality requirements. This analysis system is trained using a large database of parts. In this way, the system learned how to detect small deviations and separate the "good and bad parts" in the production process. With the help of optoelectronic solutions, this algorithm is based on high-resolution images. Combined with deep learning algorithms, these algorithms provide more accurate results in image analysis. If a new part is produced and also inspected, the algorithm will automatically recognize whether there are any abnormalities and continue self-learning training. By timely and systematic identification of errors in the production process, correct decisions can be made quickly.

example3With artificial intelligence, license plate numbers can be correctly read even under difficult conditions

The use of artificial intelligence also provides significant advantages in the field of road safety, such as in automatic license plate recognition systems, which are used for right of way and speed control.

The challenge is to correctly recognize license plates even in harsh conditions such as bad weather, darkness, or low light. Artificial intelligence systems improve the accuracy of reading rates by recognizing patterns and repeated elements, such as understanding the country of origin information conveyed by license plates. In principle, the software can also classify vehicles into cars, trucks, buses, or motorcycles.

Artificial intelligence software is directly integrated into traffic cameras, so no additional equipment or installation is required. For example, in the UK, integrating the automatic license plate recognition system into the national traffic information and search system has ultimately simplified work while making roads and communities safer.

Currently, there are hundreds of AI based devices across the UKANPRThe camera is working with * door. Due to their high accuracy reading rate, they help maintain the safety of communities and roads, as they can reliably and quickly identify vehicles of interest, allowing for appropriate intervention by * doors.

Traffic cameras not only enhance safety, but also have sustainability. Their use improves traffic flow, resulting in even speed. This in turn has a positive impact on the environment, as fuel consumption has decreased.

The above examples indicate that self-learning systems are entering daily life comprehensively due to their speed and reliability. However, in the foreseeable future, machines will not replace humans. Artificial intelligence is still created by humans and depends on the quality of the sample data used to train it.