.svg){kind=logo}
Anomaly detection takes an enormous amount of time and requires expert skill. In many companies, the burden is concentrated on specific employees.
Consequently, many people may be troubled by the desire to save labor in inspection work or to solve the problem of personalization in anomaly detection tasks.
These challenges can be solved by introducing an AI-based anomaly detection system. In this article, we introduce the benefits and use cases of implementing an anomaly detection system. By reading to the end, you will understand the points to consider during implementation and the effects that can be obtained.
|
【Table of Contents】 |
1. What is Anomaly Detection?
Anomaly detection is a method of detecting unusual elements (anomalies) by comparing them with large amounts of data. Since the system takes over the detection of failures or irregularities, it becomes possible to save labor in monitoring tasks that previously required human intervention.
Anomaly detection targets various media such as images, videos, and text. Areas where anomaly detection is relatively common are divided into two categories: image-based and time-series data.
In image-based anomaly detection, anomalies within still images are detected. Since time is not considered in image-based anomaly detection, it primarily targets visual abnormalities.
On the other hand, time-series data anomaly detection identifies abnormalities in changes that occur over time. It is used for anomaly detection in areas such as electrocardiograms (ECG) and credit card fraud detection.
This article mainly focuses on image-based anomaly detection.
Main Learning Methods Used in Anomaly Detection
The following learning methods are mainly used for anomaly detection.
| Machine Learning Model | Method Overview | Application to Anomaly Detection |
| Supervised Learning | A method of training by providing both input data and the corresponding correct answer (label) | Learns data characteristics based on provided labels and judges whether new data is normal or abnormal. Since there is a correct dataset, more precise predictions are possible. |
| Unsupervised Learning | A method of learning using only data without providing correct labels. The model discovers hidden structures and relationships within the data on its own. | By discovering hidden structures/relationships on its own, it understands normal data patterns and identifies data points that deviate from them as anomalies. Very effective when pre-preparing data labels is difficult or impossible. |
| Semi-supervised Learning | A method that leverages results from supervised learning to partially provide correct labels to data in unsupervised learning | Labor-saving is possible since it is not necessary to assign correct data to all data points. By using labeled data, it is possible to obtain higher precision and reliability than unsupervised learning. |
| Reinforcement Learning |
A learning method where the system attempts trials under certain conditions, receiving higher scores for actions that get closer to the goal. Instead of directly providing correct labels, it learns through trial and error what actions yield high rewards. |
Suitable for exploring optimal strategies or policies in complex environments or situations involving many variables. |
Machine Learning Models Used in Image-based Anomaly Detection
The model particularly used for image-based anomaly detection is deep learning. Deep learning utilizes neural networks that mimic the structure of human nerves. It consists of an input layer, multiple hidden layers, and an output layer.
Since it has a high ability to capture complex features and relationships within data, it excels at identifying patterns and anomalies from complex image data.
2. Use Cases of Image-based Anomaly Detection
.jpg?width=1500&height=670&name=image%20(1).jpg)
Image-based anomaly detection is utilized in situations such as the following:
- Weather Prediction
- Safety Confirmation
- Visual Inspection
- Medical Diagnosis
Each of these is explained below.
・Weather Prediction
Abnormal weather can be predicted by leveraging past observation data. For example, the Japan Meteorological Agency is conducting joint research with JR East to develop a system for predicting abnormal weather such as sudden wind gusts.
When sudden gusts like tornadoes occur, vortices appear on Doppler radar. Leveraging this characteristic, they provided data on over 30,000 past vortices to train the conditions under which sudden gusts occur.
In this process, AI learns patterns from vast data points and applies them to future predictions. Deep learning is utilized to determine whether a vortex is accompanied by sudden gusts.
Anomaly detection technology is expected to dramatically increase the precision of weather forecasting and improve the effectiveness of early warning systems.
・Safety Confirmation
In the field of safety confirmation, anomaly detection technology realizes both efficiency and improved safety. By utilizing anomaly detection, employee belongings inspections can be automated, realizing labor savings in safety confirmation.
For example, if belongings inspections are automatically conducted at entry and exit points of factories or construction sites, forgotten necessary tools or misplaced tools can be identified with high precision. Misplacing tools at a site during maintenance management could lead to serious accidents.
Even fine points that are easily overlooked by the human eye can be confirmed with high precision through system inspections, thereby increasing overall safety. Inspections by systems can enhance safety because they reduce the possibility of errors compared to human confirmation.
・Visual Inspection
Image-based anomaly detection is also utilized in visual inspection, which requires high levels of skill. Traditionally, product quality inspections centered on human eye checks, but the introduction of anomaly detection systems has realized the automation of inspections.
For example, anomaly detection systems are used in locations requiring large-scale visual inspection, such as automobile paint surfaces or electronic equipment circuit boards. Inspections that previously took hours to days have been significantly shortened, and human intervention has been greatly reduced. By the system automatically detecting and analyzing anomalies, improvements in inspection quality and efficiency are achieved.
Visual inspection was a time-consuming task, but since the system performs it automatically, there is no need to divert human labor. There are cases where inspection time and effort have been reduced by more than half, and it is a field where utilization is progressing.
-
Medical Diagnosis
Anomaly detection is also utilized in medical settings. In hospitals, through the introduction of AI-based anomaly detection systems for diagnostic imaging such as CT and MRI, the determination of abnormalities that doctors previously performed visually is now assisted by the system.
Utilizing the system can reduce the risk of overlooking diseases, and even doctors with less experience can make accurate judgments. Furthermore, since data from around the world can be utilized for diagnosis, it will enable more advanced diagnostics.
3. Benefits of Using Image-based Anomaly Detection
There are four main benefits of using image-based anomaly detection:
- Realization of Advanced Detection
- Prevention of Personalization
- Cost Savings
- Improved Productivity
Each of these is explained below.
・Realization of Advanced Detection
Anomaly detection systems have the capability to detect anomalies with high precision on a 24-hour basis based on learned anomaly patterns. As long as the system has learned the anomaly patterns, it will almost never overlook them at any time during the 24-hour period.
On the other hand, humans will have moments when concentration breaks due to sleepiness or fatigue. If an anomaly occurs at such a time, the risk of delayed response or oversight becomes very high.
In contrast, AI systems are free from such physical constraints and can consistently continue to detect anomalies with high precision. Particularly in environments where rapid response is required to prevent the spread of damage, the introduction of this system is highly valuable.
・Prevention of Personalization
Once you learn how to use it, an anomaly detection system can be used without special skills or experience. Human-based anomaly detection often requires high skill levels and years of experience. Conversely, anomaly detection systems can be learned relatively easily and do not require specialized skills or experience.
Furthermore, the fact that anomalies can be detected simply by learning how to use the system makes technology transfer easier. By enabling more people to perform tasks that previously depended on specific skilled employees, it has the effect of distributing the workload and increasing business sustainability.
・Cost Savings
While initial costs for anomaly detection systems are often high, from a long-term perspective, overall cost efficiency improves through reductions in labor costs and other operating costs. Visual anomaly detection by the human eye incurs ongoing labor costs, shift management, and recruitment costs, whereas system-based detection has the potential to significantly reduce these costs.
If less human labor is required, costs related to employee shift management and recruitment can also be reduced. As a result, the operational burden on management is also lightened, allowing them to focus on more strategic tasks.
・Improved Productivity
By using an anomaly detection system, you can reduce the effort spent on anomaly detection. Allocating these resources to higher-value tasks will improve productivity. Companies that have previously spent a lot of effort on anomaly detection may be able to significantly increase their productive capacity.
If productivity increases, corporate profits rise, allowing for investment in further system implementation or DX promotion, thereby enabling greater efficiency. Therefore, the introduction of an anomaly detection system can be an important step not only in reducing costs and effort but also in long-term business growth strategies.
4. Points to Consider When Implementing Anomaly Detection
When introducing an anomaly detection system, you can maximize its effectiveness by paying attention to the following points:
- Understanding of the system is required
- A sufficient amount of anomaly data is required
- High-quality image annotation is required
Each of these is explained below.
・Understanding of the system is required
To maximize the utilization of an anomaly detection system, it is important that involved staff fully understand the system's mechanisms and functions. This includes how the system operates, what types of anomalies it can detect, and how to interpret and deal with the results.
If developing the system in-house, understanding of anomaly detection itself is required in addition to understanding the system. Especially when performing anomaly detection using machine learning, there are many things to understand, such as the mechanisms for detecting anomalies and the types/balance of data serving as the model's foundation.
If employees in charge of operations completely understand the system's functions, they can operate it more efficiently and respond quickly when problems occur. If there are no personnel familiar with anomaly detection, consider consulting with external experts.
・A sufficient amount of anomaly data is required
Basically, for an AI-based anomaly detection system to function effectively, it requires large amounts of both normal and anomaly data. This is because while the system can detect learned anomaly patterns with high precision, it may overlook unlearned anomaly patterns.
Therefore, in addition to anomalies that occurred in the past, let the system learn more possible anomaly patterns. By preparing a dataset containing many actual anomaly cases, the system becomes able to more effectively distinguish between normal and abnormal states.
However, when starting a new business or in situations where past data is limited, available anomaly data might be scarce. In that case, it is also possible to use unsupervised learning or semi-supervised learning methods that are effective even in situations with little or no anomaly data.
If the available anomaly pattern data is scarce, consider an approach where you first train the system using only available normal data and accumulate data as time passes.
・High-quality image annotation is required
In image-based anomaly detection systems, the quality of image annotation (labeling) is extremely important. When building a model with supervised learning, it is necessary to provide correct answers for each data point. That is the labeling task known as annotation.
Since labeled data serves as the foundation for the model to derive correct answers, high-quality annotation is required for the development of high-performance models.
High-quality annotation provides the base for the system to accurately detect and classify anomalies. Annotation is often thought of as a simple task of just providing correct answers to images, but it requires advanced technology, such as varying labeling methods according to the target or having to adjust resolution.
If annotation precision is poor and the required performance is not obtained in the model, it could lead to a situation where thousands or tens of thousands of images must be re-annotated. If a high-performance model is required, please keep in mind to perform high-quality annotation.
5. Summary
The utilization of anomaly detection systems is helpful for labor-saving, cost reduction, and the prevention of personalization. For companies spending a lot of effort on anomaly detection such as visual inspection and safety confirmation, why not consider introducing an AI system?
To make a high-performance AI model, please be conscious of high-quality annotation. That said, specialized knowledge and technology are required to perform high-quality annotation. If you need a high-performance model, consider consulting with an annotation specialist company.
Author
Latest Articles
