What Is anomaly detection, and why is it important?

 

Anomaly detection is a critical issue that has been studied in a variety of academic and practical fields. Many outlier detection approaches are customized to certain application domains, while others are more generic. This article aims to provide a comprehensive and in-depth overview of works on outlier detection methodologies, with a particular emphasis on anomaly detection in the setting of advanced learning and surface defect detection.

What is anomaly detection?

The technique of recognizing out-of-the-ordinary features or occurrences in data sets is known as anomaly detection. it is frequently done on unsupervised learning, which would be known as "unsupervised anomaly detection". It is a prominent study issue in the field of machine learning, to discriminate between normal and aberrant samples in a dataset. Many algorithms for detecting anomalies have been developed expressly for certain application areas, while others are more general.

 

What are the different types of anomaly detection?

There are different types of anomaly detection with machine learning.

Supervised

A machine learning engineer must use a training dataset when performing supervised anomaly detection. The elements in the dataset are classed as normal or aberrant. These samples will be used by the program to extract patterns and detect anomalous patterns in heretofore unrecognized data. In supervised learning, the quality of the training dataset is critical. Because examples must be collected and tagged, there is a large amount of human labor required.

Unsupervised

The most frequent sort of anomaly detection method is unsupervised, and neural networks are the most well-known example.

By removing the need for manual labeling, artificial neural networks lower the amount of human labor necessary to preprocess instances. Neural networks can analyze unstructured data as well. Neural networks may find anomalies in unlabeled data while dealing with new data and applying what they've learned.

Semi-supervised

The advantages of the two prior methods are combined in semi-supervised anomaly detection methodologies. Unsupervised learning algorithms can be used by engineers to expedite feature learning and work with unstructured data. Developers may monitor and control what sorts of patterns the model learns by connecting it with human supervision. This enhances the model's predictions in general.

What is anomaly detection used for?

Anomaly detection is a technique for discovering out-of-the-ordinary patterns that do not fit expectations. Anomalies are a synonym for abnormalities. This approach has various applications.

Intrusion detection

Cybersecurity is crucial for many firms that deal with sensitive information, intellectual property, and employees' and clients' personal information. Intrusion detection systems examine the network and report potentially dangerous traffic. If any suspicious conduct is detected, the IDS software notifies the team.

Defect detection

Companies that supply their clients with faulty systems or mechanism information may risk millions of dollars in lawsuits. A single flaw in manufacturing standards may cause a plane to crash, killing hundreds of people.

Outlier detection systems based on computer vision are capable of identifying minute flaws in sheet metal used in plane manufacturing, even when there are thousands of similar parts. Anomaly detection systems can also be coupled to controllers that monitor internal systems such as fuel levels, engine temperatures, and other factors.

Health surveillance

In the medical profession, anomaly detection systems are specifically valuable. They help doctors diagnose patients by detecting unusual patterns in MRI and test data. Neural networks trained on thousands of samples are typically utilized here, and they may deliver a more accurate diagnosis than specialists with 20 years of experience on occasion.

Detection of Fraud

Machine learning fraud detection aids in preventing the illicit acquisition of money or property. Fraud detection software is used by banks, credit unions, and insurance companies. Before making a decision, banks, for example, analyze loan applications. If the system uncovers that some of the documents are fake, such as your tax number not being in the system, it will notify the bank's boss.

Anomaly Detection in Images

Anomaly detectors aim to handle the tough problem of identifying abnormalities in a background image, which could be anything from cloth to mammography. Since each problem demands a distinct backdrop model, thousands of detection algorithms have been presented. Examining prior techniques demonstrates that the task can be reduced to detect anomalies in residual pictures (obtained from the target image), where noise and irregularities predominate. As a result, the general and insoluble challenge of background modeling is substituted by a simple noise that allows for the calculation of tight detection criteria. Unsupervised anomaly detection, which can be applied to any picture, is the best approach. The extensive features of neural networks could be usefully employed to calculate residual images.

What is a surface defect?

The faults and limitations of current technology, working circumstances, and other variables all have a substantial impact on the quality of manufactured items during the industrial production process. Surface flaws are the most visible indication that a product's quality has been compromised. As a result, detecting surface flaws in products is crucial to maintaining a high qualification ratio and consistent quality. The absence, imperfection, or area that differs from the typical sample is referred to as a defect.

Surface defect detection is the identification of scuffs, defects, foreign object shielding, color contamination, holes, and other defects on the surface of the test sample to collect a set of essential knowledge such as the category, contour, location, and size of surface defects on the test sample. Manual defect detection was formerly the typical method, but it is inefficient; detection results are frequently impacted by human subjectivity and cannot meet the standards of real-time detection. Other methods increasingly took their place.

Surface defect detection using deep learning

Artificial intelligence is currently widely employed in a wide range of societal applications. In the field of industrial automation, effective surface defection is crucial to the quality control of the industrial environment. The conventional manual detection method is time-consuming and ineffective for large-scale goods. Another key source of difficulty in manual detection is that numerous variables affect detection accuracy. Deep learning, which has gained prominence in recent years, particularly with convolutional neural networks (CNN), has done well in a variety of computer vision applications, including image recognition and classification. As a result of recent advances in deep learning, CNN is now commonly used to target industrial inspection duties.

Deep learning approaches use appropriate classifiers to address fault detection and classification. Deep convolutional neural networks are one of the most successful technologies for picture categorization on huge datasets, having been created by various deep learning algorithms on the subject of surface defect assessment. A completely convolutional neural network (CNN) is a network that includes two different fully convolutional networks for segmentation and detection.

Anomaly detection in Saiwa

Anomaly detection automates the difficult task of detecting anomalies or faults in a background image. Identifying uncommon occurrences that differ from the normal cases that constitute the majority of a dataset, we investigated several types of surface defects in Saiwa and will continue to add anomalies in the future. For each instance and dataset, several deep networks for classification and segmentation are used.

Currently, 15 different datasets and surface defect detection methods are available for testing. These datasets include surface defects such as metal, steel, polymer, and texture. You can freely test the algorithms on your images utilizing our simple UI, and if you like, you can leave us a modification request to retrain the networks on your unique dataset or various sorts of surfaces and defects.

 

The features of the Saiwa anomaly detection service:

 

• Detecting several types of anomalies using a single interface.
• There are 15 different datasets of various flaws on metal, steel, polymer, and texturing surfaces covered.
• We provide cutting-edge, latest deep learning-based algorithms for each dataset.
• Deep neural networks with several classifications and segmentations
• Each dataset contains preview examples of faults.
• Image aggregation is used to apply the method to several photos at the same time.
• Preview and save the results.
• The results can be exported and archived locally or on the user's cloud.
• The Saiwa team can customize services by using the "Request for Customization" option.