Description: Anomaly detection in images refers to the identification of unusual or unexpected patterns within visual data sets. This process is fundamental in image analysis, as it highlights elements that deviate from what is considered normal or expected. Anomalies can manifest in various forms, such as damaged objects in industrial inspection images, irregularities in medical images that may indicate diseases, or abnormal behaviors in surveillance systems. Anomaly detection relies on machine learning and image processing techniques, where advanced algorithms analyze large volumes of visual data to identify features that do not align with established patterns. This ability to discern the anomalous is crucial across multiple sectors, as it helps improve quality and safety and enables informed decision-making based on accurate data. In a world where the amount of visual information is constantly increasing, anomaly detection becomes an essential tool for automation and process improvement in various applications.
History: Anomaly detection in images began to develop in the 1960s with the first image processing algorithms. However, significant evolution occurred in the 1990s with the rise of machine learning and artificial intelligence. As computational capabilities increased, so did the techniques for detecting anomalies, integrating statistical methods and deep learning. In the 2000s, the availability of large datasets and the development of convolutional neural networks (CNNs) revolutionized the field, enabling more accurate and efficient anomaly detection in images.
Uses: Anomaly detection in images is used in various applications, including industrial inspection to identify defects in products, medical analysis to detect diseases through radiological images, and surveillance to recognize suspicious behaviors. It is also applied in cybersecurity to detect fraud in digital images and in agriculture to monitor crop health using satellite imagery.
Examples: An example of anomaly detection in images is the use of deep learning algorithms to identify tumors in mammograms. Another case is automated inspection on production lines, where cameras are used to detect defective products. In the security field, surveillance systems can use anomaly detection to alert about unusual behaviors in public spaces.
