How Does Anomaly Detection with Computer Vision Work?
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To remain competitive, modern businesses need to constantly adhere to high standards and keep up with the times. While traditional inspection methods can be time-consuming and prone to human error, anomaly detection with computer vision (CV) can transform quality control processes across industries.
This article explains how computer vision solutions development works for anomaly detection and its benefits.
Mechanism of Computer Vision in Anomaly Detection
To understand how computer vision is used for anomaly detection, you need to be familiar with its workflow, so here it is.
1. Data collection: The first step is to collect visual data, usually using cameras or sensors.
2. Image preprocessing: Raw datasets often require preparations to make them suitable for anomaly detection using computer vision. This stage can include:
- resizing original images or videos;
- filtering out unimportant or distracting elements (noise reduction);
- adjusting pixel values (normalization);
- focusing on the area of interest (segmentation).
3. Feature extraction: The system extracts relevant features or patterns from the images that reflect the objects’ normal appearance or behavior.
4. Comparison with learned patterns: Machine learning models previously trained on standard datasets compare the input data with the learned patterns. Any deviation from the normal range is then marked as an anomaly.
5. Alert: When an anomaly is detected, the CV system can trigger an alert, stop the process, or flag the problem for further human review.
Benefits of AI Anomaly Detection
Automated quality control and inspection
AI-assisted anomaly detection significantly reduces reliance on manual inspections and provides greater accuracy in detecting defects. A great example is the vehicle hail damage scanning system enhanced by CHI Software.
Typically, assessing vehicles for hail damage is a time-consuming and imprecise process. CHI Software’s solution uses computer vision to quickly scan vehicles and automatically detect even the slightest signs of damage without human involvement. This approach increases the inspection’s speed and efficiency and ensures quality consistency by reducing human error.
Cost-effectiveness and scalability
Computer vision software development can significantly reduce costs by automating inspection tasks. Once implemented, CV systems can be scaled to handle any production volume.
Improved accuracy of real-time monitoring
Computer vision systems provide real-time anomaly detection to identify and resolve problems during production immediately.
Data-driven decision-making
With machine learning and image recognition solutions development, companies have access to a wealth of data that has been collected and already analyzed. Automated data collecting and analyzing empowers businesses to perform predictive maintenance, optimize processes, and make better strategic decisions based on the detected patterns.
By applying AI to detect anomalies, companies can take a proactive approach and increase efficiency and profitability. We can cite our article about machine learning in the healthcare industry to give an idea of the changes that a combination of CV and ML can bring. The mechanisms can analyze patient records and medical images to predict the likelihood of conditions or diseases. This approach can significantly speed up and improve decision-making, even when it comes to human health.
But that’s only the beginning! Our original article contains CV development challenges and how to solve them, industry use cases, and more insights about CHI Software workflows. Click to read!
