What Is AI Visual Inspection?
AI visual inspection is the use of artificial intelligence (AI) to inspect the appearance of products or other objects automatically.
As an alternative to human visual inspection, devices such as cameras and sensors capture images of a product’s exterior. AI algorithms then analyze these images to detect defects and anomalies. This process is typically performed at the end of the manufacturing process to enhance quality control.
AI visual inspection relies on advanced pattern recognition and image processing techniques. It can detect defects with very high accuracy and is often automated, enabling inspections that are faster and more consistent than those done manually. This improves manufacturing efficiency and the quality of the final products.
Uses of AI Visual Inspection
AI visual inspection is used across a wide range of industries. Some common examples include:
1. Automotive Industry
In automotive manufacturing, where part quality is critical, AI visual inspection detects defects such as scratches, dents, and color inconsistencies quickly and accurately. This enables early detection of faulty products and ensures the delivery of high-quality items to customers.
2. Food Industry
Since product quality and contamination directly impact consumer trust, AI visual inspection detects defects, discoloration, and foreign substances in food products. It can also verify package seals and labeling to ensure product integrity.
3. Electronics Industry
In electronics, where reliability is crucial, AI visual inspection detects issues such as soldering defects and missing components, enabling manufacturers to maintain high standards.
4. Connector and Plastic Molding Industry
Connectors and plastic molded products are often produced in small lots with many variations. Visual inspection is a challenge in this field due to the diversity and volume of products.
AI inspection is an effective alternative, especially where manual labor deployment is difficult.
Types of AI Visual Inspection
AI visual inspection uses different machine learning models:
- Supervised Learning Models: These learn from labeled abnormal images and detect similar defects.
- Unsupervised Learning Models: These models learn only from normal images and flag anything different as an anomaly. The accuracy of these models has improved significantly in recent years.
One challenge with supervised learning is the need for many abnormal image samples. To address this, researchers are exploring the use of generative AI to create synthetic defect images and improve model performance.
AI visual inspection is increasingly replacing conventional visual checks and rule-based systems, where human-defined rules were used for inspections.
How to Select an AI Visual Inspection System
Several factors should be considered when choosing an AI visual inspection system:
1. Characteristics of the Inspection Target and Types of Defects
Each AI method has strengths and limitations. Consider the shape, size, and surface of the inspection target, as well as potential types of defects. Select a system suited to these conditions.
2. Imaging Environment and Process Integration
Since detection depends on image quality, factors like lighting, camera placement, and equipment setup must be optimized. It is also important to integrate the inspection system into the workflow, enabling automated rejection of defective products.
3. Scalability for Changing Requirements
The inspection system should adapt to changes in volume or product type, such as seasonal or custom products. Evaluate whether the system supports such scalability and whether the vendor offers adequate technical support.
4. Data Requirements
High accuracy depends on training data. The more diverse and extensive the dataset, the better the AI model’s performance. Collecting and preparing quality data is therefore essential.
