AI Defect Detection Solutions

What is AI defect detection?

AI defect detection applies computer vision and machine learning to automatically identify products or materials that fall below quality standards. By analysing images and sensor data, defect detection systems can spot visible and hidden defects which include surface scratches, cracks, internal issues within material, dimensional deviations, or contamination with foreign matters.

Most AI defect detection setups rely on cameras and computer vision models, but manufacturers may add data from machines and environmental sensors, including vibration, temperature, or humidity, to detect defects sooner in the process or get predictable outputs. Learn about predictive maintenance or IIoT development to learn more about it.

A practical example of an AI defect detection system that’s based on computer vision is the wood defect detection system developed for Prohan, where deep learning models were trained on real production images to spot defects in glued wooden panels. The system automatically flagged faulty wooden panels before they reached final assembly, and achieved 90% accuracy at the PoC stage. Read the case study: Computer Vision System Eliminates 90% of Defects in Wooden Furniture Production.

Learn how AI can automate defect detection in your industry. Share your project details and get a scope that you need to define a defect detection solution that targets your issue.

What types of defects can AI detect?

AI defect detection systems can identify a wide range of product defects depending on the data available. The goal of such systems is to automate quality inspection processes that are traditionally time-consuming and prone to error.

Defects detected by AI solutions generally fall into two categories: surface defects, visible to standard cameras, and internal or structural defects, which require specialised imaging equipment such as X-ray or infrared sensors. By analysing data from these imaging sources, computer vision algorithms can recognise irregularities that signal quality issues, classify them, and perform some automated action, such as alerting the quality inspector.

Overall, AI defect detection systems provide a scalable and flexible inspection method that can adapt to various defect types and materials. If you want to learn what’s possible in terms of defects you need to catch, fill out the form below and our specialist will help you.

Surface Flaw Inspection

Surface flaw inspection is one of the most common applications of computer vision in manufacturing. Cameras mounted near the production line capture images of each product as it moves through the line. Deep learning algorithms then analyse these images to detect surface flaws that they were trained to spot. Some systems use diffuse or polarised lighting setups to eliminate reflections and increase contrast, which is a way to improve detection accuracy for reflective or textured materials like metals, ceramics, or painted surfaces.

Dimensional Defect Detection

Dimensional accuracy is critical in industries such as precast concrete or jewellery-making, where even small deviations can affect performance or assembly compatibility. AI-based dimensional inspection uses input from cameras or 3D scanners and measures shape, size, and position. Then, it compares the measurements against reference CAD models and checks if they are within tolerance.

Color and Texture Analysis

AI models can also detect surface inconsistencies related to colour or texture. These defects might include discoloration, uneven coating, stains, or variations in material finish. Computer vision systems can analyse pixel-level differences across an image and compare them to baseline samples.

Missing Parts Detection

In assembly lines, AI inspection systems identifies missing, extra, or misplaced parts by comparing images of the assembled product to a digital reference. This approach is widely used in manufacturing, where a small assembly error can lead to functional failure or product recalls.

Alignment Issue Detection

Alignment inspection ensures that all elements of a product, like labels, welds, connectors, seams, or printed patterns, are positioned correctly. Computer vision systems analyse symmetry and spacing which is vital for high-precision manufacturing, such as PCB production or metal fabrication

Foreign Object Detection

Build an AI-powered system that discovers the presence of contaminants or foreign matter that may affect the quality of the product, and reduce the reliance on human inspection. Spot debris, dust or other foreign particles.

Metal

Metal surfaces can be reflective, textured, or coated, which makes them difficult to analyse with standard lighting. To increase accuracy of defect detection, engineers often use diffused or polarised illumination to minimise glare and highlight surface irregularities such as cracks, corrosion, scratches, or weld defects. Thermal imaging is also useful for identifying heat-related issues in welded or cast components, as temperature anomalies often indicate internal flaws or incomplete bonding.

Plastics

Plastics come in a variety of colours, levels of transparency, and finishes, which makes it challenging to inspect. Engineers use backlighting or structured light to reveal deformation, inclusions, or air pockets. For thin or transparent materials, polarisation filters improve contrast and reduce reflections. Advanced models can also evaluate colour uniformity and detect subtle surface defects such as sink marks or flow lines formed during moulding.

Wood

Natural materials like wood are inherently variable and no two pieces are identical. Deep learning models are particularly effective here, as they can learn to distinguish between natural grain variations and true defects such as knots, cracks, or warping. RGB cameras paired with line-scan technology can perform inspection on moving panels or planks which works even in a fast-paced production environment.

Glass

Glass inspection requires a different approach because of its transparency and reflective nature. Computer vision systems rely on backlight illumination to reveal internal bubbles, inclusions, or thickness variations, while polarised lighting reduces glare and highlights surface scratches. In some cases, laser triangulation or interferometry is applied to check the surface for defects.

Concrete

Concrete surfaces are rough and non-reflective, yet they can still present subtle irregularities that indicate quality issues. AI models can be trained on texture patterns to identify cracks, pores, or surface contamination. When combined with 3D scanning or thermal imaging, systems can assess surface integrity, curing quality, and more.

Here are some custom AI defect detection examples

Steel manufacturing defects

Identify defects such as surface scratches, dents, rust, or cracks in steel sheets. High-resolution cameras coupled with machine learning models can quickly analyze the surface of steel products, identifying even the smallest imperfections that could compromise product quality.

Metal casting defects

Computer vision solutions can detect defects such as air pockets, cracks, or other irregularities in the mold. The system scans images of castings, allowing manufacturers to sort out defective parts before further processing, reducing waste, and making sure that products are in line with quality standards.

Quality control of lumber processing

Computer vision is used in the wood industry to detect defects such as knots, cracks, or warping in wood panels or lumber. The system can classify different defects based on size and type, allowing operators to separate defective products from those that meet quality control requirements.

Welding defect detection

Computer vision systems can be used to inspect welds on metal components, identifying problems such as cracks, incomplete fusion, or porosity. Machine learning algorithms analyze images or video streams of welds to detect defects that would be difficult for the human inspectors to find, ensuring the structural integrity of critical components.

Textile defect detection with vision AI

In textile industry, AI systems are capable of inspecting textile materials for issues like surface pilling, fraying, or irregularities in texture. These defects can be detected early in the manufacturing process, preventing the shipment of products with inferior material quality.

Jewelry authentication

Computer vision systems can be used to authenticate jewellery by analysing details of precious metals. These systems compare visual patterns gainst verified reference samples to confirm authenticity and detect counterfeits.