What Garment Machinery Suppliers Don't Tell You About AI Fabric Inspection

In textile and garment manufacturing, fabric quality directly affects cutting, sewing and final delivery. Traditionally, fabric inspection has relied on experienced inspectors to identify holes, colour spots, oil stains, snags, foreign fibres, horizontal bars and other defects by observing the fabric surface. However, as fabric roll volume increases, material types become more varied, or factories require more complete inspection records, relying on manual inspection alone makes it harder to maintain consistent results.

This is why more suppliers are introducing AI fabric inspection machines to help factories locate defects, record their positions and generate inspection reports. However, installing an AI fabric inspection machine does not mean that every fabric defect problem will automatically disappear. Fabric types, defect image data, tension control, camera configuration and daily operation all affect actual inspection performance.

Below are four common myths about AI fabric inspection machines, followed by six points that equipment suppliers may not explain clearly before purchase.

Is AI Able to Solve Everything? 4 Common Myths About AI Fabric Inspection Machines

Myth 1: Once an AI Fabric Inspection Machine Is Purchased, It Can Immediately Inspect Fabric Accurately

What actually happens: An AI inspection machine first needs to understand the factory’s fabrics and defect standards

An AI fabric inspection machine is not a one-click system that can immediately process every fabric type and every possible defect. Different factories work with different fabrics, customer standards and common quality issues. Even defects described by the same name, such as colour spots, oil stains or abrasion marks, may be handled differently depending on the fabric type, colour or position on the final garment. At the beginning of implementation, the user and equipment supplier need to clarify:

• which fabrics the factory mainly processes;

• which defects appear most frequently;

• which defects must be marked;

• which surface conditions remain acceptable;

• whether the inspection results will later be used by the cutting room.

Only when defect definitions and classifications are clear can the AI training results better reflect actual factory practice. In addition, AI fabric inspection is not only related to software. Whether the fabric passes smoothly through the inspection zone, whether stretch fabric is pulled during inspection, and whether the lighting and cameras remain stable will all affect image-recognition results.

Myth 2: The More Data You Have, the Better the AI Performance Will Be

What actually happens: Having data does not guarantee good AI; clearly classified defect image data matters more

AI does require data to train its model, but a larger quantity of data does not automatically produce better inspection results. If defect image data is not clearly classified, such as when the same stain is labelled under different categories, or normal fabric texture is mixed into defect samples, the AI may learn inconsistent judgement. The result may be missed defects or excessive detections that increase the amount of manual secondary inspection required. Useful image data for fabric inspection should include:

• clear images of actual defects;

• the corresponding fabric type and colour;

• consistent defect names and classifications;

• examples of acceptable and unacceptable conditions;

• defects that frequently occur in daily production.

Instead of collecting large amounts of mixed data at the beginning, it is more practical to start with defects that appear most often and have the greatest impact on cutting and quality, such as holes, oil stains, colour spots, foreign fibres, yarn knots, snags and horizontal bars.

Myth 3: A Factory Can Simply Wait Until Other Manufacturers Have Trained the AI Model and Then Adopt It Directly

What actually happens: An existing model can provide a starting point, but it cannot automatically represent results on your own fabrics

Using an AI fabric inspection machine with an existing defect model can reduce the time required to begin organising data from scratch. However, different factories may use fabrics with different constructions, colours, elasticity, surface shine and finishing methods.

For example, the appearance of an oil stain on dark fabric is not the same as on light fabric. Uneven elasticity may also appear differently across different knitted materials. Even when a defect carries the same name, the model’s ability to identify it still depends on the actual fabric surface.

Customer standards may also differ. A minor surface mark that is acceptable for one product may need to be marked for a high-end style or for a visible garment panel. Therefore, an existing model can help a factory start testing sooner, but before the system is placed into regular production, trials using the factory’s own fabrics and common defects remain necessary.

Myth 4: Once AI Fabric Inspection Is Introduced, Manual Inspection Is No Longer Needed

What actually happens: AI can support inspection, but experienced personnel are still needed to define and manage fabric quality An AI fabric inspection machine can help identify defects, record their positions and generate reports. This reduces the burden of having operators continuously watch moving fabric. However, AI does not completely replace experienced fabric inspectors or quality personnel. 

First, the factory and supplier still need to define which defects the system should identify. Colour stains, oil stains, abrasion marks, crease shading and uneven elasticity must be clearly classified before the system can handle them effectively. Second, when the factory introduces new fabrics, applies new customer standards or encounters new defect forms, personnel are still needed to decide whether these cases should be included in later defect image data and model adjustment. Finally, even when AI produces a defect distribution map and inspection report, the factory still needs to decide how the information will be used by the cutting room, quality team or production management.

The value of AI fabric inspection is therefore to convert part of experienced inspectors’ knowledge into a more consistent, recordable and traceable inspection process, rather than to completely remove human judgement.

6 Things AI Fabric Inspection Machine Suppliers May Not Clearly Explain

1. More Camera Stations Do Not Automatically Mean Better Inspection

When purchasing an AI fabric inspection machine, many factories first notice how many camera stations the system includes. However, additional camera stations do not automatically mean better inspection results.

Different defects require different imaging methods. Some surface problems may be identified under general lighting, while certain structural defects may require transmitted light or another imaging angle to appear clearly.

If a factory’s main fabrics and common defects can already be handled through a suitable single-station configuration, adding more camera stations may only increase equipment price and future maintenance work without directly improving inspection results.

Before purchasing, the supplier should clearly explain the purpose of each camera station and which fabrics and defects it is intended to handle, rather than only emphasising the total number of stations.

2. Each Camera Station Should Correspond to Clear Defect Types

Where a supplier provides one-station or two-station configurations, the factory should understand what each station is actually responsible for detecting. For example, surface defects such as colour spots, oil stains, snags or abrasion marks may be handled differently from structural problems such as broken warp, broken weft or thick-and-thin sections that become clearer under transmitted light or different imaging conditions. Before purchasing the equipment, the supplier should explain:

• which defects are mainly detected by the first station;

• which additional defects are covered by the second station;

• whether the factory’s regular fabrics genuinely require a two-station configuration;

• how additional stations affect the inspection report and operating process.

A factory does not need the highest number of camera stations. It needs a configuration that corresponds to its common fabric defects.

3. Higher Camera Resolution Does Not Automatically Mean Higher Inspection Accuracy

A high-resolution camera can capture more detail, but the inspection accuracy of an AI fabric inspection machine is not determined by camera resolution alone. Whether the fabric remains flat in the inspection zone, whether lighting is stable, whether the fabric speed is appropriate, and whether the software and AI model are suitable will all affect the final result.

In addition, when images become more detailed, natural fibre variation, acceptable fabric texture or slight surface differences may also become easier for the system to flag as abnormalities. If defect standards have not been clearly defined in advance, this may increase false detections and the time spent on secondary inspection. Instead of asking only about camera pixels, factories should review whether the supplier can test their own fabrics and provide actual defect-recognition results and sample inspection reports.

4. AI Fabric Inspection Requires Sufficient Computing and Data Management Capability

AI fabric inspection equipment is not only a camera system and a mechanical machine. It also includes image processing, model calculation, report generation and data storage. As the fabric runs through the machine, the system continuously captures images, identifies defects and saves inspection results. If a factory wants to review defects across different rolls later, or provide defect data to the cutting room, the way inspection reports and images are stored becomes important. Before purchasing, factories should understand:

• whether the equipment supports offline operation;

• how defect images and inspection reports are stored;

• whether reports can be searched and exported;

• who is responsible for software updates and model adjustments;

• whether the system can later support other quality management processes.

These points may not appear as the most visible specifications, but they directly affect whether the machine remains practical after implementation.

5. AI Inspection Performance Cannot Be Explained by a Single Accuracy Number

Fabric types vary widely, and defects can appear differently on different surfaces. An AI fabric inspection machine should not be treated as a system that can reach 100% accuracy with no missed defects or false detections. If a supplier provides only one inspection-rate or accuracy figure without explaining the tested fabrics, defect types, inspection speed and calculation method, the number offers limited value for purchasing decisions. Factories should pay closer attention to:

• whether the tested fabrics are close to their own production materials;

• which defect types were included in the test;

• what inspection speed was used;

• whether both missed defects and false detections were recorded;

• whether special fabrics or new materials can be tested separately.

Testing with the factory’s own fabrics provides more meaningful information than reviewing a single performance claim.

6. AI Fabric Inspection Still Requires Ongoing Maintenance and Adjustment After Implementation

An AI model is not trained once and then able to process every fabric permanently without further work. A factory may introduce new fabrics, change material suppliers, receive different customer inspection standards or encounter defects that were not previously included in its image data. When this happens, the system may need new defect images, revised classifications or further model adjustment. Therefore, when purchasing an AI fabric inspection machine, factories should look beyond equipment specifications and understand what ongoing support the supplier provides, including:

• whether new fabrics can be added for testing;

• whether defect categories can be adjusted according to factory standards;

• how software and models are updated over time;

• whether the inspection report matches actual factory use;

• whether support is available for operation and maintenance.

For a factory, an AI fabric inspection machine is not a one-time technology purchase. It is a tool that continues to be used and refined together with changing fabrics and quality-control methods.

How Does OSHIMA AI Fabric Inspection System Support AI Fabric Inspection?

OSHIMA EagleAi/Plus AI Fabric Inspection Machine can be applied to stretch knitted and woven fabrics, with an inspection speed of 10 to 40 metres per minute depending on fabric type.

For stretch and knitted fabrics, unstable tension during inspection may overstretch the material and affect how the fabric surface is interpreted. EagleAi/Plus uses tension-free handling with three-stage speed-controlled tension. Deformation of stretch and knitted fabrics within the inspection zone can be controlled within 5%.

For defect detection, the one-station configuration covers yarn knots, slubs, foreign fibres, warp abnormalities, weft abnormalities, broken weft, stop marks, horizontal bars, snags, holes, fabric joins, crease shading, solvent residue, colour spots, colour stains, dirt, oil stains, uneven elasticity and abrasion marks. The two-station configuration adds detection for broken weft, broken warp, holes, fabric joins, elasticity abnormalities and thick-and-thin sections.

After inspection, the system generates a defect distribution map and detailed report. In addition to identifying problems during inspection, factories can use the defect position and classification information for later quality management and cutting-room planning.

Conclusion

The value of an AI fabric inspection machine is not simply that a factory purchases a piece of advanced equipment. Its value lies in helping the production floor convert fabric inspection results into quality information that is more consistent, easier to record and more useful for later production decisions.

Before purchasing, factories should first organise their commonly used fabrics, major defects and current inspection methods, then use actual fabric trials to understand whether the equipment can handle their real problems. Compared with simply reviewing the number of camera stations, camera resolution or a single detection-rate claim, the supplier’s understanding of fabric characteristics, defect classification and after-sales support has a greater influence on long-term use. To understand how AI fabric inspection performs on your own materials, contact us to request a sample inspection report.