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【2020 Application Example】 AOI fabric inspector lowers the false negative rate, and reduced the re-inspection volume by 70%

Low detection rate, slow speed, difficult recruitment and high personnel costs

The textile industry has always been a labor-intensive industry. At present, almost all textile companies worldwide still inspect fabrics manually. There are three major pain points in manual fabric inspection: Low detection rate, slow speed, difficulty in recruiting workers, and high personnel costs On average, a fabric inspector can find up to 200 defects in one hour with a defect detection rate of about 70%.

However, inspectors are only able to maintain their concentration for 20 to 30 minutes at most, and their fabric inspection speed is generally limited to 20 to 30cm/s. Fabric inspectors become fatigued if they exceed this time and speed.

Domestic and foreign AOI fabric inspection machines purchased by textile manufacturers have not yet been officially integrated into the production line. At the beginning, 10,000 suspected defects could be detected in one roll of fabric. The detection rate was high but the accuracy [screening] was low. The number of suspected defects has been reduced to 7000, but is still not at the level of experienced inspectors.

High-speed cameras capture defects and record defect locations

▲High-speed cameras capture defects and record their locations

The rule-based defect identification method currently used by manufacturers requires a lot of adjustment time (about 1 to 3 months) before the manufacturers (site) actually uses it, and there is currently no solution to automatically correct the identification model after use. As a result, manufacturers need to spend extra time to adjust parameters. Therefore, it requires considerable cost for both manufacturers and clients (sites).

Current manufacturers' grey cloth inspection process

▲Current grew fabric inspection process of manufacturers

The specific method used by the guidance team and cooperating manufacturers to implement AI identification technology and learning framework (for model retraining) into the defect inspection process is described below

1. AI-based defect identification model:

Utilizes the large amount of image data collected (including fabrics with and without defects) to construct the defect detection model through machine learning, such as SVM, or deep learning object detection methods, such as SSD or YOLOv3. This model is used to determine the condition of the surface of grey fabric and determine if it is a normal product or a defective product, thereby achieving defect identification.

2. Identification model retraining framework:

If there is an error in the judgment of the visual inspector, the image will be marked and the data will be used in the dataset for re-training. After a certain number of misjudged data is accumulated, the system will automatically start the identification model retraining function, and the new model that is generated will automatically replace the old recognition model, thereby achieving the purpose of model update.

Post-project implementation grey cloth defect inspection process

▲Grey fabric defect inspection process after the implementation of this project

Low false negative rate and solves the challenges of labor shortage and higher quality requirements in the industry

This project uses a deep learning network architecture to reclassify defects that are detected, including real defects and false defects, and can further classify real defects and false defects to lower the false negative rate of traditional AOI solutions. This is expected to reduce re-inspection volume by 70% and above for fabric inspectors, eliminate concerns about implementation in the current production line, accelerate the application of AI-based AOI solutions by textile manufacturers, and solve the challenges of labor shortage and higher quality requirements in the industry.

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【導入案例】AI嘛會煮咖啡 無人烘豆機靠AI 精準設點與培養忠實客群
AI Can Make Coffee! Autonomous Coffee Roasters Relying on AI for Precise Location Setting and Cultivating Loyal Customers

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roasters in high-traffic areas, owners can use cameras to capture the crowd and assess whether the machine location has an adequate customer base, quickly analyzing whether to reposition the machines, and more easily targeting the best locations for middle and high-end coffee lovers The unmanned coffee roaster features a professional roasting mode interface, providing options based on the origin and variety of coffee beans, their roasting methods light, medium, deep, and related temperature, wind speed, and timing settings If improvement needs arise during the process, engineers can adjust firmware parameters and also assist in integration with the owner's ordering system Staff members briefly describe the operation of the autonomous coffee roaster 'Black Gold' penetrates deeper into coffee shops, science parks, and commercial buildings through AI This autonomous coffee roaster targets coffee connoisseurs and can be placed in middle to high-end coffee shops to roast more customized coffee beans than those available in bulk stores Upon completing a batch of coffee beans, it immediately provides them to professional technicians within the coffee shops for grinding and manual brewing The remaining roasted beans can also be taken home for brewing and enjoyment It also adds value to coffee shops by better understanding consumer preferences for coffee beans and launching more customer-attracting drink promotions and appropriate inventory management In addition to coffee shops, the autonomous coffee roaster can also utilize AI-based people counting analysis to precisely set up near scientific parks and commercial buildings, offering high-quality coffee beans for office brewing to internal employees with high coffee consumption needs Furthermore, implementing a physical membership system can initiate coffee bean purchase promotions or periodic payment incentives, thus attracting new clients and cultivating existing customer loyalty and retention The operation interface of the smart autonomous coffee roaster「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【解決方案】佐翼科技無人機導入高爾夫球場域 可節省一半人力
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"The so-called multispectral is to direct light with different wavelengths on the turf, and the reflected images are collected for analysis" Droxo Tech CEO Liu continued to explain that each plant absorbs light with different wavelengths, so multispectral imaging can determine the growth status of grass species At the same time, combined with AI image recognition, the distribution of pests and diseases can be accurately detected, and the amount of pesticide used is determined on this basis Cross-domain collaboration to build a multi-source turf image databasenbsp Using AI multispectral image recognition technology, Droxo Tech will collect visible light, multispectral, thermal images, and hyperspectral images to establish a multi-source turf image database to fully understand the growth cycle of Bermuda grass Droxo Tech has accumulated rich experience in agricultural AI drone pesticide spraying , but there are still many problems that need to be overcome to implement AI solutions in golf courses For example, it is necessary to establish a new pesticide spraying model and test flight methods, especially the application of multispectral image recognition PoC is not difficult, but actual implementation requires more test evidence, repeated inferences, and collaboration with plant experts This part must rely on the cross-domain integration of legal entities such as the Institute for Information Technology III, gathering more fields for verification, and creating a paradigm before it can be more widely adopted by golf courses There are not many international cases on the application of AI drones in golf courses During the verification process, it is not yet known whether it can be quickly copied to the next golf course However, Droxo Tech CEO Liu believes that through cross-domain collaboration, clearly defining the problems and listing them one by one, supply and demand parties can reach a consensus, propose solutions to each problem, and seek cooperation with internal and external resources Only then will we be able to gradually achieve the goal of making golf courses smarter and smoothly assist the industry with transformation Zuoyi Technology's CEO, Liu Junlin 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」