REVOT
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+82 10 - 7287 - 7527
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Seoul Office
서울특별시 강남구 테헤란로79길 6, JS타워 3층 1342
1342,3F,JS Tower,6,Teheran-ro 79-gil, Gangnam-gu, Seoul, Republic of Korea
R&D Center
인천광역시 연수구 송도동 30-6, 센텀하이브 A동 3106호
A-3106, Centum Hive, Centum Hive, 301 Incheon tower-daero, Yeonsu-gu, Incheon
Address
Service Area
REVOT provides AI vision inspection and manufacturing AI solutions to manufacturers across Korea. Overseas implementation and technical support are also available depending on project scope and requirements.
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Q1. What is the difference between AI vision inspection and conventional machine vision?
A. Conventional rule-based machine vision is effective for inspections where clear criteria can be defined numerically, such as color, size, position, and shape. In contrast, AI deep learning-based vision inspection can be applied to defects with irregular shapes or complex patterns that are difficult to distinguish using predefined rules alone.
Not every inspection requires AI. Depending on the inspection target and defect characteristics, rule-based vision, AI deep learning, or a combination of both may provide the most appropriate solution.
REVOT first analyzes the inspection target and production environment, then designs the appropriate inspection method and algorithm accordingly.
If it is difficult to determine which approach is suitable for your process, REVOT can review the feasibility based on your inspection samples and defect criteria.
Q2. Can AI vision inspection be applied to our production process?
A. The applicability of AI vision inspection cannot be determined solely by the type of product. Even for the same product, the appropriate inspection method may vary depending on factors such as defect characteristics, image acquisition conditions, production speed, installation space, lighting conditions, and inspection criteria.
Even when a defect can be identified visually by an operator, additional imaging or lighting design may be required if the defect characteristics cannot be captured consistently by the camera.
REVOT reviews actual samples and process conditions to determine not only whether defects can be detected, but also whether the inspection system can operate reliably in the actual production environment.
Providing normal and defective samples, along with available process information, can help us evaluate the feasibility of applying vision inspection to your production line.
Q3. What should we prepare before introducing AI vision inspection?
A. During the initial review stage, it is helpful to organize the following information:
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Products and inspection areas to be examined
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Criteria for distinguishing acceptable and defective products
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Representative normal and defective samples
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Current production speed and available inspection time
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Available space for installing cameras and lighting
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Requirements for integration with existing equipment, PLCs, MES, or other systems
In particular, the more clearly the defect criteria are defined, the more precisely the target inspection performance and validation criteria can be established.
You do not need to prepare every detail before the initial consultation. REVOT can first review the available information and samples, then identify any additional data or conditions required for further evaluation.
If technical review is required from the early planning stage, REVOT can help identify the preparation requirements based on your process and inspection target.
Q4. What should we consider when selecting an AI vision inspection provider?
A. Rather than considering only whether a provider has completed similar projects, it is important to determine whether the provider can analyze your product, defect characteristics, and production environment and design an inspection method suited to the actual process.
Vision inspection performance is not determined by the AI model alone. The overall system must be designed appropriately, including cameras, lenses, lighting, image acquisition methods, data configuration, algorithm selection, defect criteria, and integration with production equipment.
During the consultation process, it is recommended to confirm whether the provider can clearly explain:
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Why a particular image acquisition method is being used
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Which inspection algorithm is appropriate and why
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What limitations or difficult inspection conditions may exist
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How the system will be validated under actual production conditions
REVOT prioritizes evaluating whether an inspection method can operate reliably in the actual production environment, rather than simply determining whether implementation is technically possible.
Q5. What determines the cost and timeline of an AI vision inspection system?
A. An AI vision inspection system is generally not a fixed-price product. It is typically designed according to the inspection target and production environment.
The project cost and implementation timeline are primarily affected by factors such as:
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Number of inspection items and inspection areas
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Required camera, lens, and lighting configuration
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Scope of rule-based vision or AI deep learning
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Availability and preparation level of training data
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Production speed and required inspection performance
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Scope of integration with PLCs, MES, sensors, and other existing equipment
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Additional functions such as result storage, statistics, reporting, and data analysis
For this reason, an accurate quotation and project schedule generally require an initial review of the product, defect samples, production process, and inspection requirements.
You can begin the consultation with the materials currently available. REVOT can then identify any additional information required and define the appropriate scope for feasibility testing and system evaluation.