Examples of application solutions for photovoltaic module appearance defect identification

Program overview

The photovoltaic module appearance defect identification application solution example is an example of a public industry application solution in the direction of new energy visual inspection. Facing photovoltaic module production and inspection scenarios, we present a visual inspection and data closed-loop solution for defects in glass surfaces, frames, ribbons, labels, and module appearance. This page is geared towards project evaluation and solution matching. It does not disclose customer names, production line scales, business data or unconfirmed operating indicators. It focuses on what similar computer vision projects need to be evaluated, how to organize the technical route, how to confirm the delivery content, and what information should be prepared for early communication.

Project background

The identification of photovoltaic module appearance defects focuses on the surface conditions of cells, glass, frames, ribbons, backsheets and modules. Visible light, infrared or other imaging modalities may be used in the field, depending on the type of defect, the inspection station and the customer's existing equipment. In project communication, it is first necessary to clarify the detection objects, work station locations, sample differences, rhythm requirements, installation space, existing equipment interfaces and acceptance methods. For scenarios such as component appearance inspection, incoming material re-inspection, production line sampling inspection, inspection records, etc., the same visual concept may require different camera angles, light source methods, lens focal lengths, trigger methods and judgment rules at different sites. Therefore, JIVISION usually starts with sample analysis and imaging verification, rather than directly applying a fixed template.

Customer needs and assessment points

Typical requirements for this type of project include surface defects, frame anomalies, ribbon status, and component consistency. In addition to the identification itself, it is also necessary to evaluate whether the test results need to retain pictures, whether the formula needs to be switched according to product model, whether it needs to be connected with PLC, robot, MES, WMS or host computer system, and whether batch reports need to be generated. The main pain points include: the component area is large, the surface is highly reflective, and local defects are easily affected by ambient light and shooting angles; the frame, ribbon, label, and glass defect types are different and require regional modeling; the inspection results need to be uniformly recorded with the batch, process, and defect type. These issues need to be confirmed through sample testing and field condition analysis, and are not suitable to be replaced by unverified universal indicators.

Technical solution

The technical solution revolves around photovoltaic components, appearance defects, and data closed loop, and is implemented in conjunction with the project delivery process. JIVISION will first evaluate the imaging stability, and then design the algorithm link and system interface. The plan usually includes: dividing the inspection area according to the component structure, designing multi-angle acquisition and stable lighting conditions; establishing defect detection, area segmentation, label identification and abnormal level determination processes; outputting defect screenshots, location coordinates, classification results and data reflow records. In actual projects, traditional image processing, deep learning detection, OCR recognition, instance segmentation, point cloud processing or rule review processes can be combined according to object characteristics, so that the system can both identify targets and explain the basis for judgment.

System architecture

• Component Imaging Solutions
• Defect segmentation model
• Regional positioning
• Report export

Implementation process

The implementation process generally includes requirement confirmation, sample collection, imaging experiments, solution verification, algorithm training or rule development, interface definition, on-site deployment, acceptance testing, and operation and maintenance handover. At each stage, sample conditions, parameter versions, judgment rules, and abnormal sample records will be precipitated to facilitate subsequent review. When customers subsequently add product models, defect categories or on-site workstations, they can continue to iterate based on the original data and interfaces.

Delivery content

• Component testing solution
• Defect identification model
• Regional targeting results
• Data reflow mechanism

Acceptance and iteration

The acceptance indicators should be determined based on customer samples, on-site test results and testing standards confirmed by both parties. Common evaluation dimensions include recognition accuracy, risk of missed detection, false alarm handling, processing speed, stability under illumination changes, data traceability and maintenance convenience. The solution example description on the public page is used to illustrate the technical route and delivery method. The final project indicators should be based on the customer's real samples, deployment environment and contract agreements.

Applicable scenarios

This solution example is suitable for component appearance inspection, incoming material re-inspection, production line sampling inspection, inspection records and similar needs. It is also suitable for reference by customers who are evaluating machine vision inspection, computer vision algorithm, edge AI vision, 3D visual measurement, robot vision or visual data training. For projects with uncertain technical routes, sample evaluation, imaging testing, and small-scale solution verification can be conducted first to confirm feasibility before proceeding to system development and on-site deployment.

FAQ