Examples of visual data collection and model training application solutions

Program overview

The visual data collection and model training application solution examples are public industry application solution examples in the direction of visual data and model training. In response to insufficient defect samples, improved model stability, and multi-batch adaptation requirements, a closed loop of data collection, annotation, training, evaluation, and false detection reflow is presented. 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

Visual data collection and model training are the basis for the stable implementation of the algorithm system. The success or failure of the project not only depends on the model structure, but also on sample coverage, annotation specifications, data versions, evaluation methods, false detection and missed detection analysis, and continuous iteration mechanism. 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 scenes such as defect detection models, OCR recognition models, segmentation models, and anomaly detection models, 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 sample collection, data annotation, model training, and evaluation iteration. 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: a small number of defect samples, uneven category distribution, and insufficient model generalization capabilities; new batches, new lighting, and new working conditions will bring false detections, missed detections, and boundary samples; the training effect needs to be recorded in a version to facilitate customer review and long-term iterations. 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 data collection, model training, and error detection reflow, 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: formulating a collection plan to cover good products, defective products, boundary samples and on-site variation factors; establishing labeling specifications, quality inspection processes, training set/validation set division and evaluation indicators; forming model versions, test reports, error sample reflow and continuous iteration mechanisms. 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

• Collection specifications
• Annotation quality control
• Training and evaluation process
• Version management

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

• Data collection specifications
• Labeling and quality inspection process
• Model training report
• Version iteration record

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 defect detection models, OCR recognition models, segmentation models, anomaly detection models and similar needs. It is also suitable for reference by customers who are evaluating machine vision inspection, computer vision algorithms, 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.

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