Machine Vision vs. Computer Vision vs. Vision AI

Machine vision, computer vision, and vision AI describe overlapping approaches to interpreting images, with machine vision relying on deterministic rules for controlled industrial environments, computer vision using specialized learned models for tasks such as detection, classification, and segmentation, and vision AI serving as the broad umbrella that also includes vision-language models capable of open-ended natural-language interpretation. Rules-based systems offer consistency for stable, measurable checks but require recalibration when conditions change, while learned models can better handle variable products, lighting, and defect patterns but depend on representative data, evaluation, and retraining. Specialized vision models generally provide faster and less expensive production inference for defined tasks, whereas vision-language models are suited to flexible questions, descriptions, and ambiguous cases but may be less consistent. The guide recommends choosing tools according to environmental variability, required judgment, performance, and cost, then testing them on real images, training custom models where needed, and combining detection, image processing, confidence filtering, and visual reasoning into workflows. Roboflow is presented as a platform for comparing models, labeling and training data, building integrated workflows, and validating complete applications before deployment, including hybrid inspections that use a detector to isolate an object and a vision-language model to classify defects.

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