Vision AI Vs. Sensors

Industrial sensors are presented as suitable for simple, consistent physical measurements such as presence, position, weight, or a defined color change, while vision AI is recommended for more complex tasks including part identification, barcode and label reading, placement verification, orientation measurement, and visible defect detection. The comparison should consider complete station costs—including hardware, installation, integration, maintenance, lighting, compute, data labeling, model training, and future updates—rather than only comparing sensor and camera prices. The piece argues that vision systems can combine several inspections through a single camera feed, but must still be validated for image quality, production speed, defect-detection accuracy, false-reject rates, and decision latency to the PLC. Using Roboflow Workflows as an example, it describes a packaging inspection pipeline that detects packages and damage, reads barcodes and QR codes, and assesses package placement and orientation, with Roboflow Inference supporting edge deployment and PLC integration handling machine actions. It also emphasizes collecting production events and feedback to retrain and test models as products, defects, and operational requirements change, while retaining appropriate inspection records for quality reviews, audits, or claims.

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