Using Synthetic Data to Fine-tune YOLOv10 for Order Accuracy Validation

In today’s fast-paced manufacturing environments, ensuring the accuracy of outgoing orders is a critical task. Traditionally, this process has relied on manual verification, which can be time-consuming, error-prone, and subject to human fatigue. To address these challenges, this white paper explores a vision-based solution built by the team at OnLogic. The solution outlined below leverages a fine-tuned YOLOv10 real-time object detection model to automatically detect and verify components within an assembly cell. By augmenting the assembly line with cameras, and integrating synthetic data into the training process, this solution can improve the accuracy and efficiency of order fulfillment.

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This paper describes the technical details of this approach, including data collection, model training, and testing infrastructure, as well as the potential benefits of this solution for manufacturing operations.

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