Dimension Measurement with Computer Vision

Measuring object dimensions is crucial for pass/fail inspections, especially in high-value or high-volume production settings, and computer vision can automate this process by returning dimensions such as width, length, and height. Two methods for building a dimension inspection system with computer vision are discussed: using a reference object and utilizing depth measurement capabilities. The process involves training an instance segmentation model on Roboflow, labeling images of objects, generating a dataset version, and training a model to detect specific items like ramen boxes and water cases. The reference object approach involves deploying the model to measure dimensions using a known object for conversion calculations, while the depth capabilities approach leverages stereo depth perception technology to calculate dimensions more accurately. Both methods have applications in quality assurance and package sorting systems, offering a logical framework for estimating object dimensions in dynamic environments.