Experimental Study on the Accuracy and Precision of Object Size Measurement with Dual-Lens Imaging Systems

The demand for automated dimensional measurement technology is growing daily with global supply chain management challenges. Accurate dimensional measurement is critical to optimizing packaging design, reducing shipping costs, improving warehouse efficiency, and enhancing the inventory management process. Traditional manual measurement methods are time-consuming and error-prone due to measurement and data collection errors. There are hidden concerns about the accuracy of the data, and they cannot meet the fast-paced, high-efficiency demands of modern logistics. To address this problem, this study introduces an automatic three-dimensional object size measurement system based on dual-view imaging technology. The experimental environment uses top-angle and side-angle lenses, combined with the image processing technology of the OpenCV framework, to accurately measure the object's outermost three-dimensional dimensions. The image processing algorithm used in the research can automatically identify the outline of an object and calculate its length, width, and height. The development of the system includes precise camera calibration, simultaneous image acquisition, and image processing technology to ensure the accuracy and consistency of measurement results. The research demonstrates the experimental verification of accurately using images to calculate the contour feature information of objects. It conducts measurements based on various samples, proving that it can measure the outer dimensions of objects of various materials and shapes and has strong practicability and broad application prospects. The contribution of the research not only provides new solutions for dimensional measurement in the logistics field but also the experimental empirical model will expand the development of logistics warehouse automation systems with innovative and forward-looking data calculation and application.