Machine Vision-Enabled Automated System for Precise Dimension Measurement
Harshada Bhawar, Rucha Darshane, Anushka Kadam, Samruddhi Jambhale, H. T. Patil, Nivedita Daimiwal, Revati Shriram · 2025
The dimension measurement system is implemented using Raspberry Pi 4 and its camera module. The algorithm development is done using Python programming, majorly Open Source Computer Vision (OpenCV) and other allied libraries. Real-time accurate measurement of dimensions plays a critical role in various industrial and manufacturing processes, ensuring product quality, compliance with standards, and overall efficiency. Machine vision technology has emerged as a powerful tool for achieving precise dimension measurement, offering numerous advantages over traditional methods. The paper highlights the capabilities of machine vision in the accurate measurement of various objects with high repeatability and speed. This is a framework where accuracy about the dimension of real-time objects in 3 different classes like small, medium, and large-sized objects is estimated. In the proposed approach, the object is detected by using an edge detection technique, continuous contour measurement and computing Euclidean distance. Under uniform environmental conditions, the proposed system gives accurate results for diverse object sizes ranging in height from 2 to 30 cm. The accuracy of the results is reliably within a defined range, which corresponds to the distance of the camera from the object. The span for small-sized objects is 6 to 35 cm, for medium-sized sized it is 17.5 to 72.5 cm and for large-sized sized it is 37 to 85 cm.