Minimum cuboid estimation of irregular shape products for reducing box packaging waste by using vision-based measurement

Abstract Today packaging waste is a prevalent issue due to the increase in deliveries from online shopping. Here a new approach to this issue of cardboard box packaging waste is proposed by adapting an image processing algorithm based on camera vision-based measurement that computes the optimal cuboid bounding algorithm of irregular shape products. The end result is utilized so that packaging workers select the appropriate product box. This approach may also be used as a preliminary process to optimize the packaging of many products into a single cardboard box. The system setup with two cameras is prepared to capture the overhead and sideview images, estimating the box’s width, depth, and height in pixels. This system may then evaluate feasible cuboids that minimize waste, in which the traditional Otsu’s thresholding method, proposed Otsu’s scheme, and 1-D gradient means are utilized to avoid inaccuracies created by shadows. Calibration is performed with a Rubik’s cube to convert the measurement from computer simulations to real-life dimensions. The computer simulations from the overhead and sideview images compared to the actual bounding box of the object show that the proposed algorithm yields superior performance by reducing the area error of the bounding box (%) of an overhead image and the height error (%) of a sideview image than the conventional Otsu’s method.