Volume Estimation Method for Irregular Object Using RGB-D Deep Learning

The demand for non-contact object volume estimation has been increasing across various industries. Although image-based volume estimation methods have been studied, they are generally limited to measuring simple shapes and are not suitable for irregularly shaped objects. We propose a volume estimation method for a single object using color and depth images through a deep learning network. For the color image, the object region is detected using a pre-trained segmentation model. For the depth image, the heights corresponding to pixels in the object region are calculated as differences between the pixel values and a background depth image that has captured a floor without any objects. Then, the heights are divided into multiple 2 × 2 pixel units. The element volumes corresponding to the 2 × 2 pixel units are estimated by a deep learning network which takes heights in a 2 × 2 pixel unit as inputs. This network estimates the corresponding volume to this unit through multiple fully connected layers. All estimated element volumes are summed to obtain the object volume. The simulation results show that the average volume estimation error is about 2.37%.