Convolutional Neural Network Based Object Detection for Additive Manufacturing

Abstract

Efficient object detection is important for automatic manufacturing systems applications. This work proposes the use of deep learning neural networks for vision-based additive manufactured object recognition. Three deep learning based object detection architectures (SSD300, SSD512 and Faster R-CNN) are applied for detection of parts manufactured on a 3D printer. The object detection information is used to feed a vision-based robotic grasping task, as part of a robotic assisted additive manufacturing system. Transfer learning is applied and a high detection efficiency is achieved for the considered dataset.