Application of open Source Deep Neural Networks for Object Detection in Industrial Environments

Abstract

Due to dynamics, flexibility and diversity in logistics, perception-controlled, intelligent robots are required to automate logistical handling steps. Due to the additional optical influences of the industrial environment, such as labeling or damage, these applications seem predestined for the use of generalizing deep neural networks (DNN). These showed continuous improvements over the last few years based on publicly available data sets. If these DNNs are re-trained based on training data from the industrial environment, a lower performance can be observed. The additional extension of the experiments to international locations of the vehicle plants also showed that a drop in performance can be observed in the implementation of a network trained in Germany, for example, when it is used in America. However, in order to be able to use such robots in the logistic processes in the future, further measures such as a revised composition of training data or their extension by data augmentation are proposed.