  The uprising trend of deep learning in computer vision and artificial
intelligence can simply not be ignored. On the most diverse tasks, from
recognition and detection to segmentation, deep learning is able to obtain
state-of-the-art results, reaching top notch performance. In this paper we
explore how deep convolutional neural networks dedicated to the task of object
detection can improve our industrial-oriented object detection pipelines, using
state-of-the-art open source deep learning frameworks, like Darknet. By using a
deep learning architecture that integrates region proposals, classification and
probability estimation in a single run, we aim at obtaining real-time
performance. We focus on reducing the needed amount of training data
drastically by exploring transfer learning, while still maintaining a high
average precision. Furthermore we apply these algorithms to two industrially
relevant applications, one being the detection of promotion boards in eye
tracking data and the other detecting and recognizing packages of warehouse
products for augmented advertisements.
