Reduction of annotation efforts for multiclass object detection by using a domain awareness data combination strategy

To train convolutional neural networks (CNN) it is common practise to collect a huge amount of data. This is cost intensive and often not applicable. Up to date several studies have investigated the concept of few shoot learning, e.g. 1-3 samples per class. Suboptimal is still the over fitting resulting from the gap between training data and representative test data in the application. Since this is still a field of intensive research, an alternative and common approach is transfer learning with data- and image augmented pictures. However, collecting and labelling data for fine-tuning can still take an enormous amount of time, when it comes to multiclass pictures in industrial applications like assembly kit verification. The kits often contain stock lists with a small interclass and a high intraclass-distance. A specific characteristic of stock lists is that parts are easily adaptable and exchangeable. To bring object detection closer to the industry, we successfully show a dataset driven approach that combines a single class collection of pictures, which we call single class (SC) dataset and adapt with a few samples the specific multiclass use case. In result, we use a model trained on a huge SC dataset that can easily and fast be adapted to specific industrial use cases.