A DCNN-based Arbitrarily-Oriented Object Detector for a Quality-Control Application

Following the success of machine vision systems for on-line automated quality and process control, in this paper we describe an object recognition solution aiming at detecting the presence of quality control elements in surgery toolboxes prepared by the Sterilization Unit of a hospital. Our solution actually consists in a two-stage arbitrarily-oriented object detection method making use of indirect regression of oriented bounding boxes parameters. The paper describes the design process and reports on the results obtained up to date.