Deep object detection framework for automated quality inspection in assembly operations
Fotios Panagiotis Basamakis, Angelos Christos Bavelos, Dimosthenis Dimosthenopoulos, Apostolis Papavasileiou, Sotiris Makris
Abstract
The recent advance of flexible production systems requires fast and objective quality inspection of products. Computer vision based deep Convolutional Neural Networks (CNNs), are suitable for such applications since they provide automated, non-destructive, and cost-effective techniques to accomplish the requirements, hence eliminating the human operators or other inspections. In this paper a deep learning object detection framework is presented, able to detect correct, misaligned, and missing objects in complex scenes of the production line. Furthermore, the proposed architecture provides interfaces that allow the seamless integration of the model with varying manufacturing systems.