FADN: Fully Connected Attitude Detection Network Based on Industrial Video

In 3-D attitude angle estimation, monocular vision-based methods are often utilized for the advantages of short-time and high efficiency. However, limitations these lie in complexity algorithm specificity scene, which needs to match characteristics cooperation object scene. this article, we propose a fully connected detection network (FADN), combines neural traditional algorithms estimation. FADN provides whole process from input single frame image industrial video stream output corresponding Benefiting end-to-end estimation framework, avoids tedious matching thus has certain portability. A series comparative experiments based on rendering software Studio Max (3d Max) have been carried out evaluate performance FADN. The experimental results show that accuracy fast running speed. At same time, simulation reliably prove feasibility FADN, also promote research real scenarios.