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