—In 3D attitude angle estimation, monocular visionbased 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. We propose a fully connected attitude
detection network (FADN) which combines neural network and
traditional algorithms for 3D 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
3D 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 3D 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
