# MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning
## 4 Experiments
### Experiment of Visual Navigation Application Beside Industry 4.0  
In order to discern whether it is possible to use the proposed MU R-CNN in applications beside Industry 4.0, the following experiments are carried out. In the previous section, we mentioned that visual navigation based on 2D tags has been widely used in AGV applications of Industry 4.0. In addition to AGV, 2D tag positioning can also be used in other visual navigation fields besides Industry 4.0. For example, it can guide unmanned aerial vehicles (UAV) for autonomous landing. UAV is increasingly used in target tracking, danger rescue and other tasks, which requires higher and higher guidance efficiency and accuracy [75; 76]. Like manned aircraft, the mission consists of three stages: take-off, cruise, and landing. However, the take-off and cruise phases are easier to complete than the landing phase, which can be achieved through relatively simple program control. In the landing stage, the probability of an accident is often higher than the other two stages due to the complicated operation and many ground interference factors. Current navigation technologies for autonomous landing include inertial navigation, GPS and visual navigation. The biggest disadvantage of inertial navigation is that the errors will be continuously dispersed with the passage of time, so cannot be used independently. GPS is the most widely used and relatively mature technology. It uses navigation satellites to achieve positioning and has the advantage of simple use. However, as GPS is a weak radio signal, it is easily interfered by other radio signals and obstructed by obstacles, which restricts the autonomous landing environment of UAV. Visual navigation has the following technical advantages: no ground and air navigation auxiliary equipment are required, so it is low cost; it is mainly reliant on the airborne camera to obtain navigation information; it is not affected by electromagnetics, has low power consumption and a strong anti-interference capability. Visual navigation based on cooperative target is a reliable method for autonomous landing of UAV [74; 75; 76; 77; 78; 79; 80; 81; 82; 83]. Accurate positioning of the cooperative target is the basis of autonomous landing system. Examples of cooperative target patterns are as follows:  
As can be seen from Figure 11, the cooperative targets are generally composed of black and white colors, with certain coding rules, and have strong visual characteristics. Broadly speaking, the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Dataset** & **Image Number** & **True Positive** & **False Positive** & **Precision** \\ \hline MU R-CNN + HMM & \(D_{6\ell 1}\) & 900 & 892 & 8 & 99.11 \\  & \(D_{6\ell 2}\) & 900 & 897 & 3 & 99.67 \\  & \(D_{6\ell 3}\) & 900 & 889 & 11 & 98.78 \\ algorithm in Ref. [38]+ & \(D_{6\ell 2}\) & 900 & 854 & 46 & 94.89 \\ HMM & \(D_{6\ell 3}\) & 900 & 858 & 42 & 95.33 \\ \multicolumn{6}{c}{} \\ \end{tabular}
\end{table} 
Table 7: Comparison of tracking effects between \(HMM_{0}\) and \(HMM_{1}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Data Set** & **n** & **Recall** & **Precision** & **t(ms)** \\ \hline MU R-CNN & \(D_{3}\) & 810 & 99 & 99.26 & 29 \\ with ResNet-101 FPN & \(D_{2}\) & 125 & 98.4 & 98.4 & 27 \\ [PERSON] in [31] & \(D_{3}\) & 100 & - & 97–98.5 & 52–58 \\ [PERSON] in [36] & \(D_{2}\) & 125 & 95.2 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The performances of proposed algorithm compared with relevant algorithms. cooperative targets shown in Figure 11 are also some sort of 2D code, but the pattern is simpler and contains less coding information than QR code. Therefore, 2D code instance segmentation methods can also be applied to such cooperative targets, at the same time QR tags can also be used for cooperative targets of the autonomous landing of UAV. One significant advantage of the QR code relative to the above cooperative targets is its high capacity, error correction, and high security.