# MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning
## 5 Discussion  
As shown in Table 5, compared with Mask R-CNN, both the instance segmentation and detection results of our MU R-CNN can achieve a stable improvement on all backbone networks. Especially for [EMAIL_ADDRESS], all the values of MASK R-CNN are 0, and our MU R-CNN reaches 13.83 with ResNet-101 FPN backbone, which demonstrates the superiority of QR tag instance segmentation by the proposed MU R-CNN. Compared with MS R-CNN, the APm value of our MU R-CNN is also about 6 AP higher In terms of QR tag detection, our APb improved by about 1 AP and 2 AP compared with MS R-CNN and Mask R-CNN, respectively. The detection effect of MU R-CNN is about 8 AP higher than that of YOLOv3, indicating that under the complex background environment, the detection ability of MU R-CNN for QR tag is significantly better than that of YOLOv3. Since the test set D\({}_{\text{pr}}\) contains diversity information, especially D\({}_{\text{lp}}\) from the factory environment, and D\({}_{\text{2}}\) and D\({}_{\text{4p}}\) are mostly from complex background, therefore, the test results on the D\({}_{\text{pr}}\) data set can indicate the applicability of MU R-CNN to industry environment. Figure 11: Cooperative targets of UAV autonomous landing based on Visual Navigation in related literatures. (**a**) [PERSON] [74]; (**b**) [PERSON] [78]; (**c**) [PERSON] [79]; (**d**) [PERSON] [80]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Total Targets** & **TP** & **FP** & **FN** & **Precision** & **Recall** & **F1-Measure** \\ \hline MU & & & & & & \\ R-CNN & 2000 & 1998 & 1 & 2 & 99.95 & 99.9 & 99.92 \\ YOLOv3 & & 1992 & 2 & 8 & 99.90 & 99.6 & 99.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the effects of MU R-CNN and YOLOv3 in UAV environment. From the QR tag tracking results shown in Table 7, the \"MU R-CNN + HMM\" method proposed in this paper is much better than the \"algorithm in Ref. [38] + HMM\" method. We conclude that the main reason for this is that their observation state v\({}_{2}\) in HMM is quite different. In the \"MU R-CNN + HMM\" method, mask output from UNet branch of MU R-CNN is used to calculate v\({}_{2}\), while the method \"algorithm in Ref. [38]+ HMM\" uses Gaussian, median filtering de-noise and OTSU binarization to calculate v\({}_{2}\). The results in Table 7 show that the \"MU R-CNN + HMM\" method is more accurate, which verifies the good instance segmentation effect of MU R-CNN on QR tag. Both MU R-CNN and YOLOv3 show good results for QR tag detection in the images taken by the UAV's onboard camera as shown in Table 9, possibly because the background of the UAV123 data set is relatively clean, making the detection task easier to complete. Figure 12 shows some of the images in D\({}_{5\text{P}}\), which are clean and of high quality. In a UAV autonomous landing mission, if the cooperative target encodes the landing site information, after detecting the target position, QR tag texture semantic segmentation is also required to decode the information which have encoded in the QR tag. MU R-CNN can detect the position of cooperative targets while segmenting each instance. By doing this, QR tag texture semantic segmentation result can be used for subsequent QR decoding, conveniently. But YOLOv3 has no instance segmentation function, so MU R-CNN has better adaptability than YOLOv3. [PERSON] s. in [31] selected 100 high-quality images from \(D_{3}\) for implementation. Hence, according to the comparison results illustrated in Table 8, the algorithm in Ref. [31] is only suitable for locating QR tags with high-quality images. However, the proposed MU R-CNN is applicable to both high- and low-quality images, so the algorithm proposed in this paper is more suitable than Ref. [31] for PTS, AGV, and other applications in the context of Industry 4.0. While our MU R-CNN improves the detection ability of QR tags in low-quality images, its prediction speed is higher than that reported by [PERSON] s. [31].