# MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning
## 4 Experiments  
### Instance Segmentation Experiment  
We adopt the following experimental environment to train MU R-CNN: NVIDIA GeForce GTX 1080 Ti GPU, 11G of video memory. The learning rate during training is set to 0.001, total epochs is 300, number of classes (including background) is two, batch number chosen during training is one, and other experimental settings are the same as in Mask R-CNN [29]. In Equation (2), we adopt that \(\lambda=2\). We use COCO evaluation metrics AP (averaged over IoU thresholds) to report the results, including APm, [EMAIL_ADDRESS], [EMAIL_ADDRESS], and APb, [EMAIL_ADDRESS], [EMAIL_ADDRESS]. Where [EMAIL_ADDRESS], [EMAIL_ADDRESS], [EMAIL_ADDRESS] and [EMAIL_ADDRESS] represent the use of IoU threshold values of 0.5 or 0.75 to determine whether the predicted mask or bounding box is positive in the evaluation, and m and b represent mask and bounding box, respectively. APm and APb represent the average values of AP (for masks and bounding boxes respectively) within the interval of [0.5:0.05:0.95] of IoU, and 0.05 is the step size. Five data sets are used to verify the proposed algorithm, and are represented as:  
\[\{D_{i}|i=1,2,3,4,5\}\]  
Data set \(D_{1}\): self-built data set, which contains 14,000 images from industrial application scenarios. This data set includes a total of 18409 QR tags. \(D_{1}\) is then divided into two parts \(D_{1t}\) and \(D_{1p}\). \(D_{1t}\) contains 7000 images (including 8774 QR code) which are used for training, \(D_{1p}\) contains 7000 images (including 9635 QR code) which are used to test the effectiveness and robustness of the algorithm. For the Data set \(D_{2}\) and \(D_{3}\), similar as the data sets used in Ref. [36] and [31; 34], they can be used to compare the algorithm proposed in this paper with those in relevant literatures. In Ref. [31; 34], \(D_{3}\) is divided into two databases named as \"dataset1\" and \"dataset2\", and we denoted them as \(D_{3,1}\) and \(D_{3,2}\), respectively. The samples in the datasets \(D_{1}\) and \(D_{2}\) are mostly from complex background, and QR tags have the rotation, uneven brightness, and blurring features, etc. These features can be used to test the stability of low-quality images. Some images in \(D_{3,1}\) have complex backgrounds, and most images in \(D_{3,2}\) have simpler backgrounds. Since the deep network heavily depends on the characteristics of the input images, for our experiments, we composite the data set of complex scenes after [PERSON]. [PERSON] [74]'s method by superimposing \begin{table}
\begin{tabular}{c c} \hline \hline
**Steps** & **Operating Process** \\ \hline
1 & First, input the test image into Mask R-CNN branch, and get output candidate regions \(R_{i}\) \\
2 & Segment the candidate images in the input image according to the bounding box and class of \(R_{i}\) \\
3 & Candidate images are then sent to the pre-trained \(M_{UNet}\) to output texture segmented QR tag \\  & image, and this is the final output of the MU R-CNN network \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction process of total MU R-CNN. QR tag on the COCO val2017 and UAV123 [75] datasets, which is named as \(D_{4}\) and \(D_{5}\). COCO val2017 contains 5000 images with complex backgrounds and superimpose them with QR tags to form data set \(D_{4}\). UAV123 is a UAV-based data set proposed to study how to deal with strong camera motion, target scale and illumination changes in single-target tracking, and it contains 123 video sequences, totaling more than 110 k frames of video images. We randomly selected some images from each video sequence, and a total of 5,000 images were chosen and superimposed with QR tags to form data set \(D_{5}\). We randomly divided \(D_{4}\) data set into two parts: \(D_{4t}\) and \(D_{4p}\), which are used for training and prediction respectively, accounting for 60% and 40% of the total number of \(D_{4}\) images. Use the same method to divide \(D_{5}\) into \(D_{5t}\) and \(D_{5p}\).