# MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning
## 2 Related Works
Common classification algorithms include SVM, Adaboost, etc. After completing the task of object detection or instance segmentation, some standard should be used to measure the quality of the algorithm. The standard performance measure that is commonly used for detection and instance segmentation is IoU. For example, it has been widely used in Pascal VOC, MS COCO and other data sets. Given an image, the IoU measures the similarity between the predicted and the ground-truth region for an object present in the image and is defined as the size of the intersection divided by the union of the two regions. Therefore, the improvement of instance segmentation should take the improvement of IoU as the main target. Therefore, the main problems of traditional target detection algorithms are summarized as follows: Firstly, candidate region selection algorithms have high time complexity or generate a lot of redundant candidates; Secondly, the design of manual features is difficult, and features are lack robustness to various changes of the target. For feature extraction, in the field of deep learning, manual features are no longer used, but neural network is used for feature learning. The common tool is convolutional neural network (CNN). To solve methods of the target detection problem in CNN, there are roughly two categories of methods. One is a two-stage algorithm based on region proposal, such as the R-CNN series [41], and the other is a one-stage algorithm based on the regression method, which is typically represented by a YOLO series [42; 43; 44] and Single Shot MultiBox Detector (SSD) [45]. In two-stage target detection algorithms based on CNN, region proposal can be implemented in two ways. The first way is the traditional candidate selection algorithm, such as Edge Boxes [39], etc. This kind of algorithm uses the information of image color, texture, edge and so on. These methods are more efficient and accurate than the violent enumeration method of sliding window. Such methods can be summarized as region proposal + CNN, typically represented by R-CNN or Fast R-CNN. Another way of implementing region proposal is to use the region proposal network (RPN) for candidate selection, whose core idea is to use CNN to generate candidate regions. The typical representation is Faster R-CNN [41]. For the one-stage algorithms, CNN first extracts feature map from input image, and then carries out direct regression operation on these feature map to predict the borders and categories of the targets. As the region proposal process is omitted, the detection speed of the target is greatly improved, but positioning is not accurate enough. In Industry 4.0 and other QR tag application occasions, after detecting the target position, QR tag texture semantic segmentation is also required to decode the information which have encoded in the QR tag. Instance Segmentation can detect the position of tags while segmenting each instance. By doing this, both the detection and texture segmentation of QR tags can be solved simultaneously. However, instance segmentation is a challenging task in computer vision, because it requires classification and location at the instance level, while semantic segmentation requires classification at the pixel level. Early methods of instance segmentation, such as Deepmask [46], Sharpmask [47] and InstanceFCN [48], use Faster R-CNN to classify and locate instances, but the shortcoming of Faster R-CNN is that the spatial misalignment of target positioning [29]. In [49] FCIS presents the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. [PERSON] [29] through experimental verification shows that FCIS exhibits systematic errors on overlapping instances and creates spurious edges, and the effect was worse than the Mask R-CNN which is proposed in Ref. [29]. Mask R-CNN is a simple, flexible and general object instance segmentation framework, and it added a segmentation branch to the Faster R-CNN to segment the instance while detecting.