# A Deep Learning Based Dislocation Detection Method for Cylindrical Crystal Growth Process
## Proposed Method
### Detection Method  
The aim of this research is to develop a model that can distinguish whether the habit line is normal or disappears. The irregular nature of these patterns would impose a big challenge for a traditional machine vision-based detection method. The task for target detection can be roughly divided into three phases: candidate region generation, feature extraction, and target classification [7]. In the past, most detection models use the sliding window technique to generate candidate frames. R-CNN [8] firstly uses a selective search algorithm [9] to generate candidate regions and then extracts features of each region, which are classified by SVM. Fast R-CNN [10] proposes a multitarget network that combines target classification and position regression into one network, but the initial frame creation is  
Figure 4: (a–g) Different patterns of normal habit lines. Figure 5: (a, b) Different patterns of disappeared habit lines. time-consuming. Faster R-CNN [11] uses a region proposal network (RPN) to generate candidate boxes and then extracts the feature matrix for target classification and bounding box regression. The feature extraction network of the RPN has two types: the Zeiler and Fergus (ZF) [12] network and the VGG [13] network. Compared with a detection model based on sliding window technology, Faster R-CNN has a more expressive feature map and faster detection speed. Therefore, Faster R-CNN was chosen as our model, which is a classic two-stage object detection model. Instead of training a model from scratch, we applied a fine-tuning transfer learning to a Faster R-CNN network pretrained on ImageNet. The model trained feature extraction, candidate region generation, classification and bounding box regression together, and it could achieve end-to-end detection and real-time performance. Compared with general target detection tasks, the DHL detection task has two unique characteristics: (1) the aspect ratio of disappeared habit line is less than 1:2; (2) due to high temperature and strong radiation in the furnace, there exists phenomenon such as the habit line is actually normal but it seems to disappear in the middle part (special case) as shown in Figure 4g. In other words, for the same habit line, there exists disappeared and normal effect in the same image, which will cause misdetection. In order to solve these two problems, on the basis of conventional Faster R-CNN, this paper uses ResNet-50 residual neural network [14] as the backbone to extract image features, the architecture is shown in Figure 6. The idea of residual learning is added to the traditional convolutional neural network, which effectively solves the problems of gradient disappearance and gradient explosion, so that the network can better adapt to a larger depth. Taking an image of \(600\times 800\) resolution in multiscale training as an example, the image feature is extracted through the convolution operation of the residual block. A convolution operation is performed by sliding a filter along the input, in each place, the element-wise multiplication of the matrices is performed, and the result is summed and then fed into the feature map. The \(1\times 1\) convolution of the dotted line block is to adjust the dimension of the channel at the first bottleneck so that it can be operated with the output of bottleneck. The anchor scale of RPN is changed to solve the target aspect ratio problem. Figure 6: Architecture of the ResNet-50 residual neural network. The detection framework is shown in Figure 7. Faster R-CNN uses RPN to replace the selective search method of Fast RCNN [10; 11], RPN and region of interest (RoI) pooling share the previous CNN. At the end of the model, a judgement layer is added, considering the category and position of the bounding boxes. The whole process is as follows:  
1. Firstly, preprocessing is performed on the collected image data sets of the cylindrical growth step.