# Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution
## 1 Introduction  
The production of preserved Szechuan pickle includes peeling fibre, cleaning, cutting and so on. The cleaning and cutting processes can be easily and efficiently implemented with automatic devices, but peeling is always done manually, which limits the yield and quality of preserved Szechuan pickle. An automatic peeling device is needed to effectively solve this problem. Contour detection, as an important part of the device, is a necessary process to identify the location of fibre and guide the cutting tool.  
In this paper, we address the problem of detecting contour in the fibre of preserved Szechuan pickle. The preserved Szechuan pickles consist of fibre, flesh and peel, as shown in Figure 1. The fibre of preserved Szechuan pickle is inedible, while the flesh can be eaten as a dish. In the productive process, the existence of fibre is not allowed and it is necessary to peel off the fibre.  
By analyzing the section of the preserved Szechuan pickle stem, the contour of the preserved Szechuan pickle can be seen as one kind of non-salient contour. It is unsmoothed, irregular and the gray distinction between the fibre and flesh is small. The characteristics are not obvious enough and there is a large difference between individuals. The contour we studied in this paper is non-salient, which means that it is difficult to distinguish the contour from the background in texture, color and other aspects. This special contour also puts forward higher requirements for detection.  
Contour detection has broad application prospects and is an important part of object segmentation, target detection, recognition and tracking [1]. There is a trend of using convolutional networks to detect contour since convolution neural networks show a strong capability to learn high-level representations of images. The effective integration of these methods and machines has promoted great developments in detection, such as the fully convolutional network (FCN) [2], holistically-nested edge detection (HED) [3], richer convolutional features for edge detection (RCF) [4] and so on. The features of the contour have the characteristics of large intra-class differences and small differences between classes. The general contour detection method has limited ability for these features. At present, there is little research into non-salient contour, but it is also one of the necessary technical means for production.  
For this contour, we improve the structure of the HED network and use the output of each stage effectively. Our method automatically learns rich hierarchical representations and is able to make multi-scale predictions. We use dilated convolutions [5] to increase the receptive field. It is useful to reduce the loss of information and enhance the acquisition of spatial hierarchical information to improve the detection effect of the non-salient contours. In a complex background, our method can detect the region of contour. Finally, the Pixel Accuracy (PA) of our method [2] is 99.52% and the mean intersection over union (MIoU) [2] is 49.99%. Compared with the HED and RCF, the PA of our results increased by 3.80% and 9.61%, and the MIoU increased by 1.91% and 4.83% respectively.