# High-Throughput Online Visual Detection Method of Cracked Preserved Eggs Based on Deep Learning
## 1 Introduction  
At present, traditional Chinese egg products still have a relatively low automation degree [1]. Especially in the preserved egg industry, much work needs to be performed manually. Preserved eggs are one of China's traditional egg products that are made of fresh eggs with special pickling. There may be bumps during the handling and cleaning of raw eggs and the transportation of preserved eggs. In this case, cracked preserved eggs are generated, which are prone to corruption, odor, and cross-infection [2] and cannot be eaten [3]. To ensure the quality of preserved eggs, the factory needs to manually remove cracked preserved eggs in the production process, which wastes much manpower. In addition, the manual work reduces the production efficiency and is easy to cause secondary damage. Therefore, it is urgent to develop an online detection technology for preserved egg cracks to improve production efficiency, reduce the production cost, and realize automatic and rapid removal of cracked preserved eggs in the egg industry.  
The predecessors mainly used vibration signal analysis [4, 5], acoustic characteristic analysis [6], machine vision [7, 8], and other [9] technical means to detect the crack of fresh poultry eggs. Among the detection technologies applied to the online detection of poultry eggs [10, 11], machine vision technology is more mature [12]. In comparison, the use of vibration signal analysis and acoustic characteristic analysis in online detection is faced with several problems, such as difficult signal acquisition and more signal interference. Especially during the pickling process of preserved eggs, the eggshell is corroded and more fragile than fresh eggs [13], which is easy to cause secondary damage and expandthe crack of preserved eggs. Although the application of the polarization image [3] and acoustic characteristics [14] have been proposed to detect the cracks of preserved eggs, these methods have the problems of low accuracy and high detection cost, and they cannot solve actual production problems.  
Due to the interference of the spots on the surface of preserved eggs, traditional machine vision algorithms fail to achieve a suitable effect of preserved egg crack feature extraction. In recent years, deep learning has developed rapidly [15], and deep learning algorithms have advantages in solving image recognition problems under a complex background [16]. Therefore, deep learning algorithms have been applied to quality detection [17] and crack detection [18] of poultry eggs, and some deep learning models have been successfully applied to online detection [19; 20; 21]. This makes it possible to apply deep learning algorithms to solve the problem of online detection of preserved egg cracks. This paper aims to introduce the deep learning algorithm into the machine vision detection of preserved egg cracks. First, taking duck eggs as raw materials and preserved eggs as test materials, the images of preserved eggs are collected online on the three-channel egg conveyor. Then, a reasonable image preprocessing algorithm is designed to make the collected images meet the requirements of the deep learning recognition algorithm. Finally, an online deep learning detection model of preserved egg crack based is established to realize high-throughput online detection of preserved egg crack.