Abstract
Cracked preserved eggs can easily decay, emit a peculiar smell, and cause cross-infection. The identification of cracked preserved eggs during production suffers from low efficiency and high cost. This paper proposes an online detection and identification method of cracked preserved eggs to address this issue. First, the images of preserved eggs are collected online. Then, each collected image is cut into a single image of the preserved egg, and the images of different surfaces of the same preserved egg are respectively spliced by the sequential splicing scheme and the matrix splicing scheme. Finally, the data sets obtained by the two stitching methods are exploited to establish a deep learning detection model. The experimental results indicate that the MobileNetV3_egg model, an improved version of the MobileNetV3_large model, achieves the best recognition ability for cracked preserved eggs by using the matrix splicing scheme. The accuracy reaches 96.3%, and the detection time for 300 images is only 4.267 s. The proposed method can meet the needs of actual production, and the application of this method will make the identification of cracked preserved eggs more automated and intelligent.
Highlights
At present, traditional Chinese egg products still have a relatively low automation degree [1]
The results show that the deep learning models can achieve online detection of cracked preserved eggs, and the mode detection effect is independent of the position of preserved eggs
When the image size is reduced to 224 × 224 pixels, the image height and direction information is lost too much, leading to the low accuracy of the model on the data set obtained by the sequential splicing scheme
Summary
Traditional Chinese egg products still have a relatively low automation degree [1]. In the preserved egg industry, much work needs to be performed manually. 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. 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
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