# Quality Grading of Dried Abalone Using an Optimized VGGNet
## 1 Introduction  
As one of the most popular seafood products in East Asia, abalone has high nutritional and medicinal value. However, it is susceptible to bacterial contamination and spoilage in the air [1]. By drying abalone, most of the water can be removed, affecting the environment required by bacteria and prolonging the expiration date. Dried abalone has a unique and delicious flavor. Consequently, dried abalone products have become popular and have significant economic value. The quality of dried abalone varies due to differences in the product processing, storage, and transportation conditions. Poor-quality dried abalone may be a danger to human health. To meet the requirements of different market demands and food safety, it is necessary to be able to determine the quality of dried abalone. Both the external quality (or appearance quality) and the internal quality of dried abalone are of importance. The external quality can be judged manually or automaticallyby optical image processing. At present, the research focused on internal quality has mainly relied on texture characteristics analysis [2; 3; 4] and nutrient composition analysis [5; 6; 7]. These methods require equipment such as texture analyzers and microscopes for quality detection, which are destructive to dried abalone and are not suitable for quality classification during production. During production, the quality of dried abalone is assessed using the light transmission method. If there are silhouetted internal cavities, the dried abalone is considered poor quality and will be screened out. However, there is a high rate of missed detection, and the internal quality of dried abalone without silhouetted internal cavities cannot be determined. The production location and processing year of dried abalone are also evaluation indicators of the internal quality of abalone, but these evaluation methods are too subjective. In order to meet the requirements of the rapid expansion of the dried abalone industry, we must urgently develop a more effective and nondestructive method for evaluating the internal quality of dried abalone. X-ray is an electromagnetic wave with an extremely short wavelength and high frequency, which allows nondestructive and rapid imaging [8]. Since [PERSON] captured the first X-ray images of human hands, X-ray technology has been applied in a wide range of fields, including materials science [9], medicine [10], Earth science [11], and more. X-ray has different absorption rates for materials with different densities, and this difference in absorption rate is reflected in the gray value of X-ray images, which can distinguish bones, soft tissues, and cavities with different densities. In addition, well-designed X-ray systems have been widely used for the identification and classification of various foods [12; 13; 14]. Due to the lack of a direct correlation between the internal quality and the external appearance of dried abalone, it is necessary to assess its internal structure. Traditional methods for analyzing the internal quality of dried abalone rely on histological analysis, combined with experiential judgment. This method not only has a low accuracy rate in identification, it also causes damage to the dried abalone samples. On this basis, we considered using X-ray technology to determine the internal quality of dried abalone. By utilizing X-ray technology, we were able to quickly conduct non-destructive testing on abalone. However, due to the complexity and diversity of information reflected in X-ray images of dried abalone, manual processing is challenging. Therefore, a method was required to extract and classify the features of the X-ray images of dried abalone. Deep learning performs well in image classification tasks. In the ImageNet Large-scale Visual Recognition Challenge (ILSVRC), a convolutional neural network, VGGNet [15], was proposed for image classification tasks. At the last ISLVRC in 2017, the winner, SENet [16], used a squeeze-and-excitation (SE) module to help the network focus on the important features.