# Feature Extraction and Recognition of Chinese Mitten Crab Carapace Based on Improved MobileNetV2
## 1 Introduction  
The Chinese mitten crab (_Ericheir sinensis_), commonly known as the hairy crab or river crab, is renowned for its unique flavor and rich nutritional value, making it one of China's principal aquaculture products and a favorite among global culinary enthusiasts [1]. According to the \"China Fisheries Statistical Yearbook\" (2023) [2], China's 2022 cultivation output of Chinese mitten crabs reached 815,318 tons, with a total value of 50 billion RMB, underscoring its significant role in freshwater fisheries. The market for Chinese mitten crabs is extremely broad, ranging from upscale restaurants to family dining tables, and from online marketplaces to offline markets, with its presence felt everywhere. The numerous brands and trademarks registered, over a hundred in total, demonstrate its significant status in the aquaculture market. As market demand increases, issues related to the quality and safety of Chinese mitten crab products are also rising, such as the misuse of drugs during breeding processes and the practice of temporarily altering breeding environments to counterfeit branded products [3, 4]. This not only poses a threat to consumer food safety but also impedes efforts towards supply chain transparency and brand value establishment. To enhance the traceability of Chinese mitten crab products and combat counterfeit goods, various solutions have been proposed. For example, anti-counterfeit labels printed with barcodes or QR codes are attached to the crab's claws, allowing consumers to scan these identifiers with mobile devices to access product information [5]. This method incurshigh traceability costs and has poor environmental sustainability. Moreover, the anti-counterfeit tags are logically separate from the Chinese mitten crabs themselves, making it easy for unscrupulous merchants to recycle or counterfeit these identifiers. Studies show that the morphological growth of Chinese mitten crabs is influenced by genetic factors and the surrounding environment, with their carapaces exhibiting unique features such as grooves, protrusions, and textures [6; 7]. Similar to human fingerprints, the characteristics of the Chinese mitten crab's shell are unique and non-replicable [8], and short-term changes in the environment do not alter these morphological features [9]. Therefore, the recognition of the carapace has become an important means of distinguishing different Chinese mitten crabs [10]. [PERSON] and others were the first to use SURF and FLANN algorithms to extract and match feature points on crab carapaces, performing individual matching verification based on these features, which showed variability among individual crab carapace features, although this method is susceptible to disturbances from uneven lighting and noise [11]. Therefore, the individual recognition of Chinese mitten crabs requires a more accurate, reliable, efficient, and convenient method of recognition. Given the powerful feature extraction capabilities of deep learning, image recognition based on deep learning has been widely applied in studies on humans and certain animals and plants, such as facial recognition [12; 13], animal facial recognition (e.g., pigs and sheep) [14; 15; 16], and plant disease recognition [17; 18]. Some researchers have also used deep learning for the individual recognition of Chinese mitten crab carapace features. [PERSON] and other researchers and other researchers utilized a pyramid convolutional network to extract carapace image features of Chinese mitten crabs, combining it with a Multilayer Perceptron (MLP) to classify and recognize 100 individual crabs and achieving an accuracy rate of 98.88% [19]. Although this method achieved a high classification accuracy by enhancing the model's ability to extract image features, it cannot recognize Chinese mitten crabs not included in the training samples. The addition of new individuals requires retraining the entire network.