Feature Extraction and Recognition of Chinese Mitten Crab Carapace Based on Improved MobileNetV2

Abstract

The Chinese mitten crab (Eriocheir sinensis), a species unique to Chinese aquaculture, holds significant economic value in the seafood market. In response to increasing concerns about the quality and safety of Chinese mitten crab products, the high traceability costs, and challenges for consumers in verifying the authenticity of individual crabs, this study proposes a lightweight individual recognition model for Chinese mitten crab carapace images based on an improved MobileNetV2. The method first utilizes a lightweight backbone network, MobileNetV2, combined with a coordinate attention mechanism to extract features of the Chinese mitten crab carapace, thereby enhancing the ability to recognize critical morphological features of the crab shell while maintaining the model’s light weight. Then, the model is trained using the ArcFace loss function, which effectively extracts the generalized features of the Chinese mitten crab carapace images. Finally, authenticity is verified by calculating the similarity between two input images of Chinese mitten crab carapaces. Experimental results show that the model, combined with the coordinate attention mechanism and ArcFace, achieves a high accuracy rate of 98.56% on the Chinese mitten crab image dataset, surpassing ShuffleFaceNet, MobileFaceNet, and VarGFaceNet by 13.63, 11.1, and 6.55 percentage points, respectively. Moreover, it only requires an average of 1.7 milliseconds per image for verification. While maintaining lightness, this model offers high efficiency and accuracy, offering an effective technical solution for enhancing the traceability of Chinese mitten crab products and combating counterfeit goods.