# Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
## 1 Introduction
Traditional classification methods can only distinguish some low-dimensional features, while neural networks can extract and recognize high-dimensional features. It has shown good performance and broad application prospects in the task of aquatic product classification and recognition [12; 13; 14; 15; 16; 17]. [PERSON] et al. [18] adopted the detection and identification method of fish in the electronic monitoring data based on the improved Yolov8 commercial fishing vessels; the backbone network used the GCBlock structure to model remote dependencies in order to increase the feature extraction ability. The new convolution method of GSConv is used on the Neck side to reduce the amount of model computation. The results showed that the model can quickly and accurately complete the detection and identification of fish in the electronic monitoring data of commercial fishing vessels at a lower cost. [PERSON] et al. [19] proposed an improved BIAS-YOLO network based on the YOLOv5 network, which was used to automatically detect different kinds of fish. Based on the original YOLOv5 network, the Spatial Pyramid Pooling (SPP) module is improved, and the idea of spatial bias is introduced, so that the information of the whole image can be fully utilized. Experiments show that the BIAS-YOLO algorithm is improved by 3.1% on the [EMAIL_ADDRESS] compared with the YOLOv5 network. Based on the study of the characteristics of fish acoustic scattering, [PERSON] et al. [20] extracted the singular value features of wavelet packet coefficients, temporal centroid features, and discrete cosine transform coefficient features of the fish acoustic scattering signals from the perspective of collaborative fusion of multi-directional acoustic scattering data, and made decisions on the extracted features through SVM, and finally obtained the final classification results of fish through the collaborative fusion method. [PERSON] et al. [21] proposed a method for detecting fish in static photos using the YOLOv4 architecture and attention system with multi-scale feature combinations. This method is a deep learning-based object detection method specifically designed for underwater environments. Some researchers also combine machine learning and computer vision techniques with methods such as using multi-class support vector machines for classification, using probabilistic background models for real-time fish detection in images with complex backgrounds, and modeling biological features for fish classification. For example, [PERSON] et al. [22] model utilized an improved version of the AlexNet model, which features object-based soft attention and transfer learning. Convolutional neural networks are an effective method for target detection and classification of aquatic products. It can automatically extract image features, process target objects of different sizes and shapes, and train through backpropagation algorithms to gradually improve the accuracy of the model. However, there are two problems with the detection and recognition methods of commercial hairy crabs based on convolutional neural networks: First, the detection and recognition accuracy were low in real-world scenarios, which hindered the fine-grained species classification of hairy crabs. Second, models with high detection and recognition accuracy have higher costs and consume more computing resources. There is relatively little research on how to efficiently complete commercial hairy crab detection and recognition at lower costs in real-world scenarios. To address the aforementioned limitations in the hairy crab classification task, this paper proposes a two-stage identification methodology that synergistically combines an enhanced YOLOv10n object detector with an improved IGINC classification model reconstructed from GoogleNet. The main contributions of this article are summarized as follows:  
1. A large image dataset labeled with the geographical origin of Chinese mitten crab has been established.