# Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
## 4 Conclusions  
To address the challenge of accurately tracing the geographical origin of Chinese mitten crabs during the breeding and distribution processes, this paper proposes a two-stage traceability framework that integrates an improved YOLOv10n object detection model with a lightweight IGINC classification model optimized from GoogleNet. In the detection stage, a YOLOv10n-based architecture enhanced with ODConv, SlimNeck, and LSCD modules is employed to efficiently locate crab targets under complex backgrounds and multi-scale conditions. In the classification stage, an IGINC model based on GoogleNet is designed, incorporating asymmetric convolution (AC.B) and channel attention (SE) modules to achieve robust origin classification under diverse environmental variations. Experimental results show that the proposed detector achieves an mAP50 of 99.5% and mAP50-95 of 88.5% while running at 309 FPS, with a 35.3% reduction in GFLOPs. The IGINC classifier achieves 93.7% accuracy using only 17.4% of VGG16's parameters and 40% of AlexNet's.  
Figure 14: Comparison of the loss curves changing with iterations of the three models.  
Unlike traditional origin traceability methods based on morphological traits or elemental fingerprinting, this approach leverages deep learning to achieve more efficient and automated identification, providing strong technical support for crab quality control and market supervision. While the proposed two-stage framework achieves high accuracy and efficiency, it still has some limitations. The current model is trained on a dataset captured under controlled conditions and may require adaptation when applied to fully underwater or highly dynamic environments. Additionally, the system is currently designed for post-harvest identification. In future work, we plan to extend the framework to support real-time underwater crab recognition and expand the dataset to cover more farming areas and seasonal variations.  
All authors contributed to the study conception and design. [PERSON] and [PERSON] participated in the overall framework construction and the first draft writing of the paper. [PERSON], [PERSON] and [PERSON] participated in sorting out the experimental data and revised and improved the first draft. [PERSON] and [PERSON] reviewed and finalized the paper to ensure its quality. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China under Grant 2022 YFF0608201, in part by the Central Public-interest Scientific Institution Basal Research Fund of the Chinese Academy of Fishery Sciences, CAFS under Grant 2023 TD65, in part by the Central Public-interest Scientific Institution Basal Research Fund of Fisheries Research Center, CAFS under Grant 2021 JBFM11, and in part by the Procurement Project for Fisheries Ecology and Resources Monitoring Services in 2024.  
Not applicable.  
The data presented in this study is available on request from the corresponding author. The data is not publicly available due to privacy restrictions.  
The authors thank the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflicts of interest related to this work.  
The following abbreviations are used in this manuscript:  
ODConv omni-dimensional dynamic convolution  
LSC Lightweight Shared Convolutional Detection  
IGINC Improved GoogleNet's Inception Net for Crab  
AC.B Asymmetric Convolution Blocks  
SE Squeeze and Excitation