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Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing

文献类型： 外文期刊

作者： Ma, Xiaofei 1 ; Shen, Nannan 1 ; He, Yanhui 1 ; Fang, Zhuo 2 ; Zhang, Hongyan 1 ; Wang, Yun 1 ; Duan, Jinrong 1 ;

作者机构： 1.Chinese Acad Fishery Sci, Freshwater Fisheries Res Ctr, Wuxi 214081, Peoples R China

2.Anhui Jianzhu Univ, Sch Elect & Informat Engn, Hefei 230601, Peoples R China

3.Nanjing Agr Univ, Wuxi Fisheries Coll, Wuxi 214081, Peoples R China

关键词： Chinese mitten crab; attention mechanism; YOLOv10; target detection; image recognition; geographic origin traceability

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2025 年 15 卷 13 期

页码：

收录情况： SCI

摘要： During the cultivation and circulation of Chinese mitten crab (Eriocheir sinensis), the difficulty in tracing geographic origin leads to quality uncertainty and market disorder. To address this challenge, this study proposes a two-stage origin traceability framework that integrates a lightweight object detector and a high-precision classifier. In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. In the second stage, an Improved GoogleNet's Inception Net for Crab is developed based on the Inception module, with further integration of Asymmetric Convolution Blocks and Squeeze and Excitation modules to improve the feature extraction and classification ability for regional origin. A comprehensive crab dataset is constructed, containing images from diverse farming sites, including variations in species, color, size, angle, and background conditions. Experimental results show that the proposed detector achieves an mAP50 of 99.5% and an mAP50-95 of 88.5%, while maintaining 309 FPS and reducing GFLOPs by 35.3%. Meanwhile, the classification model achieves high accuracy with only 17.4% and 40% of the parameters of VGG16 and AlexNet, respectively. In conclusion, the proposed method achieves an optimal accuracy-speed-complexity trade-off, enabling robust real-time traceability for aquaculture systems.

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