Abstract
Shellfish hold significant economic value and are one of the major economic crops in coastal areas of China. To identify a rapid and effective method for distinguishing different Shellfish species in Jiangsu based on morphological differences, this paper proposes a recognition algorithm for common economic Shellfish in Jiangsu based on the Fused-ResNet network. Firstly, the basic structure of the ResNet network is introduced. Then, to address the issues of similarity in shape, color, and texture among Shellfish, a Channel Spatial Attention (CSA) module is incorporated. Additionally, to more effectively differentiate the diversity of Shellfish, a transfer learning training strategy is adopted. Finally, the optimized model is applied to Shellfish recognition tasks. The proposed recognition method demonstrates universality under different targets and achieves a recognition accuracy of 98.8%. Moreover, the algorithm’s low computational cost makes it more suitable for deployment on mobile devices, providing effective technical support for the rapid identification of economic Shellfish in Jiangsu.
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Acknowledgments
This work is supported in part by Nantong Science and Technology Program JC2023076 and in part by Postgraduate Research and Practice Innovation Program of Jiangsu Province KYCX24_3643 and in part by The “JBGS” Project of Seed Industry Revitalization in Jiangsu Province JBGS (2021) 142.
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Liu, Z., Zhang, B., Wu, Y., Zhang, Z., Chen, J., Li, H. (2024). Research on the Identification of Common Economic Shellfish in Jiangsu Based on Fused-ResNet Network. In: Suen, C.Y., Krzyzak, A., Ravanelli, M., Trentin, E., Subakan, C., Nobile, N. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2024. Lecture Notes in Computer Science(), vol 15154. Springer, Cham. https://doi.org/10.1007/978-3-031-71602-7_25
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