| 摘 要: 针对传统的淡水鱼类图像识别方法速度慢、需要人工提取特征等问题，提出一种基于改进Res2Net模型的淡水鱼类图像识别算法。提出的改进方案如下：首先使用CELU激活函数代替ReLu激活函数；接着将残差块与混合注意力网络相结合；最后使用三个3×3卷积核替代Res2Net模型中第一个卷积层的7×7卷积核，同时在下采样的残差连接中加入平均池化层。实验结果表明，改进的网络在淡水鱼类图像分类上达到了96.34%的准确率，比Res2Net的准确率高3.67%，具有更加优异的性能，可为淡水鱼类识别提供参考。 |
| 关键词: 淡水鱼；图像识别；注意力机制 |
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中图分类号: TP391.4
文献标识码: A
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| 基金项目: 广西民族大学高等教育改革项目(2020XJGY41)；国家级大学生创新创业训练计划项目(202010608005)；广西科技基地和人才专项(2021AC23009). |
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| Research on Classification of Freshwater Fish Images based on Improved Res2Net Model |
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ZHAO Zhengwei, ZHU Hongjin, YANG Genteng, WANG Jinkun
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(College of Electronic Information, Guangxi Minzu University, Nanning 530006, China)
zzwei@gxun.edu.cn; 1437489585@qq.com; ygt6686@126.com; 1823323212@qq.com
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| Abstract: Aiming at the slow speed of traditional freshwater fish image recognition methods and the need to manually extract features, this paper proposes an improved freshwater fish image recognition algorithm based on improved Res2Net model. The proposed improvement scheme is as follows: first, CELU activation function is used instead of ReLu. Then, the residual block is combined with the hybrid attention network. Finally, three 3×3 convolution kernels are used to replace the 7×7 convolution kernels of the first convolutional layer in Res2Net model. At the same time, an average pooling layer is added to the down-sampled residual connection. Experimental results show that the improved network achieves 96.34% accuracy in freshwater fish image classification, which is 3.67% higher than that of Res2Net. It has more excellent performance and provides a reference for freshwater fish recognition. |
| Keywords: freshwater fish; image recognition; attention mechanism |
