基于改进AlexNet的鱼类识别算法

Fish Recognition Algorithm Based on Improved AlexNet

自然环境中的鱼类形状种类繁多且易受到不同光线和背景环境影响,导致一些传统的基于颜色纹理或特征点提取的鱼类识别算法识别精度降低,达不到良好的分类效果。针对这一问题,文中在已有的AlexNet卷积神经网络的基础上,减少了部分冗余卷积层以加快模型训练速度。将基于项的柔性注意力算法应用于改进后的AlexNet卷积神经网络模型,该模型由4个卷积层、1个基于项的柔性注意力层和两个全连接层组成。同时,利用迁移学习的方法建立鱼类识别模型。测试结果表明,相较于原始AlexNet,提出的算法平均识别准确率为97.43%,准确率提升了4.08%,对部分鱼类的识别准确率达到了99.31%,识别耗时降低了35%。与现有鱼类识别算法相比,文中提出的鱼类识别算法的识别精准度更高,模型复杂度更低,鲁棒性更强。

Due to the various species of fish,and the influence of different light and background environments,the recognition accuracy of some traditional fish recognition algorithms bases on color texture or feature point extraction is reduced,and good classification results cannot be achieved.To solve the problem,based on the existing AlexNet convolutional neural network,this paper proposes one method to speed up model training by reducing redundant convolutional layers.An item-based soft attention algorithm is applied to the improved AlexNet convolutional neural network model,which consists of four convolutional layers,one item-based soft attention layer,and two fully-connected layers.Meanwhile,a fish recognition model is established using transfer learning methods.The test results show that the average recognition accuracy of the proposed algorithm achieves 97.43%,which is 4.08%higher than the original AlexNet model,and the average recognition rate of some fishes achieves 99.31%,and time consumption of fish recognition is reduced by 35%.In conclusion,compared with state-of-the-art fish recognition algorithms,the fish recognition algorithm proposed in this study achieve higher accuracy,lower model complexity,and stronger robustness.