摘要
为提高鲨鱼种群细粒度分类的准确率,解决数据集图像干扰因素多、图像局部关键特征提取不足、通道语义关联欠缺等问题,提出了一种基于改进双线性卷积神经网络B-CNN模型的DM-BCNN鲨鱼种群细粒度分类模型。首先,引入可变形卷积将原模型中的特征提取部分替换为DRAM_ResNet网络结构,提升模型对复杂非规则形状和局部结构的检测能力;然后在此基础上采用NAM注意力机制,加强模型对关键特征的识别提取能力;最后引入互通道损失函数,增强鲨鱼图像不同通道间的语义关联性,使得模型可以更全面地捕捉图像不同方面的信息。结果显示:改进模型DM-BCNN在Top-1准确率达到了96.12%,相较于原模型提升了2.51个百分点。研究表明,改进模型相比原模型在细粒度图像分类上的表现更加出色,对鲨鱼种群的细粒度分类识别更加有效。
To enhance the accuracy of fine-grained classification of shark populations and address issues such as image interference,insufficient extraction of local key features,and lack of semantic correlation between channels,a DM-BCNN model for fine-grained classification of shark populations based on an improved Bilinear Convolutional Neural Network(B-CNN)is proposed.First,deformable convolution is introduced to replace the feature extraction part of the original model with a DRAM_ResNet network structure,enhancing the model′s ability to detect complex and irregular shapes and local structures.Then,the NAM attention mechanism is employed to strengthen the model′s ability to identify and extract key features.Finally,a Mutual Channel Loss function is introduced to enhance the semantic correlation between different channels of shark images,allowing the model to capture information from various aspects of the images more comprehensively.The results show that the improved DM-BCNN model achieved a Top-1 accuracy of 96.12%,representing a 2.51 percentage point improvement over the original model.The study demonstrates that the proposed improved model outperforms the original model in fine-grained image classification,making it more effective for fine-grained classification and identification of shark populations.
作者
蒋飞
李皞
李雅琴
肖松宴
刘天玮
JIANG Fei;LI Hao;LI Yaqin;XIAO Songyan;LIU Tianwei(School of Mathematics and Computer Science,Wuhan Polytechnic University,Wuhan 430048,Hubei,China)
出处
《渔业现代化》
CSCD
北大核心
2024年第5期90-101,共12页
Fishery Modernization
基金
湖北省重点研发计划(2023BBB046)。
关键词
鲨鱼
细粒度图像
注意力机制
可变形卷积
互通道损失
shark
fine-grained image
attention mechanism
deformable convolution
mutual channel loss
