现代电子技术2024，Vol.47Issue(2)：183-186,4.DOI:10.16652/j.issn.1004-373x.2024.02.033
增强细节信息特征提取的鱼类个体识别算法
Fish individual recognition algorithm of feature extraction of enhanced detailed information
摘要
Abstract
In practical application scenarios for fish individual recognition,due to high underwater environmental noise,tilted fish angles,and insignificant intra class feature differences,the feature extraction ability of convolutional neural networks is low,which affects the recognition accuracy.On this basis,The YOLOv5 network is improved and a loss function is established to optimize the detection results of fish individuals.Based on MobileNet-v1,the main network is optimized,deep convolution layers are improved,ReLU activation functions are updated,and Leaky ReLU is used to retain negative feature information for feature acquisition.A feature weighting layer is added before the fully connected layer at the end of the network structure to enhance the fine details in the images and extract features by removing the pooling layer commonly used in convolutional neural networks.The experimental results show that the proposed model achieves an average accuracy of 92.46%and a maximum accuracy of 95.69%on the DLOUFish dataset.关键词
鱼类个体识别/关键点检测/特征提取/MobileNet-v1/YOLOv5网络/特征加权Key words
fish individual recognition/key points detection/feature extraction/MobileNet-v1/YOLOv5 network/feature weighted分类
电子信息工程引用本文复制引用
王伟芳,殷健豪,高春奇,刘梁..增强细节信息特征提取的鱼类个体识别算法[J].现代电子技术,2024,47(2):183-186,4.基金项目
国家自然科学基金项目:水下实时背景下鱼类精准识别新方法研究:融合VSM和DELM(31972846) （31972846）
