基于样本迭代融合的海洋生物检测  

Marine Creature Detection Based on Sample Iterative Fusion

作　　者：吴立栋 彭宗举[1,2] 李欣 苏涛 陈芬 王晓东 Wu Lidong;Peng Zongju;Li Xin;Su Tao;Chen Fen;Wang Xiaodong(Faculty of Electrical Engineering and Computer Science,Ningbo University,Ningbo 315000,Zhejiang,China;School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 310027,China)

机构地区：[1]宁波大学信息科学与工程学院,浙江宁波315000 [2]重庆理工大学电气与电子工程学院,重庆310027

出　　处：《激光与光电子学进展》2023年第2期306-314,共9页Laser & Optoelectronics Progress

摘　　要：海洋生物相互聚集形成遮挡现象是误检和漏检的重要原因。为了解决这个问题,提出一种采用样本迭代融合辅助网络训练的海洋生物检测方法。首先,选用改进后的深度空洞残差结构作为特征提取网络,提升了网络的特征提取能力;然后,结合海洋生物图像目标遮挡、密集的特点,改进损失函数避免发生误检、漏检现象;最后,为了进一步解决目标遮挡、数据不平衡的问题,提出样本迭代融合方法,生成模拟图像扩充训练集,提高了网络训练的有效性和对小样本量海洋生物的检测能力。实验结果表明,所提海洋生物检测方法在URPC2018和台湾地区鱼类数据集上的准确率分别达91.36%和90.27%,检测准确率和速度高于现有目标检测算法。Occlusion caused by gathering of marine creatures together is an important reason for false and missed detections.Therefore,this study proposes a marine creature detection method based on iterative fusion of sample-assisted network training.First,an improved deep hole residual structure is selected as the feature extraction network,which improves the feature extraction ability of the network.Second,because of the occlusion and dense characteristics of marine creature images,the loss function is improved to avoid false and missed detections.Finally,to solve the problems of target occlusion and data imbalance,a sample iterative fusion method is proposed to generate an extended training set of simulated images.This improves the effectiveness of network training and the ability to detect marine creatures with a small sample size.The experimental results show that the proposed method can achieve a detection accuracy of 91.36%on the URPC2018 dataset and 90.27% on the Taiwan region fish dataset.The detection accuracy and speed of the proposed method are higher than those of existing target detection algorithms.

关 键 词：海洋生物检测 样本迭代融合 深度学习 水下目标检测识别 数字图像处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]