摘要
针对现有多目标鱼体检测大多针对受控环境进行,泛化能力有限的问题,提出了一种简单、有效的复杂背景下多目标鱼体检测模型。通过迁移学习构建基于DRN的特征提取方法,对原始图像进行特征提取,结合RPN进一步生成候选检测框;构建基于Faster R-CNN的复杂背景多目标鱼体检测模型。在ImageNet2012数据集上的实验结果表明:该模型对复杂背景下金鱼的检测平均精度达到89.5%,远高于R-CNN+AlexNet模型、Faster R-CNN+VGG16模型和Faster R-CNN+ResNet101的检测精度,表明该模型可以高效精确地实现复杂背景下的多目标鱼体检测。
Target detection is the key link of fish tracking,behavior recognition and abnormal behavior detection of fish body.Therefore,fish detection has important practical significance.Due to the low imaging quality of underwater surveillance video,the complicated underwater environment,and the high visual diversity of fish bodies,multi-target fish detection in complex background is still a very challenging problem.In order to solve the problem that the existing multi-target fish detection is mostly carried out in a controlled environment and the generalization ability is limited,a simple and effective multi-target fish detection model in complex background was proposed.The feature extraction method based on DRN was constructed by transfer learning.The features were extracted from the original image,and the candidate detection frame was further generated by combining RPN.A multi-target fish detection model in complex background was constructed based on Faster R-CNN.The experimental results on the ImageNet2012 data set showed that the detection accuracy of this model for goldfish in complex background reached 89.5%,which was much higher than the detection accuracy of the R-CNN+AlexNet model,Faster R-CNN+VGG16 model and Faster R-CNN+ResNet101 model in this data set,indicating that this model can effectively and accurately realize the detection of multi-target fish in complex background.
作者
孙龙清
孙希蓓
吴雨寒
罗冰
SUN Longqing;SUN Xibei;WU Yuhan;LUO Bing(National Innovation Center for Digital Fishery,China Agricultural University,Beijing 100083,China;College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2021年第S01期245-251,315,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2020YFD0900201)
关键词
鱼体
目标检测
特征
卷积神经网络
深度残差网络
fish
object detection
feature
convolutional neural networks
deep residual network
