改进的Faster R-CNN海洋鱼类检测模型

doi:10.3969/j.issn.2095-2198.2022.06.016

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摘要

为提高海洋鱼类检测的准确率，提出一种基于Faster R-CNN的海洋鱼类检测方法.首先，利用迁移学习方法训练Faster R-CNN网络，克服海洋鱼类样本集有限的限制；其次，增加颈部连接层，使用双向特征金字塔网络(BiFPN)进行特征融合，得到具有丰富位置信息和语义信息的融合特征图；再次，将卷积层输出的特征矩阵作外积相乘运算，提高对相似海洋鱼类的识别精度；最后，结合Mask R-CNN中的ROI Align方法对预测位置进行二次修正，使检测框更加准确.实验结果表明，在检测海洋鱼类数据集时，与原始的Faster R-CNN算法相比，改进后的 Faster R-CNN 检测模型的平均准确度均值提高了7.4%.

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Faster R-CNN 海洋鱼类检测特征融合 ROI Align

Abstract:

In order to improve the accuracy of marine fish detection，a Faster R-CNN based detection method for marine fish is proposed.Firstly，the Faster R-CNN network is trained by using the migration learning method to overcome the limited sample set of marine fishes.Secondly，the neck connection layer is added and the bi-directional feature pyramid network(BiFPN) is used to fuse the features.The fusion feature map with rich position information and semantic information is obtained.Then the feature matrix output from the convolution layer is multiplied by the outer product to improve the recognition accuracy of similar marine fishes.Finally，the ROI Align method in Mask R-CNN is combined.In order to make the detection frame more accurate，align method modifies the prediction position twice.The experimental results show that compared with the original Faster R-CNN algorithm，the average accuracy of the improved Faster R-CNN detection model is improved by 7.4% in the marine fish data set.

Key words: Faster R-CNN')" href="#">

Faster R-CNN marine fish detection feature fusion ROI Align

出版日期: 2022-12-31 发布日期: 2024-06-06 整期出版日期: 2022-12-31

ZTFLH:

TP391.4

引用本文:

张翔宇, 朱立军.

改进的Faster R-CNN海洋鱼类检测模型 [J]. 沈阳化工大学学报, 2022, 36(6): 562-568.
ZHANG Xiang-yu, ZHU Li-jun.

Improved Faster R-CNN Marine Fish Detection Model . Journal of Shenyang University of Chemical Technology, 2022, 36(6): 562-568.

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https://xuebao.syuct.edu.cn/CN/10.3969/j.issn.2095-2198.2022.06.016 或 https://xuebao.syuct.edu.cn/CN/Y2022/V36/I6/562

［1］ALSMADI M K，OMAR K，NOAH S A，et al.Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network［J］.Journal of Computer Science，2010，6(10):1088-1094.

［2］HU J，LI D L，DUAN Q L，et al.Fish Species Classification by Color，Texture and Multi-Class Support Vector Machine Using Computer Vision［J］.Computers and Electronics in Agriculture，2012，88:133-140.

［3］姚润璐，桂詠雯，黄秋桂.基于机器视觉的淡水鱼品种识别［J］.微型机与应用，2017，36(24):37-39.

［4］吴一全，殷骏，戴一冕，等.基于蜂群优化多核支持向量机的淡水鱼种类识别［J］.农业工程学报，2014，30(16):312-319.

［5］顾郑平，朱敏.基于深度学习的鱼类分类算法研究［J］.计算机应用与软件，2018，35(1):200-205.

［6］GIRSHICK R，DONAHUE J，DARRELL T，et al.Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation［C］//2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus：IEEE，2014:580-587.

［7］GIESHICK R.Fast R-CNN［C］//2015 IEEE International Conference on Computer Vision（ICCV）.Santiago：IEEE，2015:1440-1448.

［8］REN S Q，HE K M，GIRSHICK R，et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks［C］//Proceedings of the 28th International Conference on Neural Information Processing Systems.Montreal：MIT Press，2015:91-99.

［9］LIU W，ANGUELOV D，ERHAN D，et al.SSD:Single Shot MultiBox Detector［C］//Computer Vision–ECCV 2016.Cham：Springer，2016:21-37.

［10］REDMON J，DIVVALA S，GIRSHICK R，et al.You Only Look Once:Unified，Real-Time Object Detection［C］//2016 IEEE Conference on Computer Vision and Pattern Recognition（CVPR）.Las Vegas：IEEE，2016:779-788.

［11］REDMON J，FARHADI A.YOLO9000:Better，Faster，Stronger［C］//2017 IEEE Conference on Computer Vision and Pattern Recognition（CVPR）.Honolulu：IEEE，2017:6517-6525.

［12］REDMON J，FARHADI A.YOLOv3:An Incremental Improvement［EB/OL］.(2018-04-8)［2020-06-24］.http://arxiv.org/abs/1804.02767.pdf.

［13］袁红春，张硕.基于Faster R-CNN和图像增强的水下鱼类目标检测方法［J］.大连海洋大学学报，2020，35(4):612-619.

［14］LI X，TANG Y H，GAO T W.Deep but Lightweight Neural Networks for Fish Detection［C］//OCEANS 2017-Aberdeen:IEEE，2017：1-5.

［15］LIN T Y，DOLLAR P，GIESHICK R，et al.Feature Pyramid Networks for Object Detection［C］//2017 IEEE Conference on Computer Vision and Pattern Recognition（CVPR）.Honolulu：IEEE，2017:936-944.

［16］TAN M X，PANG R M，LE Q V，et al.EfficientDet:Scalable and Efficient Object Detection［EB/OL］.(2020-07-27)［2020-06-24］.https://arxiv.org/pdf/1911.09070.pdf.

［17］LI H C，XIONG P F，AN J，et al.Pyramid Attention Network for Semantic Segmentation［EB/OL］.(2018-11-25)［2020-06-24］.https://arxiv.org/abs/1805.10180.pdf.

［18］CHOLLET F.Xception:Deep Learning with Depthwise Separable Convolutions［C］//2017 IEEE Conference on Computer Vision and Pattern Recognition（CVPR）.Honolulu：IEEE，2017:1800-1807.

［19］LIN T Y，ROYCHOWDHURY A，MAJI S.Bilinear CNN Models for Fine-Grained Visual Recognition［C］//2015 IEEE International Conference on Computer Vision（ICCV）.Santiago：IEEE，2015:1449-1457.

［20］HE K M，GKIOXARI G，DOLLAR P，et al.Mask R-CNN［C］//2017 IEEE International Conference on Computer Vision（ICCV）.Venice：IEEE，2017:2980-2988.

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