计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 334-339.doi: 10.11896/jsjkx.210100138
周彦1, 陈少昌1, 吴可1, 宁明强1, 陈宏昆2, 张鹏1
ZHOU Yan1, CHEN Shao-chang1, WU Ke1, NING Ming-qiang1, CHEN Hong-kun2, ZHANG Peng1
摘要: 近年来,卷积神经网络(CNN)在大规模自然图像数据集(如ImageNet,COCO)中获得了广泛应用,但在声呐图像检测识别领域的应用研究较缺乏,其存在声呐图像目标检测和分类数据集缺乏且水下目标样本往往面临样本稀少、不平衡等问题。针对这一问题,在进行广泛收集声呐图像的基础上,构建了一个完全公开的、可以用于开展声呐图像检测和分类研究的声呐常见目标检测数据集SCTD1.0,该数据集目前已包含水下沉船、失事飞机残骸、遇难者3类典型目标,共计596个样本。在SCTD1.0的基础上,文中采用迁移学习的方式测试了检测和分类的基准,具体来说:针对检测任务,使用特征金字塔网络对多尺度特征进行组合利用,比较了YOLOv3,Faster R-CNN,Cascade R-CNN这3种检测框架在本数据集上的性能表现;针对分类任务,对比了VGGNet,ResNet50,DenseNet 3种网络的分类性能,分类准确率达到了90%左右。
中图分类号:
[1]LUO X B,XU D M,HU J J,et al.Application Research of 3D Imaging Sonar System in Salvage Process[J].Applied Mechanics and Materials,2014,643(8):279-282. [2]DONG L Y,SHAN R,LIU H M,et al.Survey RecognitionMethod of Side Scan Sonar Image Based on Fractal Texture Features[J/OL].Marine Geology and Quaternary Geology:1-8.[2021-02-10].https://doi.org/10.16562/j.cnki.0256-1492.2020070301. [3]EILER J H,GROTHUES T M,DOBARRO J A,et al.Comparing autonomous underwater vehicle(AUV) and vessel-based tracking performance for locating acoustically tagged fish[J].Marine Fisheries Review,2013,75(4):27-42. [4]SHEN J Y,LI L Y,DAI Y L,et al.Fish detection and monitoring system based on YOLO algorithm[J].Journal of Suzhou University of Science and Technology (Natural Science),2020,37(3):68-73. [5]SHEN W,ZHU Z H,ZHANG J,et al.Fish target recognition and counting based on dual-frequency recognition sonar[J].Fishery Modernization,2020,47(6):83-89. [6]DOBECK G J,HYLAND J C,SMEDLEY L,et al.Automateddetection and classification of sea mines in sonar imagery[J].1997,3079:90-110. [7]HU H B,MEI X H.A brief description of mine target detection technology based on sonar image[J].Digital Ocean and Underwater Warfare,2020,3(4):303-308. [8]KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNetClassification with Deep Convolutional Neural Networks[J].Advances in Neural Information Processing Systems,2012,25(2):84-90. [9]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149. [10]REDMON J,FARHADI A.YOLOv3:An Incremental Improvement[J].Computer Vision and Pattern Recognition,2018,4(2):1-6. [11]LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single ShotMultiBox Detector[C]//European Conference Computer Vision.2016. [12]KAIMING H,GEORGIA G,PIOTR D,et al.Mask R-CNN[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence.2017. [13]WILLIAMS D P.Underwater target classification in syntheticaperture sonar imagery using deep convolutional neural networks[C]//2016 23rd International Conference on Pattern Recognition (ICPR).IEEE,2016. [14]KIM J,YU S C.Convolutional neural network-based real-timeROV detection using forward-looking sonar image[C]//Autonomous Underwater Vehicles.IEEE,2016:396-400. [15]VALDENEGRO-TOROM.Object recognition in forward-loo-king sonar images with Convolutional Neural Networks[C]//Oceans.IEEE,2016. [16]KIM J,CHO H,PYO J,et al.The convolution neural network based agent vehicle detection using forward-looking sonar image[C]//OCEANS 2016 MTS/IEEE Monterey.2016. [17]VALDENEGRO-TORO M.Objectness Scoring and DetectionProposals in Forward-Looking Sonar Images with Convolutional Neural Networks[C]//IAPR Workshop on Artificial Neural Networks in Pattern Recognition. Springer International Publishing,2016,21(3):209-219. [18]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [19]DENG J,DONG W,SOCHER R,et al.ImageNet:A large-scale hierarchical image database[C]//IEEE Conference on Computer Vision & Pattern Recognition.IEEE,2009. [20]EVERINGHAM M,GOOL L V,WILLIAMS C K I,et al.The Pascal Visual Object Classes (VOC) Challenge[J].International Journal of Computer Vision,2010,88(2):303-338. [21]LIN T Y,MAIRE M,BELONGIES,et al.Microsoft COCO:Common Objects in Context[J].Springer International Publishing,2014,86(93):740-755. [22]https://www.sdms.afrl.af.mil/datasets/mstar/. [23]LI J,QU C,SHAO J.Ship detection in SAR images based on an improved faster R-CNN[C]//Sar in Big Data Era:Models,Methods & Applications.IEEE,2017. [24]HUANG L,LIU B,LI B,et al.OpenSARShip:A Dataset Dedicated to Sentinel-1 Ship Interpretation[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2018,11(1):195-208. [25]SUN X,WANG Z R,SUN Y R,et al.AIR-SARShip-1.0:High-resolution SAR ship detection dataset[J].Journal of Radars,2019,8(6):852-862. [26]JIANG L,CAI T,MAQ,et al.Active Object Detection in Sonar Images[J].IEEE Access,2020,99(4):1-14. [27]LIN T Y,DOLLARP,GIRSHICK R,et al.Feature PyramidNetworks for Object Detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE Computer Society,2017. [28]CAI Z,VASCONCELOS N.Cascade R-CNN:Delving into High Quality Object Detection[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2018. [29]CHEN K,WANG J,PANG J,et al.MMDetection:OpenMMLab Detection Toolbox and Benchmark[J].Computer Vision and Pattern Recognition,2019,6(7):1-11. [30]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2016. [31]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[J].Computer Scien-ce,2014,23(7):1-14. [32]HUANG G,LIU Z,VAN DER MAATEN L,et al.DenselyConnected Convolutional Networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2017. [33]IOFFE S,SZEGEDY C.Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift[J].Computer Science,2015,12(27):1-11. |
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