Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 334-339.doi: 10.11896/jsjkx.210100138

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

SCTD 1.0:Sonar Common Target Detection Dataset

ZHOU Yan1, CHEN Shao-chang1, WU Ke1, NING Ming-qiang1, CHEN Hong-kun2, ZHANG Peng1   

  1. 1 School of Electronic Engineering,Naval University of Engineering,Wuhan 430033,China
    2 92118 Troops of PLA,Zhoushan,Zhejiang 316000,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:ZHOU Yan,born in 1991,postgraduate.His main research interests include deep learning and computer vision.
    ZHANG Peng,born in 1996,Ph.D.His main research interests include research on rotation pattern recognition of convo-lutional neural network and automated deep learning.
  • Supported by:
    National Natural Science Foundation of China(61671461).

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Abstract: In recent years,convolutional neural networks (CNN) have been widely used in large-scale natural image datasets (such as ImageNet,COCO).However,there is a lack of applied research in the field of sonar image detection and recognition,which suffers from a lack of sonar image target detection and classification datasets and often faces sparse and unbalanced samples of underwater targets.In response to this problem,based on the extensive collection of sonar images,this paper constructs a completely open sonar common target detection dataset SCTD1.0 that can be used for sonar image detection and classification research.The dataset currently contains three types of typical targets:underwater shipwreck,wreckage of crashed aircraft,and victims,with a total of 596 samples.On the basis of SCTD1.0,this paper uses transfer learning to test the benchmarks of detection and classification.Specifically,for the detection task,the feature pyramid network is used to combine and utilize multi-scale features,and the performance of the three detection frameworks YOLOv3,Faster R-CNN,and Cascade R-CNN on this dataset is compared.For classification tasks,the classification performance of the three networks of VGGNet,ResNet50,and DenseNet is compared,and the classification accuracy rate reaches about 90%.

Key words: Convolutional neural network, Dataset, Detection and classification, Sonar image, Transfer learning

CLC Number: 

  • TP391
[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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