MARITIME TARGET DETECTION FOR UNMANNED SURFACE VEHICLES BASED ON LIGHTWEIGHT NETWORKS UNDER FOGGY WEATHER, 31-45.

Shuyue Li, Junjie Wang, Jinlu Sheng, Ziyu Liu, Shixin Li, and Ying Cui

References

  1. [1] A. Ali, O.G. Olaleye, and M. Bayoumi, Fast region-based dpmobject detection for autonomous vehicles, Proc. 2016 IEEE59th International Midwest Symp. on Circuits and Systems(MWSCAS), Abu Dhabi, 2016, 1–4.
  2. [2] B. Bahmani, B. Moseley, A. Vattani, R. Kumar, and S.Vassilvitskii, Scalable k-means++, 2012, arXiv:1203.6402.
  3. [3] A. Bochkovskiy, C.-Y. Wang, and H.-Y. Mark Liao, Yolov4:Optimal speed and accuracy of object detection, 2020,arXiv:2004.10934.
  4. [4] L. Bo, X. Xiaoyang, W. Xingxing, and T. Wenting, Shipdetection and classification from optical remote sensingimages: A survey, Chinese J. Aeronaut., 34(3), 2021,145–163.
  5. [5] Y.-T. Chan, Y.-H. Chu, C.-C. Lee, C.-H. Chen, T.-W. Hou,and C.-H. Huang, Implementation of deep-learning-based edgecomputing for maritime vehicle classification, Proc. of the 8thIIAE International Conf. on Industrial Application Engineering2020, Matsue, 2020, 247–252.
  6. [6] F. Chollet, Xception: Deep learning with depthwise separableconvolutions, Proc. of the IEEE Conf. on Computer Visionand Pattern Recognition, Honolulu, HI, 2017, 1251–1258.
  7. [7] S. Jiang, H. Li, X. Zou, C. Tang, D. Hang, J. Yang, and Die Liu,Lightweight mesh crack detection algorithm based on efficientattention mechanism, International Journal of Robotics andAutomation, 8(31), 2023.
  8. [8] D. Engin, A. Gen¸c, and H.K. Ekenel, Cycle-dehaze: EnhancedcycleGAN for single image dehazing, Proc. of the IEEE Conf.on Computer Vision and Pattern Recognition Workshops, SaltLake City, UT, 2018, 825–833.
  9. [9] P. Ganesh, Y. Chen, Y. Yang, D. Chen, and M. Winslett,YOLO-ReT: Towards high accuracy real-time object detectionon edge GPUs, Proc. of the IEEE/CVF Winter Conf.on Applications of Computer Vision, Waikoloa, HI, 2022,3267–3277.
  10. [10] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, S. Ozair, A. Courville, and Y. Bengio,Generative adversarial networks, Communications of the ACM,63(11), 2020, 139–144.
  11. [11] Y. Guo, Y. Lu, Y. Guo, R.W. Liu, and K.T. Chui, Intelligentvision-enabled detection of water-surface targets for videosurveillance in maritime transportation, Journal of AdvancedTransportation, 2021, 2021.
  12. [12] A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen,M. Tan, G. Chu, V. Vasudevan, Y. Zhu, R. Pang, H.Adam, and Q. Le, Searching for mobilenetv3, Proc. of theIEEE/CVF International Conf. on Computer Vision, Seoul,2019, 1314–1324.
  13. [13] Z. Huang, B. Sui, J. Wen, and G. Jiang, An intelligent shipimage/video detection and classification method with improvedregressive deep convolutional neural network, Complexity, 2020,2020.
  14. [14] J. Hu, D. Zhao, Y. Zhang, C. Zhou, and W. Chen, Real-timenondestructive fish behavior detecting in mixed polyculturesystem using deep-learning and low-cost devices, ExpertSystems with Applications, 178, 2021, 115051.
  15. [15] X. Hu, Y. Liu, Z. Zhao, J. Liu, X. Yang, C. Sun, S. Chen, B.Li, and C. Zhou, Real-time detection of uneaten feed pellets inunderwater images for aquaculture using an improved YOLO-V4 network, Computers and Electronics in Agriculture, 185,2021, 106135.
  16. [16] S. Ioffe and C. Szegedy, Batch normalization: Accelerating deepnetwork training by reducing internal covariate shift, Proc.International Conf. on Machine Learning, (PMLR), Lille, 2015,448–456.
  17. [17] M. Ju, C. Ding, W. Ren, and Y. Yang, IDBP: Image dehazingusing blended priors including non-local, local, and globalpriors, IEEE Transactions on Circuits and Systems for VideoTechnology, 32(7), 2022, 4867–4871.
  18. [18] M. Ju, C. Ding, D. Zhang, and Y.J. Guo, Gamma-correction-based visibility restoration for single hazy images, IEEE SignalProcessing Letters, 25(7), 2018, 1084–1088.
  19. [19] R.W. Liu, W. Yuan, X. Chen, and Y. Lu, An enhancedcnn-enabled learning method for promoting ship detection inmaritime surveillance system, Ocean Engineering, 235, 2021,109435.
  20. [20] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, Path aggregationnetwork for instance segmentation, Proc. Proc. of the IEEEConf. on Computer Vision and Pattern Recognition, Salt LakeCity, UT, 2018, 8759–8768.
  21. [21] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y.Fu, and A.C. Berg, Ssd: Single shot multibox detector, Proc.European Conf. on Computer Vision, Amsterdam, 2016, 21–37.
  22. [22] W. Liu, X. Hou, J. Duan, and G. Qiu, End-to-end singleimage fog removal using enhanced cycle consistent adversarialnetworks, IEEE Transactions on Image Processing, 29, 2020,7819–7833.
  23. [23] Z. Li, X. Liu, Y. Zhao, B. Liu, Z. Huang, and R. Hong, Alightweight multi-scale aggregated model for detecting aerialimages captured by UAVs, Journal of Visual Communicationand Image Representation, 77, 2021, 103058.
  24. [24] Z. Li, W. Zhou, C. Liukui, and S. Jin, Bio-inspiredapproach for image vehicle detection under low illumination,International Journal of Robotics and Automation, 2(87), 2020,332–338.
  25. [25] N. Ma, X. Zhang, H.-T. Zheng, and J. Sun, ShuffleNet v2:Practical guidelines for efficient cnn architecture design, Proc.of the European Conf. on Computer Vision (ECCV), Munich,2018, 116–131.
  26. [26] R.-Q. Ma, X.-R. Shen, and S.-J. Zhang, Single image defoggingalgorithm based on conditional generative adversarial network,Mathematical Problems in Engineering, 2020, 1–8, 2020.
  27. [27] Z. Ouyang, J. Cui, X. Dong, Y. Li, and J. Niu, SaccadeFork:A lightweight multi-sensor fusion-based target detector,Information Fusion, 77, 2022, 172–183.
  28. [28] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You onlylook once: Unified, real-time object detection, Proc. of theIEEE Conf. on Computer Vision and Pattern Recognition, LasVegas, NV, 2016, 779–788.
  29. [29] J. Redmon and A. Farhadi, YOLO9000: Better, faster, stronger,Proc. of the IEEE Conf. on Computer Vision and PatternRecognition, Honolulu, HI, 2017, 7263–7271.13
  30. [30] J. Redmon and A. Farhadi, YOLOV3: An incrementalimprovement, 2018, arXiv:1804.02767.
  31. [31] S. Ren, K. He, R. Girshick, and J. Sun, Faster R-CNN: Towardsreal-time object detection with region proposal networks,Advances in Neural Information Processing Systems, 28, 2015.
  32. [32] O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutionalnetworks for biomedical image segmentation, Proc. Interna-tional Conf. on Medical Image Computing and Computer-Assisted Intervention, Munich, 2015, 234–241.
  33. [33] C. Sakaridis, D. Dai, and L.Van Gool, Semantic foggy sceneunderstanding with synthetic data, International Journal ofComputer Vision, 126(9), 2018, 973–992.
  34. [34] C. Sun, B. Kong, L. He, and Q. Tian, An algorithm of imagingsimulation of fog with different visibility, Proc. 2015 IEEEInternational Conf. on Information and Automation, Lijiang,2015, 1607–1611.
  35. [35] C. Szegedy, S. Ioffe, V. Vanhoucke, and A.A. Alemi, Inception-v4, inception-ResNet and the impact of residual connectionson learning, Proc. Thirty-First AAAI Conf. on ArtificialIntelligence, San Francisco, CA, 2017, 4278–4284.
  36. [36] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov,D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeperwith convolutions, Proc. of the IEEE Conf. on ComputerVision and Pattern Recognition, Boston, MA, 2015, 1–9.
  37. [37] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna,Rethinking the inception architecture for computer vision,Proc. of the IEEE Conf. on Computer Vision and PatternRecognition, Las Vegas, NV, 2016, 2818–2826.
  38. [38] M. Tan and Q. Le, Efficientnet: Rethinking model scaling forconvolutional neural networks, Proc. International Conf. onMachine Learning, (PMLR), Long Beach, 2019, 6105–6114.
  39. [39] C.-Y. Wang, A. Bochkovskiy, and H.-Y. Mark Liao, Scaled-YOLOv4: Scaling cross stage partial network, Proc. of theIEEE/CVF Conference on Computer Vision and PatternRecognition, Nashville, TN, 2021, 13029–13038.
  40. [40] C.-Y. Wang, H.-Y. Mark Liao, Y.-H. Wu, P.-Y. Chen, J.-W.Hsieh, and I.-H. Yeh, CSPNet: A new backbone that canenhance learning capability of CNN, Proc. of the IEEE/CVFConference on Computer Vision and Pattern RecognitionWorkshops, Seattle, WA, 2020, 1571–1580.
  41. [41] Y. Wang, S. Sun, and J. Zhong, An ensemble anomaly detectionwith imbalanced data based on robot vision, InternationalJournal of Robotics and Automation, 31(2), 2016.
  42. [42] Z. Xiang, T. Tao, L. Song, Z. Dong, Y. Mao, S. Chu, and H.Wang, Object tracking algorithm for unmanned surface vehiclebased on improved mean-shift method, International Journalof Advanced Robotic Systems, 17(3), 2020, 1729881420925294.
  43. [43] S. Chen, Y. Feng, T. Tang, and Y. Wu, Automated defectdetection based on transfer learning and deep convolutiongenerative adversarial networks, International Journal ofRobotics and Automation, 7(35), 2021, 471–478.
  44. [44] Q. Zhang, C. Zhao, X. Zhang, F. Yuan, C. Li, and D. Hao, Thegenerative adversarial network based on attention mechanismfor image defogging, Proc. International Forum on Digital TVand Wireless Multimedia Communications, Shanghai, 2020,12–25.
  45. [45] W. Zhang, X.-Z. Gao, C.-F. Yang, F. Jiang, and Z.-Y. Chen, Aobject detection and tracking method for security in intelligenceof unmanned surface vehicles, Journal of Ambient Intelligenceand Humanized Computing, 13, 2022, 1279–1291.
  46. [46] X. Zhang, X. Zhou, M. Lin, and J. Sun, ShuffleNet: Anextremely efficient convolutional neural network for mobiledevices, Proc. of the IEEE Conf. on Computer Vision andPattern Recognition, Salt Lake City, UT, 2018, 6848–6856.
  47. [47] Y. Zhang, D. Ren, B. Chen, and J. Gu, Detection of pine wiltdisease in autumn based on remote sensing images and enfmodule, International Journal of Robotics and Automation,37(6), 2023.
  48. [48] Z. Zhou and X. Yang, Pine wilt disease detection in UAV-captured images, International Journal of Robotics andAutomation, 37(1), 2022.
  49. [49] A. Zhu and Y. Chen, A machine-learning-based algorithm fordetecting a moving object, International Journal of Roboticsand Automation, 31(5), 2016, 402–408.
  50. [50] J.-Y. Zhu, T. Park, P. Isola, and A.A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks,Proc. of the IEEE International Conf. On Computer Vision,Venice, 2017, 2223–2232.

Important Links:

Go Back