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[1] Matej Kristan,et al. Obstacle Tracking for Unmanned Surface Vessels Using 3-D Point Cloud , 2020, IEEE Journal of Oceanic Engineering.
[2] Zhenyu He,et al. The Seventh Visual Object Tracking VOT2019 Challenge Results , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[3] Ricardo Ribeiro,et al. A Data Set for Airborne Maritime Surveillance Environments , 2019, IEEE Transactions on Circuits and Systems for Video Technology.
[4] Paul Newman,et al. 1 year, 1000 km: The Oxford RobotCar dataset , 2017, Int. J. Robotics Res..
[5] Wei Zhang,et al. A Review of Research on Light Visual Perception of Unmanned Surface Vehicles , 2020 .
[6] Matej Kristan,et al. Obstacle Detection for USVs by Joint Stereo-View Semantic Segmentation , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[7] Ian D. Reid,et al. RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Hong-Yuan Mark Liao,et al. YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.
[9] Fernando Seco Granja,et al. A Short-Range Ship Navigation System Based on Ladar Imaging and Target Tracking for Improved Safety and Efficiency , 2009, IEEE Transactions on Intelligent Transportation Systems.
[10] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[11] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Matej Kristan,et al. A water-obstacle separation and refinement network for unmanned surface vehicles , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[13] Hao Chen,et al. FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[14] Alessandro Farinelli,et al. Waterline and obstacle detection in images from low-cost autonomous boats for environmental monitoring , 2020, Robotics Auton. Syst..
[15] Donghwa Lee,et al. Vision-Based Real-Time Obstacle Segmentation Algorithm for Autonomous Surface Vehicle , 2019, IEEE Access.
[16] Xiaochun Cao,et al. Omni-Directional Surveillance for Unmanned Water Vehicles , 2008 .
[17] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[18] Thierry Bouwmans,et al. Double-constrained RPCA based on saliency maps for foreground detection in automated maritime surveillance , 2015, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
[19] James M. Ferryman,et al. Evaluating deep semantic segmentation networks for object detection in maritime surveillance , 2018, 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
[20] James M. Ferryman,et al. PETS 2017: Dataset and Challenge , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[21] Erhan Gundogdu,et al. MARVEL: A Large-Scale Image Dataset for Maritime Vessels , 2016, ACCV.
[22] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[23] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Yi-Tung Chan. Comprehensive comparative evaluation of background subtraction algorithms in open sea environments , 2021, Comput. Vis. Image Underst..
[25] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[26] Gaurav S. Sukhatme,et al. Obstacle detection and avoidance for an Autonomous Surface Vehicle using a profiling sonar , 2011, 2011 IEEE International Conference on Robotics and Automation.
[27] Luca Iocchi,et al. ARGOS-Venice Boat Classification , 2015, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
[28] Han Wang,et al. Stereovision based obstacle detection system for unmanned surface vehicle , 2013, 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).
[29] Eugenio Culurciello,et al. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation , 2016, ArXiv.
[30] Yongmei Ren,et al. Surface Vehicle Detection and Tracking with Deep Learning and Appearance Feature , 2019, 2019 5th International Conference on Control, Automation and Robotics (ICCAR).
[31] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Abhishek Dutta,et al. The VGG Image Annotator (VIA) , 2019, ArXiv.
[33] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Geoff A. W. West,et al. Visual Maritime Attention Using Multiple Low-Level Features and Naïve Bayes Classification , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.
[35] Matej Kristan,et al. Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles , 2015, IEEE Transactions on Cybernetics.
[36] Lawrence O. Hall,et al. Horizon Detection Using Machine Learning Techniques , 2006, 2006 5th International Conference on Machine Learning and Applications (ICMLA'06).
[37] Deepu Rajan,et al. Object Detection in a Maritime Environment: Performance Evaluation of Background Subtraction Methods , 2019, IEEE Transactions on Intelligent Transportation Systems.
[38] Paul Newman,et al. The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[39] Chiemela Onunka,et al. Autonomous marine craft navigation: On the study of radar obstacle detection , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.
[40] James M. Ferryman,et al. Saliency-Based Detection for Maritime Object Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[41] Ricardo Ribeiro,et al. Unmanned aircraft systems in maritime operations: Challenges addressed in the scope of the SEAGULL project , 2015, OCEANS 2015 - Genova.
[42] Michael Teutsch,et al. A Benchmark for Deep Learning Based Object Detection in Maritime Environments , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[43] Parul Parashar,et al. Neural Networks in Machine Learning , 2014 .
[44] Gang Yu,et al. BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation , 2018, ECCV.
[45] Matej Kristan,et al. The MaSTr1325 dataset for training deep USV obstacle detection models , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[46] Johan Lilius,et al. Comparing CNN-Based Object Detectors on Two Novel Maritime Datasets , 2020, 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).
[47] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] Abhishek Dutta,et al. The VIA Annotation Software for Images, Audio and Video , 2019, ACM Multimedia.
[50] Andreas Geiger,et al. Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[51] Matej Kristan,et al. Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation , 2018, Robotics Auton. Syst..
[52] Ross B. Girshick,et al. LVIS: A Dataset for Large Vocabulary Instance Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Deepu Rajan,et al. Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey , 2016, IEEE Transactions on Intelligent Transportation Systems.
[54] Luc Van Gool,et al. Deep Extreme Cut: From Extreme Points to Object Segmentation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[55] Daniel Cremers,et al. MOT20: A benchmark for multi object tracking in crowded scenes , 2020, ArXiv.
[56] Wei Xie,et al. Convolutional neural network based obstacle detection for unmanned surface vehicle. , 2019, Mathematical biosciences and engineering : MBE.
[57] Xiaogang Wang,et al. Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[59] Nikolaos D. Doulamis,et al. Vision-based maritime surveillance system using fused visual attention maps and online adaptable tracker , 2013, 2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS).
[60] Hyewon Lee,et al. Image-Based Ship Detection and Classification for Unmanned Surface Vehicle Using Real-Time Object Detection Neural Networks , 2018 .
[61] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[62] Alexandre M. Amory,et al. A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions , 2019, Sensors.
[63] Yoshua Bengio,et al. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).