Evaluating deep semantic segmentation networks for object detection in maritime surveillance

Cane, Tom; Ferryman, James

doi:10.1109/avss.2018.8639077

Cited by 49 publications

(28 citation statements)

References 21 publications

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“…Bovcon et al explored semantics segmentation assisted by an inertial measurement unit (IMU) for stereo obstacle detection [27]. Cane et al evaluated the semantic segmentation networks on several public maritime datasets and compared their performances [28]. Kim et al proposed a probabilistic method to detect and classify ships based on a faster R-CNN detector and improved the probability of ship detection by intersection over union (IOU) tracking [29].…”

Section: Visible Vessel Detection and Trackingmentioning

confidence: 99%

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

et al. 2019

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It is crucial for unmanned surface vessels (USVs) to detect and track surrounding vessels in real time to avoid collisions at sea. However, the harsh maritime environment poses great challenges to multitarget tracking (MTT). In this paper, a novel tracking by detection framework that integrates the multimodel and multicue (M3C) pipeline is proposed, which aims at improving the detection and tracking performance. Regarding the multimodel, we predicted the maneuver probability of a target vessel via the gated recurrent unit (GRU) model with an attention mechanism, and fused their respective outputs as the output of a kinematic filter. We developed a hybrid affinity model based on multi cues, such as the motion, appearance, and attitude of the ego vessel in the data association stage. By using the proposed ship re-identification approach, the tracker had the capability of appearance matching via metric learning. Experimental evaluation of two public maritime datasets showed that our method achieved state-of-the-art performance, not only in identity switches (IDS) but also in frame rates.

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Section: Visible Vessel Detection and Trackingmentioning

confidence: 99%

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

et al. 2019

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“…Fully convolutional network performed the best on said images, but further post processing is required to improve the segmentation. Cane and Ferryman (2018) evaluated semantic segmentation networks (SSN) for object detection system in the maritime environment. The authors proposed a simple system which takes RGB images on input.…”

Section: Ann-based Methodsmentioning

confidence: 99%

An Overview on Horizon Detection Methods in Maritime Video Surveillance

2020

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The interest in video surveillance has been increasing in the fields of maritime industry in the past decade. Maritime transportation system is a vital part of the world’s economy and the extent of global ship traffic is increasing. This trend encourages the development of intelligent surveillance systems in the maritime zone. The development of intelligent surveillance systems includes sensor and data fusion, which incorporates multispectral and multisensory data to replace the traditional approach with radars only. Video cameras are widely used since they capture images of greater resolution than most sensor systems. Also, combined with video analytics they provide sensors with high capability, complex pattern recognition analytics, and multiple variables for the decision making process. In this paper, an overview of a small part of the system is presented – horizon detection.

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“…e SMD-based dataset can be used as a benchmark that encourages reproducibility and comparability for object detection in maritime environments. Recent research [12,70,81,110,127,[144][145][146][147][148][149][150][151] reflects this characteristic.…”

Section: Marine Datasets Comparison Moosbauer Et Al [144]mentioning

confidence: 99%

“…In [12], a "reference model" pretrained with Pascal VOC image dataset and a "proposed model" trained with a specific maritime dataset (Singapore Maritime Dataset, SMD), the same structure of the "reference model" compared with the "proposed model," experiments show that, in SMD verification dataset, the proposed model is about twice as accurate as the reference model in terms of IoU and recall rate. Cane et al [81] evaluated semantic segmentation networks in the context of an object detection system for maritime surveillance. e authors indicate that the SegNet and ENet achieve higher detection accuracy and precision.…”

Section: Maritime Surveillancementioning

confidence: 99%

Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

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We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

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Evaluating deep semantic segmentation networks for object detection in maritime surveillance

Cited by 49 publications

References 21 publications

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

An Overview on Horizon Detection Methods in Maritime Video Surveillance

Survey on Deep Learning-Based Marine Object Detection

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