Horizon detection in maritime images using scene parsing network

Jeong, Chi Yoon; Yang, Hyun Seung; Moon, Kiyoung

doi:10.1049/el.2018.0989

Cited by 31 publications

(17 citation statements)

References 10 publications

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“…This issue could be addressed by combining the network output with an edge or feature detector to infer boundaries between objects. Finally, an interesting 'by-product' of the proposed approach is that the horizon line can be easily inferred from the segmentation map, as has been done recently [10]. Horizon detection is a common pre-cursor task in maritime surveillance, as it can be used to determine camera orientation and inferring the distance (and hence real-world size) of objects.…”

Section: Results and Analysismentioning

confidence: 98%

Evaluating deep semantic segmentation networks for object detection in maritime surveillance

Cane

Ferryman

2018

2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Maritime surveillance is important for applications in safety and security, but the visual detection of objects in maritime scenes remains challenging due to the diverse and unconstrained nature of such environments, and the need to operate in near real-time. Recent work on deep neural networks for semantic segmentation has achieved good performance in the road/urban scene parsing task. Driven by the potential application in autonomous vehicle navigation, many of the architectures are designed to be fast and lightweight. In this paper, we evaluate semantic segmentation networks in the context of an object detection system for maritime surveillance. Using data from the ADE20k scene parsing dataset, we train a selection of recent semantic segmentation network architectures to compare their performance on a number of publicly available maritime surveillance datasets.

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Section: Results and Analysismentioning

confidence: 98%

Evaluating deep semantic segmentation networks for object detection in maritime surveillance

Cane

Ferryman

2018

2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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“…A novel approach for horizon detection was proposed by Jeong et al (2018a). The authors segmented each pixel into semantic categories using a pyramid scene parsing network (PSPnet).…”

Section: Ann-based Methodsmentioning

confidence: 99%

“…Horizon information is used in some object detection approaches and for the reduction of false positives for a given object detection rate (Jeong et al, 2018a). Also, it is used for distance prediction of another object to the camera (Gladstone et al, 2016) or for maritime target detection and tracking in infrared images (Jian and Wen, 2019).…”

Section: Horizon Detection In Maritime Zone Video Surveillancementioning

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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“…They use CNN to verify the edge pixels in the complex maritime scenes. A researcher also attempted to apply a semantic segmentation network for water line detection [28,29].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Semantic Segmentation for Unmanned Surface Vehicle Navigation

et al. 2020

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The intelligentization of unmanned surface vehicles (USVs) has recently attracted intensive interest. Visual perception of the water scenes is critical for the autonomous navigation of USVs. In this paper, an adaptive semantic segmentation method is proposed to recognize the water scenes. A semantic segmentation network model is designed to classify each pixel of an image into water, land or sky. The segmentation result is refined by the conditional random field (CRF) method. It is further improved accordingly by referring to the superpixel map. A weight map is generated based on the prediction confidence. The network trains itself with the refined pseudo label and the weight map. A set of experiments were designed to evaluate the proposed method. The experimental results show that the proposed method exhibits excellent performance with few-shot learning and is quite adaptable to a new environment, very efficient for limited manual labeled data utilization.

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Horizon detection in maritime images using scene parsing network

Cited by 31 publications

References 10 publications

Evaluating deep semantic segmentation networks for object detection in maritime surveillance

Evaluating deep semantic segmentation networks for object detection in maritime surveillance

An Overview on Horizon Detection Methods in Maritime Video Surveillance

Adaptive Semantic Segmentation for Unmanned Surface Vehicle Navigation

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