Weighted Radon transform for line detection in noisy images

Alpatov, Boris A.; Babayan, Pavel V.; Shubin, Nikita

doi:10.1117/1.jei.24.2.023023

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…Compared with the GRT, the GWRT makes full use of the image gradient information as its weights, thus providing better results. For a two-dimensional Euclidean space, the GWRT can be defined for a continuous image as (Alpatov et al, 2015) U weighted (p) = ∫∫ (x,y)∈c(p) 〈∇f(x, y),ñ 〉 2 dxdy (8…”

Section: Doa Estimation Methods Based On Gwrtmentioning

confidence: 99%

Broadband high-resolution direction of arrival estimation using the generalized weighted Radon transform

Lu,

Sun,

Gulliver

et al. 2024

Front. Mar. Sci.

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Traditional direction of arrival (DOA) estimation algorithms typically have poor spatial resolution and robustness. In this paper, we propose a broadband high-resolution DOA estimation method based on the generalized weighted Radon transform (GWRT). The array signal can be converted into the frequency-wavenumber (f-k) domain using the conditional wavenumber spectrum function (CWSF). Then, a linear integral mathematical model for high-resolution DOA estimation is derived by transforming the f-k domain into the azimuth-energy domain using the GWRT. Computer simulation and sea trials were conducted to validate the feasibility and performance of the proposed method. The results obtained indicate that the proposed method yields a lower sidelobe level and can more effectively suppress the output energy in the non-target direction when compared to the conventional beamforming (CBF), steered minimum variance (STMV), and deconvolution (DCV) methods. Further, the proposed method provides improved spatial resolution and robustness in a multi-target environment.

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Section: Doa Estimation Methods Based On Gwrtmentioning

confidence: 99%

Broadband high-resolution direction of arrival estimation using the generalized weighted Radon transform

Lu,

Sun,

Gulliver

et al. 2024

Front. Mar. Sci.

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“…It has a median error of about 2 pixels (less than 0.2%) from the center of the actual horizon and a median angular error of less than 0.4 deg. Compared with traditional methods (ENIW [111], FGSL [112], MuSMF [113], IntGF, IntG, Hough [114], and GWR [115]), MuSCoWERT has excellent performance. Jeong et al [116] proposed a fast method for detecting the horizon line in maritime scenarios by combining a multiscale approach and region-of-interest detection.…”

Section: Horizon Detectionmentioning

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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“…A horizontal line is generally represented as a straight line in a maritime scenario. Typical approaches for horizon detection include projection-based methods, [5][6][7][8][9] intensity analysis (including the vertical direction), 10,11 and statistical analysis of the sea and sky regions. 10,12 In the projection-based methods, an edge detector is first applied to generate an edge map.…”

Section: Related Workmentioning

confidence: 99%

“…Typical approaches for horizon detection in maritime scenes assume that the horizon is a straight line and include projection-based methods, 5–9 intensity analysis (including the vertical direction), 10,11 and statistical analysis of the sea and sky regions. 10,12 These methods have relatively low complexity, but they have a limitation on the accuracy of horizon detection.…”

Section: Introductionmentioning

confidence: 99%

Fast horizon detection in maritime images using region-of-interest

Jeong

Yang

Moon

2018

International Journal of Distributed Sensor Networks

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In this article, we propose a fast method for detecting the horizon line in maritime scenarios by combining a multi-scale approach and region-of-interest detection. Recently, several methods that adopt a multi-scale approach have been proposed, because edge detection at a single is insufficient to detect all edges of various sizes. However, these methods suffer from high processing times, requiring tens of seconds to complete horizon detection. Moreover, the resolution of images captured from cameras mounted on vessels is increasing, which reduces processing speed. Using the region-ofinterest is an efficient way of reducing the amount of processing information required. Thus, we explore a way to efficiently use the region-of-interest for horizon detection. The proposed method first detects the region-of-interest using a property of maritime scenes and then multi-scale edge detection is performed for edge extraction at each scale. The results are then combined to produce a single edge map. Then, Hough transform and a least-square method are sequentially used to estimate the horizon line accurately. We compared the performance of the proposed method with stateof-the-art methods using two publicly available databases, namely, Singapore Marine Dataset and buoy dataset. Experimental results show that the proposed method for region-of-interest detection reduces the processing time of horizon detection, and the accuracy with which the proposed method can identify the horizon is superior to that of state-of-the-art methods.

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Weighted Radon transform for line detection in noisy images

Cited by 12 publications

References 13 publications

Broadband high-resolution direction of arrival estimation using the generalized weighted Radon transform

Broadband high-resolution direction of arrival estimation using the generalized weighted Radon transform

Survey on Deep Learning-Based Marine Object Detection

Fast horizon detection in maritime images using region-of-interest

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