Research on infrared ship detection method in sea-sky background

Tang, Da; Sun, Gang; Wang, Ding-he; Niu, Zhaodong; Chen, Zengping

doi:10.1117/12.2033039

Cited by 23 publications

(23 citation statements)

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“…In the hybrid approach of [60], for each candidate generated by a projection-based method, the regions above and below the candidate line were considered as hypothetical sky and sea regions, and their statistical distributions were computed. The candidate that gives maximum value of the Mahalanobis [14], [26], [38] Hough transform Mathematically well-defined ⊗ Work for prominent well-defined linear horizon only ⊗ Horizon may not be the most prominent Region based Median, correlation, Work for blurred horizon as well ⊗ Requires statistical apriori knowledge [17], [29], [31], [41] covariance Suitable for IR images ⊗ Based on statistics Hybrid…”

Section: ) Region Based Horizon Detectionmentioning

confidence: 99%

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Prasad

Rajan

Rachmawati³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

329

168

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We present a survey on maritime object detection and tracking approaches, which are essential for the development of a navigational system for autonomous ships. The electro-optical (EO) sensor considered here is a video camera that operates in the visible or the infrared spectra, which conventionally complement radar and sonar and have demonstrated effectiveness for situational awareness at sea has demonstrated its effectiveness over the last few years. This paper provides a comprehensive overview of various approaches of video processing for object detection and tracking in the maritime environment. We follow an approachbased taxonomy wherein the advantages and limitations of each approach are compared. The object detection system consists of the following modules: horizon detection, static background subtraction and foreground segmentation. Each of these has been studied extensively in maritime situations and has been shown to be challenging due to the presence of background motion especially due to waves and wakes. The main processes involved in object tracking include video frame registration, dynamic background subtraction, and the object tracking algorithm itself. The challenges for robust tracking arise due to camera motion, dynamic background and low contrast of tracked object, possibly due to environmental degradation. The survey also discusses multisensor approaches and commercial maritime systems that use EO sensors. The survey also highlights methods from computer vision research which hold promise to perform well in maritime EO data processing. Performance of several maritime and computer vision techniques is evaluated on newly proposed Singapore Marine Dataset.• the difficulty in modeling the dynamics of water (including waves, wakes and foams) for background subtraction and detection of foreground objects, • variations in object appearances due to distance and angle of viewing, and • changes in illumination and weather conditions, such as due to clouds, sunshine, rain, glint, etc.

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Section: ) Region Based Horizon Detectionmentioning

confidence: 99%

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Prasad

Rajan

Rachmawati³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

329

168

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“…The horizon is also used to make image registration for subsequent target tracking [10; 14]. There exist several mainstream algorithms for horizon detection and evaluation such as: color-based statistical model [10; 13; 15], edge phase encoding [12], pixels classification [16], linear feature [10; 12-14], color intensity and gradient [12; 14; 16], textural feature [17], region-growing [19] and other physical characteristics near the horizon [14; 17; 19]. In general, the hybrid algorithm performs a higher accuracy rate but it also brings computational burden.…”

Section: оVerview Of Methods For Detecting Horizonmentioning

confidence: 99%

Method of the horizon detection from electro-optical sensors under maritime environment

Євсеєв¹,

Shmatko²,

Дун³

et al. 2018

СОІ

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A new method is presented to detect the sea sky line. In the first step, the original image is transformed into grayscale image and the sea-skyline region is located by using the textural features gray-level co-occurrence matrix based on texture feature. In the second step, the adaptive segmentation thresholds are obtained by the OTSU algorithm, which produces a set of sea sky line candidate points. Finally, a simple clustering method is adopted to select appropriate points and transform them by straight line fitting. The results of experiments established on realworld sea-sky images demonstrate the effectiveness and robustness of the proposed approach.

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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%

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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Research on infrared ship detection method in sea-sky background

Cited by 23 publications

References 0 publications

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Method of the horizon detection from electro-optical sensors under maritime environment

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

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