Ship Detection Using Edge-Based Segmentation and Histogram of Oriented Gradient with Ship Size Ratio

Eum, Hyukmin; Bae, Jaeyun; Yoon, Changwoo; Kim, Euntai

doi:10.5391/ijfis.2015.15.4.251

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…Such an approach would significantly reduce false-detection rates. Eum et al [78] used the Sobel edge-detection method and morphological operations to segment foreground objects to separate the maritime foreground from the background. Genitha et al [79] offered an improved watershed segmentation algorithm using label control to avoid oversegmentation of the algorithm.…”

Section: Foreground Segmentationmentioning

confidence: 99%

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Lyu

Shao

Cheng

et al. 2023

IEEE Intell. Transport. Syst. Mag.

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Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The article also proposes the direction of future research for sea-surface object detection based on EO sensors.

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Section: Foreground Segmentationmentioning

confidence: 99%

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Lyu

Shao

Cheng

et al. 2023

IEEE Intell. Transport. Syst. Mag.

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show abstract

“…The integration of spatial-temporal algorithms and systems for detecting marine objects from UAV footage, as discussed in [6] and [8], demonstrates significant progress in identifying floating objects without presupposing specific maritime conditions. Further elaborating on detection capabilities, [7] and [9] illustrate how leveraging low-level features, edgebased segmentation, and Histogram of Oriented Gradient HOG methods surpass traditional object detection approaches. Moving towards practical applications, [10] unveils a computer vision technique for autonomously identifying marine vehicles through buoy camera systems.…”

Section: Introductionmentioning

confidence: 99%

Enhanced YOLOv8 Ship Detection Empower UnmannedSurface Vehicles for Advanced Maritime Surveillance

HAIJOUB,

HATIM,

HAMMIA

et al. 2024

Preprint

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The evolution of maritime surveillance is sig-nificantly marked by the incorporation of Artificial In-telligence and machine learning into Unmanned SurfaceVehicles (USVs). This paper presents a an AI methodfor detecting and tracking unmanned surface vehicles,specifically leveraging an enhanced version of YOLOv8,fine-tuned for maritime surveillance needs. Deployedon the NVIDIA Jetson TX2 platform, the system fea-tures an innovative architecture and perception mod-ule optimized for real-time operations and energy effi-ciency. Demonstrating superior detection accuracy witha mean Average Precision (mAP) of 0.99 and achievingan operational speed of 17.99 FPS, all while maintain-ing energy consumption at just 5.61 joules. The remark-able balance between accuracy, processing speed, and energy efficiency underscores the potential of this sys-tem to significantly advance maritime safety, security,and environmental monitoring.

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“…In [13], the human subjects are segmented without considering the ROI extraction. Edge-based segmentation using histogram of oriented gradient (HOG) with the ship size ratio is proposed in [14].…”

mentioning

confidence: 99%

Multi-Level Segmentation of Infrared Images with Region of Interest Extraction

Yeom

2016

IJFIS

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Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

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Ship Detection Using Edge-Based Segmentation and Histogram of Oriented Gradient with Ship Size Ratio

Cited by 10 publications

References 17 publications

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Enhanced YOLOv8 Ship Detection Empower UnmannedSurface Vehicles for Advanced Maritime Surveillance

Multi-Level Segmentation of Infrared Images with Region of Interest Extraction

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