Maritime Ship Detection using Convolutional Neural Networks from Satellite Images

Alghazo, Jaafar; Bashar, Abul; Latif, Ghazanfar; Zikria, Mohammed

doi:10.1109/csnt51715.2021.9509628

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…The experimental data show that the system enhanced the detection and segmentation of mean average precision very efficiently. Alghazo et al [36] have proposed a study for the development of an efficient and effective ship detection procedure with the assistance of a CNN-grounded deep learning paradigm from the images obtained from the satellite. In the proposed study, two procedures with different frameworks are implemented and tested on the data of the Airbus satellite.…”

Section: State Of the Artmentioning

confidence: 99%

“…[21] Ship size, sea condition, accuracy, cost [37] Gradient explosion, robustness, speed, detection accuracy [22] Ship detection, efficiency, robustness, sea-land segmentation [38] Deep learning features, ship target, detection performance [26] Detection accuracy, false alarm rate, performance, position [39] Verification accuracy, testing accuracy, ship classification, false alarm [27] Detection rate, speed, detection accuracy, ship's target [40] Ship detection, ship size, performance, robustness [28] Real-time observation, rescue, detection accuracy, faster [41] Scene classification, ship detection, accuracy, efficiency [29] Missed detections, accuracy, densely arranged ships, scale sensitivity [42] Mean average precision, accuracy, dataset, performance [30] Multi-scene detection, false alarm, performance [43] Small targets, computational efficiency, detection performance, ship management [31] Training speed, accuracy, performance, ship detection [44] Extraction and classification of candidate regions, robustness, adaptability [32] Speed, accuracy, performance, ship detection, cost [45] Ship detection, image recognition, automatic, time [33] Lost ships, open-source, fast, cost [46] Small ships, computational efficiency, pixels, precision, classification [34] Accuracy, ship detection, mean average precision, unique [64] Processing speed, accuracy, object detection, unique [35] Detection, segmentation, accuracy, pixel level [65] Object detectors, land-ocean segmentation, performance [36] Automatic, accuracy, speed, loss function…”

Section: Features Citations Featuresmentioning

confidence: 99%

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Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

Yasir,

Niang,

Hossain

et al. 2023

JMSE

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We aimed to improve the performance of ship detection methods in synthetic aperture radar (SAR) images by utilizing machine learning (ML) and artificial intelligence (AI) techniques. The maritime industry faces challenges in collecting precise data due to constantly changing sea conditions and weather, which can affect various maritime operations, such as maritime security, rescue missions, and real-time monitoring of water boundaries. To overcome these challenges, we present a survey of AI- and ML-based techniques for ship detection in SAR images that provide a more effective and reliable way to detect and classify ships in a variety of weather conditions, both onshore and offshore. We identified key features frequently used in the existing literature and applied the graph theory matrix approach (GTMA) to rank the available methods. This study’s findings can help users select a quick and efficient ship detection and classification method, improving the accuracy and efficiency of maritime operations. Moreover, the results of this study will contribute to advancing AI- and ML-based techniques for ship detection in SAR images, providing a valuable resource for the maritime industry.

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Section: State Of the Artmentioning

confidence: 99%

Section: Features Citations Featuresmentioning

confidence: 99%

Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

Yasir,

Niang,

Hossain

et al. 2023

JMSE

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“…For the identification of ships from the Airbus satellite image dataset, Jaafar Alghazo [ 36 ] developed two CNN-based deep learning models, namely CNN model 1 and CNN model 2. The proposed methods could be utilised to address a variety of maritime-related issues, such as illegal fishing, maritime resource surveillance, and so on.…”

Section: State-of-the-art (Sota) On Asdmentioning

confidence: 99%

Deep Learning-Based Automatic Detection of Ships: An Experimental Study Using Satellite Images

2022

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The remote sensing surveillance of maritime areas represents an essential task for both security and environmental reasons. Recently, learning strategies belonging to the field of machine learning (ML) have become a niche of interest for the community of remote sensing. Specifically, a major challenge is the automatic classification of ships from satellite imagery, which is needed for traffic surveillance systems, the protection of illegal fisheries, control systems of oil discharge, and the monitoring of sea pollution. Deep learning (DL) is a branch of ML that has emerged in the last few years as a result of advancements in digital technology and data availability. DL has shown capacity and efficacy in tackling difficult learning tasks that were previously intractable. Specifically, DL methods, such as convolutional neural networks (CNNs), have been reported to be efficient in image detection and recognition applications. In this paper, we focused on the development of an automatic ship detection (ASD) approach by using DL methods for assessing the Airbus ship dataset (composed of about 40 K satellite images). The paper explores and analyzes the distinct variations of the YOLO algorithm for the detection of ships from satellite images. A comparison of different versions of YOLO algorithms for ship detection, such as YOLOv3, YOLOv4, and YOLOv5, is presented, after training them on a personal computer with a large dataset of satellite images of the Airbus Ship Challenge and Shipsnet. The differences between the algorithms could be observed on the personal computer. We have confirmed that these algorithms can be used for effective ship detection from satellite images. The conclusion drawn from the conducted research is that the YOLOv5 object detection algorithm outperforms the other versions of the YOLO algorithm, i.e., YOLOv4 and YOLOv3 in terms accuracy of 99% for YOLOv5 compared to 98% and 97% respectively for YOLOv4 and YOLOv3.

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“…In addition, vehicle detection on highways using vision sensors is also prone to driver privacy issues. Other researchers have proposed using satellite remote sensing images to identify vehicles and roads to control traffic congestion on roads and identify traffic density quickly and accurately [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. However, this method is computationally intensive and is not conducive to real-time processing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Tracking Algorithm Combined with High-Precision Map

Cai

Zhu

et al. 2022

Sensors

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On high-speed roads, there are certain blind areas within the radar coverage, especially when the blind zone is in curved road sections; because the radar does not have the measurement point information in multiple frames, it is easy to have a large deviation between the real trajectory and the filtered trajectory. In this paper, we propose a track prediction method combined with a high-precision map to solve the problem of scattered tracks when the targets are in the blind area. First, the lane centerline is fitted to the upstream and downstream lane edges obtained from the high-precision map in certain steps, and the off-north angle at each fitted point is obtained. Secondly, the normal trajectory is predicted according to the conventional method; for the extrapolated trajectory, the northerly angle of the lane centerline at the current position of the trajectory is obtained, the current coordinate system is converted from the north-east-up (ENU) coordinate system to the vehicle coordinate system, and the lateral velocity of the target is set to zero in the vehicle coordinate system to reduce the error caused by the lateral velocity drag of the target. Finally, the normal trajectory is updated and corrected, and the normal and extrapolated trajectories are managed and reported. The experimental results show that the accuracy and convergence effect of the proposed methods are much better than the traditional methods.

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Maritime Ship Detection using Convolutional Neural Networks from Satellite Images

Cited by 10 publications

References 14 publications

Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

Deep Learning-Based Automatic Detection of Ships: An Experimental Study Using Satellite Images

Multi-Target Tracking Algorithm Combined with High-Precision Map

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