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. 2022 Jun 28;8(7):182.
doi: 10.3390/jimaging8070182.

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

Krishna Patel  1 , Chintan Bhatt  2 , Pier Luigi Mazzeo  3
Affiliations

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

Krishna Patel et al. J Imaging. .

Abstract

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.

Keywords: convolutional neural networks; deep learning; image classification; remote sensing; satellite images; ships detection; surveillance.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The number of papers on Ship Detection irrespective of region and country, (from 2016–2022 (March month)).
Figure 2
Figure 2
Image extracted from the dataset of ships.
Figure 3
Figure 3
Dataset Image without ship.
Figure 4
Figure 4
Overall design of YOLO algorithm.
Figure 5
Figure 5
Architecture of YOLOv5 algorithm.
Figure 6
Figure 6
Result of Ship Detection using YOLO family algorithms: (a) YOLOv3 (b) YOLOv4 (c) YOLOv5.
Figure 6
Figure 6
Result of Ship Detection using YOLO family algorithms: (a) YOLOv3 (b) YOLOv4 (c) YOLOv5.
Figure 7
Figure 7
Performance of YOLOv3, YOLOv4, and YOLOv5 on PC.
See this image and copyright information in PMC

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