Abstract
Underwater mines pose extreme danger for ships and submarines. Therefore, navies around the world use mine countermeasure (MCM) units to protect against them. One of the measures used by MCM units is mine hunting, which requires searching for all the mines in a suspicious area. It is generally divided into four stages: detection, classification, identification and disposal. The detection and classification steps are usually performed using a sonar mounted on a ship’s hull or on an underwater vehicle. After retrieving the sonar data, military personnel scan the seabed images to detect targets and classify them as mine-like objects (MLOs) or benign objects. To reduce the technical operator’s workload and decrease post-mission analysis time, computer-aided detection (CAD), computer-aided classification (CAC) and automated target recognition (ATR) algorithms have been introduced. This paper reviews mine detection and classification techniques used in the aforementioned systems. The author considered current and previous generation methods starting with classical image processing, and then machine learning followed by deep learning. This review can facilitate future research to introduce improved mine detection and classification algorithms.
Highlights
Underwater mines are a strategic military tool to protect any country’s naval borders.They constitute fully autonomous devices composed of an explosive charge, sensing device and fuse mechanism
This results in significant variability in targets, clutter and background signatures
The templates are used for detecting highlight and shadow combinations
Summary
Underwater mines are a strategic military tool to protect any country’s naval borders. After retrieving the sonar data, military personnel scan the seabed images to detect targets and classify them as MLOs or benign objects. The detection and classification methods presented in the literature can be divided into classical image processing, machine learning (ML) and deep learning (DP) techniques [3]. By fusing two or more algorithms, the probability of mine detection and proper classification increases Another fusion technique is to combine classical image processing with deep learning [3]. A classical method distinguishes ROIs in the detection step while deep learning classifies ROIs as MLOs or benign objects in this technique This combination can improve the performance of the classification step since the neural network analyses only the ROIs’. This is especially important because of the actual joining of classical image processing with deep learning algorithms
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