How Sea Mines Threaten Global Trade, and How Navies Detect Them
Many modern mines are cylindrical or torpedo-shaped, allowing them to be deployed from aircraft or submarines and descend in a controlled way before settling on the seabed. More advanced designs include so-called rising mines, which sit on the seabed and launch upward toward a target once it is detected.
Mine Countermeasures
A key advantage of naval mines is not just the damage they can cause, but also the time and resources required to find and clear them. This is because it’s challenging to do so over large areas quickly and reliably.
Even the possibility of mines can disrupt shipping and force extensive and costly clearance operations. This has been demonstrated in practice: During the 1980s, Iran and Iraq deployed relatively small numbers of mines against each other in the so-called Tanker War in the Persian Gulf and Red Sea. This caused significant disruption to shipping and forced costly, time-consuming clearance operations, even when direct damage was limited.
Some countermeasures use uncrewed systems to trigger mines by mimicking the magnetic or acoustic signatures of ships, or to disable them with explosive charges. However, more targeted approaches require identifying individual mines, which motivates the need for reliable detection.
Mine Hunting
Mine detection is best understood as a wide-area sonar search, which produces many contacts – essentially, anything unusual in the sonar data. Automatic target recognition algorithms then triage these contacts and classify them as either minelike objects or benign. Divers or camera systems then provide higher-confidence identification or confirmation to validate the result. This is known as a detect-classify-identify pipeline.
To collect data, an uncrewed surface vehicle – deployed from a larger ship – can tow a sonar platform at a fixed height above the seabed. The platform, called a towfish, resembles a small missile and carries multiple sensors, including port and starboard side-scan sonar. The British Royal Navy is also preparing to send this type of towed sonar array to the Persian Gulf region, according to a report.
These sonar devices use sound rather than light to form images. Unlike a photograph, a sonar image is built from one-dimensional measurements of returned sound energy as a function of distance from the sensor. As the platform moves, these slices are assembled to form a continuous image of the seabed. The center of the image corresponds to the water column directly beneath the sonar device and appears dark. The seabed appears as if illuminated from the sensor, with objects characterized by a bright highlight facing the sonar and a shadow extending away from it.
At the detection stage, researchers have developed a range of techniques to detect minelike objects in sonar imagery. Early methods segmented sonar imagery into regions that show as highlights paired with acoustic shadows. Other statistical approaches model seabeds and identify anomalies that deviate from it. Template-like matched filters are used to identify objects with known geometric characteristics.
More advanced approaches incorporate machine learning, using carefully selected features derived from texture, intensity and shadow geometry to classify objects.
More recently, researchers have applied deep learning methods directly to sonar imagery and have often shown improved performance, particularly in complex environments. But their effectiveness depends on the availability of representative training data.
Unlike the data for training many other computer vision systems, high-resolution side-scanning sonar data is particularly expensive to collect and label in large enough amounts to successfully train deep learning mine detection systems.
Perhaps, when it becomes safe to do so, navies can clear mines from the Strait of Hormuz and add to the limited supply of this data.
John Femiani is Professor of Computer Science and Software Engineering, Miami University. This article is published courtesy of The Conversation.
