Automatic Classification of Unexploded Ordnance (UXO) Based on Deep Learning Neural Networks (DLNNS)

Abstract

This article discusses the use of a deep learning neural network (DLNN) as a tool to improve maritime safety by classifying the potential threat to shipping posed by unexploded ordnance (UXO) objects. Unexploded ordnance poses a huge threat to maritime users, which is why navies and non-governmental organisations (NGOs) around the world are using dedicated advanced technologies to counter this threat. The measures taken by navies include mine countermeasure units (MCMVs) and mine-hunting technology, which relies on the use of sonar imagery to detect and classify dangerous objects. The modern mine-hunting technique is generally divided into three stages: detection and classification, identification, and neutralisation/disposal. The detection and classification stage is usually carried out using sonar mounted on the hull of a ship or on an underwater vehicle. There is now a strong trend to intensify the use of more advanced technologies, such as synthetic aperture sonar (SAS) for high-resolution data collection. Once the sonar data has been collected, military personnel examine the images of the seabed to detect targets and classify them as mine-like objects (MILCO) or non mine-like objects (NON-MILCO). Computer-aided detection (CAD), computer-aided classification (CAC) and automatic target recognition (ATR) algorithms have been introduced to reduce the burden on the technical operator and reduce post-mission analysis time. This article describes a target classification solution using a DLNN-based approach that can significantly reduce the time required for post-mission data analysis during underwater reconnaissance operations.

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

“…P. Nguyen et al, 2024a;V. G. Nguyen et al, 2023b), and others (Sigiel et al, 2024;Sumari et al, 2022). While artificial neural networks (ANNs) are computationally demanding and need a lot of training data, their potential for enhancing the performance of RES and their integration into existing power grids seems bright.…”