A small vessel detection using a co-located multi-frequency FMCW MIMO radar

Small vessels detection is a known issue due to its low radar cross section (RCS). An existing shore-based vessel tracking radar is for long-distance commercial vessels detection. Meanwhile, a vessel-mounted radar system known for its reliability has a limitation due to its single radar coverage. The paper presented a co-located frequency modulated continuous waveform (FMCW) maritime radar for small vessel detection utilising a multiple-input multiple-output (MIMO) configuration. The radar behaviour is numerically simulated for detecting a Swerling 1 target which resembles small maritime’s vessels. The simulated MIMO configuration comprised two transmitting and receiving nodes. The proposal is to utilize a multi-frequency FMCW MIMO configuration in a maritime environment by applying the spectrum averaging (SA) to fuse MIMO received signals for range and velocity estimation. The analysis was summarised and displayed in terms of estimation error performance, probability of error and average error. The simulation outcomes an improvement of 2.2 dB for a static target, and 0.1 dB for a moving target, in resulting the 20% probability of range error with the MIMO setup. A moving vessel's effect was observed to degrade the range error estimation performance between 0.6 to 2.7 dB. Meanwhile, the proposed method was proven to improve the 20% probability of velocity error by 1.75 dB. The impact of multi-frequency MIMO was also observed to produce better average error performance.

An Augmented Reality Periscope for Submarines with Extended Visual Classification

Submarines are considered extremely strategic for any naval army due to their stealth capability. Periscopes are crucial sensors for these vessels, and emerging to the surface or periscope depth is required to identify visual contacts through this device. This maneuver has many procedures and usually has to be fast and agile to avoid exposure. This paper presents and implements a novel architecture for real submarine periscopes developed for future Brazilian naval fleet operations. Our system consists of a probe that is connected to the craft and carries a 360 camera. We project and take the images inside the vessel using traditional VR/XR devices. We also propose and implement an efficient computer vision-based MR technique to estimate and display detected vessels effectively and precisely. The vessel detection model is trained using synthetic images. So, we built and made available a dataset composed of 99,000 images. Finally, we also estimate distances of the classified elements, showing all the information in an AR-based interface. Although the probe is wired-connected, it allows for the vessel to stand in deep positions, reducing its exposure and introducing a new way for submarine maneuvers and operations. We validate our proposal through a user experience experiment using 19 experts in periscope operations.

Lightweight deep network-enabled real-time low-visibility enhancement for promoting vessel detection in maritime video surveillance

Maritime video surveillance has become an essential part of the vessel traffic services system, intended to guarantee vessel traffic safety and security in maritime applications. To make maritime surveillance more feasible and practicable, many intelligent vision-empowered technologies have been developed to automatically detect moving vessels from maritime visual sensing data (i.e., maritime surveillance videos). However, when visual data is collected in a low-visibility environment, the essential optical information is often hidden in the dark, potentially resulting in decreased accuracy of vessel detection. To guarantee reliable vessel detection under low-visibility conditions, the paper proposes a low-visibility enhancement network (termed LVENet) based on Retinex theory to enhance imaging quality in maritime video surveillance. LVENet is a lightweight deep neural network incorporating a depthwise separable convolution. The synthetically-degraded image generation and hybrid loss function are further presented to enhance the robustness and generalisation capacities of LVENet. Both full-reference and no-reference evaluation experiments demonstrate that LVENet could yield comparable or even better visual qualities than other state-of-the-art methods. In addition, it takes LVENet just 0⋅0045 s to restore degraded images with size 1920 × 1080 pixels on an NVIDIA 2080Ti GPU, which can adequately meet real-time requirements. Using LVENet, vessel detection performance can be greatly improved with enhanced visibility under low-light imaging conditions.

Vessel Target Detection in Spaceborne–Airborne Collaborative SAR Images via Proposal and Polarization Fusion

This paper focuses on vessel detection through the fusion of synthetic aperture radar (SAR) images acquired from spaceborne–airborne collaborative observations. The vessel target detection task becomes more challenging when it features inshore interferences and structured and shaped targets. We propose a new method, based on target proposal and polarization information exploitation (TPPIE), to fuse the spaceborne–airborne collaborative SAR images for accurate vessel detection. First, a new triple-state proposal matrix (TSPM) is generated by combining the normed gradient-based target proposal and the edge-based morphological candidate map. The TSPM can be used to extract the potential target regions, as well as filtering out the sea clutter and inshore interference regions. Second, we present a new polarization feature, named the absolute polarization ratio (APR), to exploit the intensity information of dual-polarization SAR images. In the APR map, the vessel target regions are further enhanced. Third, the final fused image with enhanced targets and suppressed backgrounds (i.e., improved target-to-clutter ratio; TCR) is attained by taking the Hadamard product of the intersected TSPM from multiple sources and the composite map exploiting the APR feature. Experimental analyses using Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR imagery indicate that the proposed TPPIE fusion method can yield higher TCRs for fused images and better detection performance for vessel targets, compared to commonly used image fusion approaches.