Research on Key Technology of Ship Re-Identification Based on the USV-UAV Collaboration

Vessel re-identification (re-ID) is a crucial task in maritime supervision, enhancing maritime safety and improving the maritime situational awareness system. However, distinct from land-based scenarios involving vehicles or pedestrians, vessels, as enormous rigid bodies situated in the dynamic marine environment, face unique challenges such as significant variations in the scale of discriminative features and unpredictable sway. Furthermore, there is a limited number of publicly available datasets for vessel re-ID in complex backgrounds. In this paper, to overcome these challenges, a novel Hierarchical Perceptual Aggregation Network with Inclination-Aware Attention (HPAN-IAA) is proposed. HPAN-IAA comprises two main modules: the Hierarchical Perceptual Aggregation Block (HPAB) and the Inclination-Aware Attention Block (IAAB). Specifically, in HPAB, a hierarchical perceptual function is introduced to decompose visual information of vessels into discriminative features at multiple levels. These feature maps with different levels of detail from diverse network layers are then fused together by concatenation, resulting in a comprehensive feature representation that effectively integrates information across various scales. Conversely, to address the irregular variations and random omissions in discriminative feature distribution caused by unpredictable vessel sway, in IAAB, the Channel Collaborative Attention Module and the Pyramidal Spatial Attention Module are designed to adaptively extract potential discriminative features within each channel and spatial dimension, enhancing model’s ability in effectively extracting and utilizing irregularly changing discriminative features. Moreover, we propose a novel vessel re-ID dataset—VesselReID-2258. Extensive experiments conducted on VesselReID-2258 and the publicly available dataset VesselReID demonstrate that HPAN-IAA outperforms the current state-of-the-art methods,achieving superior performance with mean Average Precision scores of 0.861 and 0.823.

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Maritime surveillance using unmanned vehicles: deep learning-based vessel re-identification

Geers,

Willems,

Nita

et al. 2024

Artificial Intelligence for Security and Defence Applications II

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Maritime surveillance is crucial for ensuring compliance with regulations and protecting critical maritime infrastructure. Conventional tracking systems, such as AIS or LRIT, are susceptible to manipulation as they can be switched off or altered. To address this vulnerability, there is a growing need for a visual monitoring system facilitated by unmanned systems such as unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs). Equipped with sensors and cameras, these unmanned vehicles collect vast amounts of data that often demand time-consuming manual processing. This study presents a robust method for automatic target vessel re-identification from RGB imagery captured by unmanned vehicles. Our approach uniquely combines visual appearance and textual data recognized from the acquired images to enhance the accuracy of target vessel identification and authentication against a known vessel database. We achieve this through utilizing Convolutional Neural Network (CNN) embeddings and Optical Character Recognition (OCR) data, extracted from the vessel's images. This multi-modal approach surpasses the limitations of methods relying solely on visual or textual information. The proposed prototype was evaluated on two distinct datasets. The first dataset contains small vessels without textual data and serves to test the performance of the fine-tuned CNN model in identifying target vessels, trained with a triplet loss function. The second dataset encompasses medium and large-sized vessels amidst challenging conditions, highlighting the advantage of fusing OCR data with CNN embeddings. The results demonstrate the feasibility of a computer vision model that combines OCR data with CNN embeddings for target vessel identification, resulting in significantly enhanced robustness and classification accuracy. The proposed methodology holds promise for advancing the capabilities of autonomous visual monitoring systems deployed by unmanned vehicles, offering a resilient and effective solution for maritime surveillance.

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Research on Key Technology of Ship Re-Identification Based on the USV-UAV Collaboration

Cited by 3 publications

References 56 publications

FVSI-YOLO: A Full-View Ship Identification and Tracking Method for Drones

FVSI-YOLO: A Full-View Ship Identification and Tracking Method for Drones

Vessel re-identification by a hierarchical perceptual aggregation network with inclination-aware attention

Maritime surveillance using unmanned vehicles: deep learning-based vessel re-identification

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