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

Abstract 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.