A Novel Approach for Vessel Graphics Identification and Augmentation Based on Unsupervised Illumination Estimation Network

Vessel identification in low-light environments is a challenging task since low-light images contain less information for detecting objects. To improve the feasibility of vessel identification in low-light environments, we present a new unsupervised low-light image augmentation approach to augment the visibility of vessel features in low-light images, laying a foundation for subsequent identification. This guarantees the feasibility of vessel identification with the augmented image. To this end, we design an illumination estimation network (IEN) to estimate the illumination of a low-light image based on the Retinex theory. Then, we augment the low-light image by estimating its reflectance with the estimated illumination. Compared with the existing deep learning-based supervised low-light image augmentation approach that depends on the low- and normal-light image pairs for model training, IEN is an unsupervised approach without using normal-light image as references during model training. Compared with the traditional unsupervised low-light image augmentation approach, IEN shows faster image augmentation speed by parallel computation acceleration with image Processing Units (GPUs). The proposed approach builds an end-to-end pipeline integrating a vessel-aware weight matrix and SmoothNet, which optimizes illumination estimation under the Retinex framework. To evaluate the effectiveness of the proposed approach, we build a low-light vessel image set based on the Sea Vessels 7000 dataset—a public maritime image set containing 7000 vessel images across multiple categories Then, we carry out an experiment to evaluate the feasibility of vessel identification using the augmented image. Experimental results show that the proposed approach boosts the AP75 metric of the RetinaNet detector by 6.6 percentage points (from 56.8 to 63.4) on the low-light Sea Vessels 7000 dataset, confirming that the augmented image significantly improves vessel identification accuracy in low-light scenarios.