Visible-to-Infrared Ship Image Generation via Improved Pix2Pix for Unmanned Surface Vehicles Perception
In the field of unmanned surface vehicles (USV) autonomous control, fusing visible and infrared images enhances target detection robustness and supports navigation and obstacle avoidance tasks. However, infrared image acquisition is constrained by high equipment costs and harsh offshore environments, resulting in scarce samples and high acquisition costs that severely hinder the deployment of USV perception-control systems. To address this issue, this paper proposes a supervised improved Pix2Pix-based visible-to-infrared image generation method to supplement paired datasets. Three optimizations are adopted: the generator integrates cross-layer feature fusion and channel attention modules to enhance vessel structural details and feature focus; the discriminator employs a multi-scale local-global joint structure to ensure detail authenticity and overall consistency; the loss function incorporates perceptual loss under cycle consistency constraints. Experiments on a self-constructed dataset of 4000 paired vessel images show that the generated infrared images have reasonable thermal features and intact structures, with metrics significantly outperforming supervised baseline models. Ablation studies validate the effectiveness of each module, providing reliable data support for USV infrared target detection and improving the environmental adaptability of autonomous control systems.