Abstract
Detection of infrared objects is essential for applications ranging from remote sensing to thermal imaging. In certain instances, such as when the infrared target is situated at a considerable distance from the detector, the detected object exhibits diminutive dimensions with a concurrently low signal intensity, leading to a challenge in achieving precision in object detection. In this paper, we propose a dual-layer omnidirectional target enhancement (DODTE) module to address the issue of low contrast, which causes the target to be submerged in the background and lose its position information. The module aims to extract and enhance the position information of the target, serving as a feature map for subsequent guidance. Furthermore, to tackle the problem of the fuzzy and ambiguous shape of the target caused by a weak signal and tiny dimension, a residual-based pyramid-like (RBPL) module is designed, which extracts the deep information (i.e. the shape information) from the images to compensate for the lack of expressive ability of the fixed convolution kernel for shape information. These two main modules are employed as the core to form a feature fusion network to realize the detection of infrared dim tiny-sized targets. The comparison of the proposed network with other algorithms are performed on open-source dataset and experimentally generated infrared images. The quantitative evaluation metrics, including IOUs and F1 scores, validate the outperformance of the proposed network. Furthermore, ablation experiments demonstrate that the proposed two modules can effectively tackle the tiny-size, low contrast and dark intensity issues, providing a solution for detecting dim and small-size infrared targets.