DMEF-Net: Lightweight Infrared Dim Small Target Detection Network for Limited Samples

Intractable sparse feature extraction, overweight model size, and limited training samples are currently bewildering in infrared dim small target detection, which is not adequately addressed by current state-of-the-art (SOTA) methods. Here, to synchronously address these issues, a dense multilevel feature extraction and fusion network (DMEF-net) is designed, mainly consisting of two modules: target context and Gaussian saliency feature extraction (TCGS) module and multilevel dense feature fusion structure (MLDF). Inspired by the physical thermal diffusion model and the human visual mechanism for small-scale targets, Gaussian salience features and local context features are introduced into the target feature expression through the designed TCGS module to solve the sparse feature extraction problem. Then, to solve the overweight model size problem, a novel MLDF module is designed to incorporate the feature reuse mechanism into our model, thereby significantly reducing the number of trainable parameters. Finally, in the training procedure, an efficient saliency labeling strategy is proposed to jointly supervise the model training in both Euclidean and Gaussian spaces, ultimately enhancing the model's ability to explore the target features and alleviate performance deterioration when the training samples are limited. Extensive experiments on several large-scale open datasets, including TDSATUA and MTDUCB, prove that the proposed DMEF-net outperforms other SOTA methods by 8% in accuracy and has 4800% less model size.