Lightweight Multimechanism Deep Feature Enhancement Network for Infrared Small-Target Detection

Abstract

Specific to the problem of infrared small-target detection in complex backgrounds, a multimechanism deep feature enhancement network model (MDFENet) was proposed. A lightweight multimechanism attention collaborative fusion module was proposed to efficiently fuse low-level features and high-level features to solve the problem that small infrared targets are easy to annihilate in the deep layer of the network. Based on the analysis of the background and target data, a normalized loss function was proposed, which integrates the segmentation threshold selection into the network and normalizes the probability of the network output to simulate a step function and reflect relative differences. Aiming at the sparseness of infrared target features, we used the subpixel convolution method to upsample the features to obtain high-resolution feature images while expanding the size of the feature map. We conducted detailed comparison and ablation experiments, comparing MDFENet with ALCNet, APGCNet, and other state-of-the-art networks to verify the effectiveness and efficiency of the network. Results show that the MDFENet algorithm achieves the optimal result in the balance of detection efficiency and lightweightedness on two datasets.