Moderately Dense Adaptive Feature Fusion Network for Infrared Small Target Detection

Detecting infrared small targets quickly and accurately in complex backgrounds has always been a challenging task. Data-driven methods have achieved good results because of their powerful feature extraction capabilities. Many algorithms use ResNet or VGG as their backbone, but because of the small size and inconspicuous features, pooling layers in their networks could lead to the loss of targets in deep layers. Even though dense network structure is proposed to alleviate this issue, its excessive dense connections makes it difficult to achieve real-time detection. To meet the requirements of both accurate performance and real-time detection, we propose moderately dense adaptive feature fusion network (MDAFNet). We design a moderately dense adaptive feature fusion (MDAF) module that contains only three feature layers as the backbone of the network. This module connects all the internal features with each other and uses a weighted sum of different layers as the output, promoting feature reuse and maintaining infrared small target features in the deep layers of the network. We also design a coarse-to-fine detection head (CFHead) and introduce auxiliary loss to enable the network to predict target contours with greater precision. Moreover, we propose a new data augmentation method that effectively enhances the generalization performance of network. Experimental results demonstrate that our network achieves excellent performance in detection accuracy and meets the requirements for real-time detection on RTX3080 GPU.