Multi-scale feature fusion attention network for infrared small target detection

Abstract

Compared with other target detection tasks, infrared small target detection has the problem of feature information loss in deep networks due to fewer target pixels and the lack of color and texture features. To address aforementioned issue, a Multi-Scale Feature Fusion Attention Network (MSFFA) is proposed to better utilize shallow edge features and deep semantic features. Its main components contain Convolutional Block Attention Module (CBAM), Multi-Scale Receptive Field Feature Fusion Module (R3FM), and Bidirectional Feature Aggregation Network (BFANet). CBAM is designed to calculate the importance of each feature map and enhance useful features from the channel and spatial dimensions. R3FM is proposed to characterize the global context information of deep layers feature map to enlarge the network's receptive field for small targets detection with a larger range of location information. BFANet is developed to shorten the path of information exchange between different layers and reinforce the utilization of shallow features in the network. Moreover, the K-means clustering algorithm is adopted to optimize the width to height ratio of the bounding anchor, and it can better match the positive samples to improve the training performance. Extensive experiments on public infrared small target detection dataset demonstrate that the proposed method achieves better performance compared to the other state-of-the-art methods.