Gated bidirectional pyramid context network for infrared maritime target detection

Abstract

Detecting infrared small targets in complex marine environments is an important technology in maritime distress target search and tracking systems. To enrich the feature representation of maritime targets and suppress background noise, a gated bidirectional pyramid context network (GBPC-Net) is proposed. Firstly, a hierarchical feature extraction backbone is constructed to generate multi-scale feature, and then a gated bidirectional connection module (GBCM) is designed to aggregate hierarchical features of infrared maritime targets and eliminate complex background interference. Among them, the channel-based GBCM adopts the directions of top-to-down and bottom-to-up to aggregate multi-scale features from different layers into semantic-assisted detail features and detail-assisted semantic features. While the spatial-based GBCM further hierarchically modulates channel aggregation features in different directions to generate multi-scale gated aggregation features. Next, an adaptive pyramid context module (APCM) is introduced to learn the similarity between the local detail and the context information of different scales, which can emphasize the difference between small maritime targets and complex backgrounds. Subsequently, the features from APCM are used to guide the fusion of detailed features in lower-layer networks, and the aggregated feature map is applied to infrared maritime target detection. Finally, a target detection dataset derived from the real marine environment is constructed, and a series of comparative experiments are conducted on this dataset and the results show that our method can more accurately detect infrared maritime targets than some state-of-the-art methods.