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.

Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.

Robust and efficient detection of small infrared target is a critical and challenging task in infrared search and tracking applications. The size of the small infrared targets is relatively tiny compared to the ordinary targets, and the sizes and appearances of the these targets in different scenarios are quite different. Besides, these targets are easily submerged in various background noise. To tackle the aforementioned challenges, a novel asymmetric pyramid aggregation network (APANet) is proposed. Specifically, a pyramid structure integrating dual attention and dense connection is firstly constructed, which can not only generate attention-refined multi-scale features in different layers, but also preserve the primitive features of infrared small targets among multi-scale features. Then, the adjacent cross-scale features in these multi-scale information are sequentially modulated through pair-wise asymmetric combination. This mutual dynamic modulation can continuously exchange heterogeneous cross-scale information along the layer-wise aggregation path until an inverted pyramid is generated. In this way, the semantic features of lower-level network are enriched by incorporating local focus from higher-level network while the detail features of high-level network are refined by embedding point-wise focus from lower-level network, which can highlight small target features and suppress background interference. Subsequently, recursive asymmetric fusion is designed to further dynamically modulate and aggregate high resolution features of different layers in the inverted pyramid, which can also enhance the local high response of small target. Finally, a series of comparative experiments are conducted on two public datasets, and the experimental results show that the APANet can more accurately detect small targets compared to some state-of-the-art methods.