A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images

Ship detection is a challenging task for synthetic aperture radar (SAR) images. Ships have arbitrary directionality and multiple scales in SAR images. Furthermore, there is a lot of clutter near the ships. Traditional detection algorithms are not robust to these situations and easily cause redundancy in the detection area. With the continuous improvement in resolution, the traditional algorithms cannot achieve high-precision ship detection in SAR images. An increasing number of deep learning algorithms have been applied to SAR ship detection. In this study, a new ship detection network, known as the instance segmentation assisted ship detection network (ISASDNet), is presented. ISASDNet is a two-stage detection network with two branches. A branch is called an object branch and can extract object-level information to obtain positioning bounding boxes and classification results. Another branch called the pixel branch can be utilized for instance segmentation. In the pixel branch, the designed global relational inference layer maps the features to interaction space to learn the relationship between ship and background. The global reasoning module (GRM) based on global relational inference layers can better extract the instance segmentation results of ships. A mask assisted ship detection module (MASDM) is behind the two branches. The MASDM can improve detection results by interacting with the outputs of the two branches. In addition, a strategy is designed to extract the mask of SAR ships, which enables ISASDNet to perform object detection training and instance segmentation training at the same time. Experiments carried out two different datasets demonstrated the superiority of ISASDNet over other networks

Passive Deicing CFRP Surfaces Enabled by Super-Hydrophobic Multi-Scale Micro-Nano Structures Fabricated via Femtosecond Laser Direct Writing

Carbon fiber reinforced plastic (CFRP) is the main material of aircraft skin. Preparing superhydrophobic anti-icing/deicing surface on the CFRP is of great importance for aircraft flight safety. In this work, a variety of multi-scale micro-nano structures were imprinted on CFRP by femtosecond laser processing, and a transition from hydrophilic to superhydrophobic CFRP was realized. After being optimized by different geometries and laser conditions, the water contact angle, which is tested at 24.3 °C and 34% humidity, increased from 88 ± 2° (pristine) to 149 ± 3° (100 μm groove) and 153 ± 3° (80 μm grid). A further anti-icing test at −10 °C (measured on the cooling platform) and 28% humidity showed that the freezing time was increased from 78 ± 10 s (pristine) to 282 ± 25 s (80 μm grid). Most importantly, the tensile tests showed that the femtosecond laser processing method did not deteriorate the mechanical properties of CFRP. This work provides great significance for aircraft passive deicing technology