In order to solve the problem of poor small target detection effect in Synthetic Aperture Radar (SAR) target ship detection, a self-adaptive anchor single-stage ship detection method was proposed. Firstly, on the basis of Feature Selective Anchor-Free (FSAF) algorithm, the optimal feature fusion method was obtained by using the Neural Architecture Search (NAS) to make full use of the image feature information. Secondly, a new loss function was proposed to solve the imbalance of positive and negative samples while enabling the network to regress the position more accurately. Finally, the final detection results were obtained by combining the Soft-NMS filtering detection box which is more suitable for ship detection. Several groups of comparison experiments were conducted on the open SAR ship detection dataset. Experimental results show that, compared with the original target detection algorithm, the proposed method significantly reduces the missed detections and false positives of small targets, and improves the detection performance for inshore ships to a certain extent.

Recently, the research on clothing simulation is becoming hotter. But the flexibility, sense of reality, real-time and integrity are always difficult to be unified. Therefore, a new dressing simulation system was designed concerning the automatic fitting of any human body and clothes. At first, the surface of Non-Uniform Rational B-Spline (NURBS) was used to complete deformable body modeling. Then, particles were reconstructed from the 3DMAX model and multi-type springs were created to complete arbitrary cloth modeling. Finally, Verlet integrator was adopted to complete dressing simulation, while a new simplification algorithm for cloth models and a new method for judging interior point with a triangle were implemented. The results show that the proposed modeling approach for body and clothes guarantees the diversity of dressing effect, and the model simplification and interior point judgment can increase the simulation performance by 30% or so, which ensures the real-time quality.