Aiming at the inaccurate positioning problem of salient object in the previous weakly supervised salient object detection algorithms， a weakly supervised salient object detection algorithm based on bounding box annotation was proposed. In the proposed algorithm， the minimum bounding rectangle boxes， which are the bounding boxes of all objects in the image were adopted as supervision information. Firstly， the initial saliency map was generated based on the bounding box annotation and GrabCut algorithm. Then， a correction module for missing object was designed to obtain the optimized saliency map. Finally， by combining the advantages of the traditional methods and deep learning methods， the optimized saliency map was used as the pseudo ground-truth to learn a salient object detection model through neural network. Comparison of the proposed algorithm and six unsupervised and four weakly supervised saliency detection algorithms was carried on four public datasets. Experimental results show that the proposed algorithm significantly outperforms comparison algorithms in both Max F-measure value （Max-F） and Mean Absolute Error （MAE） on four datasets. Compared with SBB （Sales Bounding Boxes）， which is also a weakly supervised method based on boundary box annotation， the annotation method of the proposed algorithm is simpler. Experiments were conducted on four datasets， ECSSD， DUTS-TE， HKU-IS， DUT-OMRON， and the Max-F increased by 1.82%， 4.00%， 1.27% and 5.33% respectively， and the MAE decreased by 13.89%， 15.07%， 8.77% and 13.33%， respectively. It can be seen that the proposed algorithm is a weakly supervised salient object detection algorithm with good detection performance.

Single Long Short-Term Memory （LSTM） network cannot effectively extract key information and cannot accurately fit data distribution in trajectory prediction. In order to solve the problems， a short-term trajectory prediction model of aircraft based on attention mechanism and Generative Adversarial Network （GAN） was proposed. Firstly， different weights were assigned to the trajectory by introducing attention mechanism， so that the influence of important features in the trajectory was able to be improved. Secondly， the trajectory sequence features were extracted by using LSTM， and the convergence net was used to gather all aircraft features within the time step. Finally， the characteristic of GAN optimizing continuously in adversarial game was used to optimize the model in order to improve the model accuracy. Compared with Social Generative Adversarial Network （SGAN）， the proposed model has the Average Displacement Error （ADE）， Final Displacement Error （FDE） and Maximum Displacement Error （MDE） reduced by 20.0%， 20.4% and 18.3% respectively on the dataset during climb phase. Experimental results show that the proposed model can predict future trajectories more accurately.