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.