Aiming at the problem of poor precision performance of traditional Bayesian model saliency detection algorithm, a Bayesian model saliency detection algorithm based on improved convex hull was proposed. Firstly, the foreground of the image was extracted by the manifold ranking algorithm, which was used as the prior probability in Bayesian model. Secondly, Harris corner detection algorithm based on color enhancement was used to detect the feature points of the image in three color spaces of RGB, HSV and CIELab; the convex hulls in RGB, HSV and CIELab spaces were constructed respectively; and the intersection of convex hulls were obtained. Thirdly, the saliency region map was calculated by Bayesian model according to the prior probability and the observed likelihood probability obtained by combining convex hulls and color histograms. Finally, the proposed algorithm was tested in two public data sets MSRA and ECSSD. The experimental results show that the proposed algorithm can suppress the background noise effectively and detect the salient areas completely. The test results of F-measure value in MSRA and ECSSD databases are 0.87 and 0.71 respectively, and the accuracy-recall rate curve is higher than that of traditional classical algorithms in complex image databases. The proposed algorithm improves the detection effect of the traditional classical algorithm and the accuracy of saliency map detection.