Abstract: The flame image of the sintering section is usually interfered by smoke and halo,which would cause the blurring of flame edge and material layer in the image.In order to solve the problem that tradi-tional saliency detection method based on two-dimensional image features is difficult to effectively obtain the actual saliency of the cross-sectional flame image,a saliency detection method based on boundary con-nectivity and multi-kernel Boosting is proposed.Firstly,the color de-correlation is used in the process of image color space conversion.Boundary connectivity and dark channel prior principle are used to obtain the initial saliency map.Secondly,the super-pixel region information,regional variance and regional contrast of the flame image are used to construct region descriptor,the multiple kernel Boosting algorithm based on support vector regression is used to generate the complementary saliency map on 4 scales.Finally,the initial saliency map and the complementary saliency map are fused to obtain the final saliency map.600 flame images including manual labeling are used to compare the proposed method with other 5 existing methods and each stage of the proposed method is analyzed.P-R curve,F-measure,mean absolute error and running time are taken as evaluation indexes.The experimental results show that the proposed method is superior to the other 5 methods,and the detection performance of each stage is gradually enhanced,which lays a foun-dation for improving the effective information extraction of sintering flame section image.