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Abstract Existing deep‐learning–based saliency detection methods mainly design a sophisticated architecture to integrate multi‐level convolutional features, for example, recurrent network or bi‐directional message passing model, and achieve gratifying performance. However, the direct transmission of information with large span, for example, the information from the deepest layer and the shallowest layer, may lead to antagonistic problems. To address this problem, we propose a local bi‐directional funnel network (LBDFN) to effectively integrate multi‐level features for salient object detection. In the proposed local bi‐directional funnel network, a local bi‐directional feature integration (LBDFI) module is designed to fuse the features with similar properties from adjacent convolution layers, which can solve the problem of mismatch between fusion features well. The extensive experiments on five saliency detection datasets clearly demonstrate that the proposed method outperforms the state‐of‐the‐art approaches.