The huge scale variation of objects in scene images limits the performance of deep neural networks based on single-scale image input. To tackle the scale variation issue, we propose an improved multiscale attention network based on enhancing asymmetric convolution and strip pooling (EASNet), which uses three different inference scales. First, complementary weight masks are employed from branches of coarse scale to fine scale, to enhance predictions across scales. Second, the strip pooling module is adopted as strip pooling attention module (SPAM) in the coarser scales to produce the weight masks. It will make maximum use of the coarser scale, improving both the accuracy of boundary segmentation and the integrity of body segmentation for large targets or homogeneous objects but distributed discretely. Besides, enhancing asymmetric convolution block is designed to obtain richer and more robust feature information before SPAM. The effectiveness of EASNet experiments is validated using benchmarks (PASCAL VOC 2012 Aug and Cityscapes). The experimental results demonstrate that our method outperforms or is comparable to state-of-the-art methods by the widely used criteria mean intersection over union.