The morphologies of various surface defects on strip steel suffer from oil stain water drops steel textures and erratic illumination. It is still challenging to recognize defect boundary precisely from cluttered backgrounds. This article emphasizes such a fact that skip connections between encoder and decoder are not equally effective attempts to adaptively allocate the aggregation weights that represent differentiated information entropy values in channelwise by importing a stack of cross-attention transformer (CAT) into the encoder–decoder network (EDNet). Besides a cross-attention refinement module (CARM) is constructed closely after the decoder to further optimize the coarse saliency maps. This newly nominated CAT-EDNet can well address the semantic gap issue among the multiscale features for its multihead attention structure. The CAT-EDNet performs best on insuring defect integrity and maintaining defect boundary details when compared with 12 state-of-the-arts and the detection efficiency is at 28 fps even under the noise interfered scenario.