Abstract: Surface defects on strip steel have characteristics such as low contrast， large intra⁃class differences， and inter⁃class similarity， which bring significant challenges to the accuracy of model detection. To address these challenges， a salient object detection model based on a triple⁃attention mechanism was proposed to achieve more accurate detection of surface defects on strip steel. A feature refinement module， consisting of three branches， was constructed for the coarse defect feature maps extracted from the model backbone network. Each branch was designed to focus on the channel and spatial features of surface defects on the strip steel from different perspectives. Additionally， to refine the edge details of salient targets， a local attention feature filter module was built and embedded into the feature refine module as a plugin， which enhances the model’s sensitivity in defect areas. The experimental results indicated that the proposed model had significant advantages over existing methods in salient object detection and outperforms similar methods in evaluation indicators such as mean absolute error and weighted F-measure， thereby verifying the effectiveness and robustness of the proposed method.