<p>A novel dual-branch decoding fusion convolutional neural network model (DDFNet) specifically designed for real-time salient object detection (SOD) on steel surfaces is proposed. DDFNet is based on a standard encoder–decoder architecture. DDFNet integrates three key innovations: first, we introduce a novel, lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details, enabling efficient salient feature extraction. Then, we propose an innovative dual-branch decoding fusion structure, comprising the refined defect representation branch and the enhanced defect representation branch, which enhance accuracy in defect region identification and feature representation. Additionally, to further improve the detection of small and complex defects, we incorporate a multi-scale attention fusion module. Experimental results on the public ESDIs-SOD dataset show that DDFNet, with only 3.69 million parameters, achieves detection performance comparable to current state-of-the-art models, demonstrating its potential for real-time industrial applications. Furthermore, our DDFNet-L variant consistently outperforms leading methods in detection performance. The code is available at <a href="https://github.com/13140W/DDFNet">https://github.com/13140W/DDFNet</a>.</p>