To address the existing challenges of discontinuity in road region extraction and difficulty in extracting roads of different sizes, especially the misclassification of narrow roads, a novel road extraction algorithm combining element-wise multiplication and detail optimization was proposed. Firstly, an element-wise multiplication module (IEM module) was introduced in the encoder part to perform feature extraction, preserving and extracting multi-scale and multi-level road features. A Conv3×3 with a stride of 2 was used for twofold downsampling, reducing information loss during the extraction process of remote sensing images. The encoder-decoder was structured with five layers and utilized skip connections to maintain multi-scale extraction capabilities while improving road continuity. Secondly, PFAAM was employed to enhance the network's focus on road features. Finally, a fine residual network (RRN) was utilized to enhance the network's ability to extract boundary details, refining the boundary information. Experiments were conducted on the public road dataset of Massachusetts (CHN6-CUG) to test the network model, achieving evaluation metrics of OA (accuracy), IoU (intersection over union), mIoU (mean IoU), F1-score of 98.06% (97.19%)、64.52% (60.24%)、81.25% (78.66%), and 88.70% (86.85%). The experimental results demonstrated that the proposed method outperformed all the compared methods, effectively improving the accuracy of road segmentation.