Synthetic aperture radar （SAR） image targets typically exhibit subtle edge features， which can vary across different scales. Edge features provide crucial information about the shape and contour of target objects， improving the model’s localization capabilities. However， existing SAR object detection methods often underperform in learning edge features， limiting their ability to accurately perceive target edges. To address this， we propose a SAR target detection method based on edge feature guided learning （EFGL）. This approach builds upon the fully convolutional one-stage （FCOS） object detection framework and leverages edge features to guide the learning process in feature pyramid networks （FPN）. By integrating an edge operator module into FPN， the network’s capacity to learn multi-scale edge features is explicitly enhanced. Additionally， during multi-scale feature fusion， we introduce an edge feature-guided fusion module that incorporates a spatial attention mechanism to enable edge-guided fusion across adjacent feature levels. On the MSAR and SAR-Aircraft-1.0 datasets， the proposed method achieves detection accuracies of 68.68% and 67.44% under the AP’07 standard， showing improvements of 1.34% and 4.81% over the baseline network， respectively compared to other related algorithms， this method demonstrates superior target localization and overall performance in SAR target detection.