Road surface defects impact traffic safety, road durability, and driving comfort. To address the low detection accuracy of complex features such as cracks and potholes in existing methods, this paper proposes YOLOv8n-Edge—a road defect detection algorithm based on YOLOv8n with enhanced edge features. RFAConv is integrated into the backbone to enlarge the receptive field while avoiding kernel parameter sharing. An Edge Enhance Conv module is introduced to fuse high-frequency details with the input, reinforcing feature representation. Additionally, the Manet-Star structure, combining Manet and Starnet, replaces parts of the C2f module to boost feature extraction. A shallow-layer auxiliary branch, Sub-GEIM, generates multi-scale edge features that are fused with corresponding detection heads to improve localization accuracy. Experimental results show that YOLOv8n-Edge achieves a mAP@50 of 72.1% on the preprocessed RDD2022 dataset—an improvement of 3.3% over the baseline—while only slightly increasing model complexity. Its effectiveness is further validated through generalization and comparative experiments.