To address the challenges of detecting multi-scale road defects and the lack of lightweight designs in conventional detection models, we propose ACD-YOLOv8, an enhanced model based on YOLOv8s. Our model enhances baseline architecture by integrating three key components: a lightweight Cross-Scale Feature Fusion Module (CCFM), an ADown sampling operation, and a Dynamic Head (DyHead). Experimental results on the RDD2022 dataset demonstrate the superiority of our approach. Compared to the baseline YOLOv8s, ACD-YOLOv8 achieves a 0.9% increase in mAP@0.5 and a 1.6% increase in the more stringent mAP@0.5:0.95 metric. Simultaneously, the model’s parameter count is reduced by 3.72 million (a 33.3% reduction) and its size is reduced by 7.4 MB. This work provides a practical and scalable solution for deploying high-accuracy defect detection on resource-constrained mobile platforms, offering significant potential to enhance traffic safety and maintenance efficiency.