Deep learning-driven medical image analysis has become a critical auxiliary tool for clinical oral disease diagnosis. However, most existing caries detection methods based on YOLO series models are validated on single-lesion datasets, leading to generally high missed-detection rates and unsatisfactory localization accuracy in complex dental radiographic scenarios where multiple dental conditions and restorations coexist. To address this limitation, this paper proposes YOLO-ASPE, an improved YOLOv11n lightweight framework for tiny-carious-lesion detection. It integrates a P2 high-resolution detection branch, a multi-scale sequential feature fusion module, a lightweight SE channel attention mechanism, and EIoU bounding box regression loss. Experiments were conducted on a public eight-category dental radiographic dataset with caries as the sole detection target. The results show that YOLO-ASPE achieved a Caries AP@0.5 of 68.7%, 10.5 percentage points higher than the baseline model, with a real-time inference speed of 312 FPS. This work provides a lightweight technical reference for the computer-assisted screening of dental caries, although further external validation is still required before clinical application.