Selecting the most effective lightweight YOLO model for object detection remains a complex task, especially when object scale and scene density vary significantly. This study systematically compares seven nano/tiny YOLO architectures (v5n, v6n, v8n, v9t, v10n, 11n, 12n) trained and evaluated under identical conditions using a unified Ultralytics pipeline. Three custom datasets were designed to represent distinct visual challenges: small objects (bees), medium-scale objects (sheep), and dense, occluded human heads. Both quantitative and qualitative analyses revealed that model superiority shifts with object scale and crowding, and that conventional accuracy metrics such as mAP may fail to reflect real-world detection reliability. While most prior studies focus on a single dataset or rely solely on numerical accuracy, our work introduces a multiscale, multi-density benchmark that bridges statistical evaluation with visual error analysis. This approach exposes hidden performance tradeoffs between detection precision and contextual robustness. The findings underscore the importance of dataset-specific validation and highlight that mAP alone is insufficient for comprehensive model assessment in lightweight YOLO deployment.