对比三模型在不同分辨率下的表现，选型更省心。
The Impact of Image Resolution on Face Detection: A Comparative Analysis of MTCNN, YOLOv XI and YOLOv XII models
- 测试三种模型在160×160至640×640分辨率下的表现
- YOLOv11在高分辨率下准确率最高，YOLOv12召回略优
- MTCNN定位准但推理慢，适合非实时场景
人脸检测是安防、生物识别和人机交互等AI应用的核心。然而真实场景中低分辨率图像会显著降低检测性能。本研究系统评估了YOLOv11、YOLOv12和MTCNN三种主流深度学习人脸检测器在不同输入分辨率（160×160、320×320、640×640）下的准确率与鲁棒性。基于WIDER FACE数据集，使用精度、召回率、mAP50、mAP50-95及推理时间等指标进行评测。结果表明，YOLOv11在高分辨率下检测准确率最优，YOLOv12略具召回优势；而MTCNN虽在关键点定位上表现良好，但实时推理速度较慢。研究为不同部署约束下的分辨率感知模型选型提供可操作建议。
原文摘要 · Abstract (English)
Face detection is a crucial component in many AI-driven applications such as surveillance, biometric authentication, and human-computer interaction. However, real-world conditions like low-resolution imagery present significant challenges that degrade detection performance. In this study, we systematically investigate the impact of input resolution on the accuracy and robustness of three prominent deep learning-based face detectors: YOLOv11, YOLOv12, and MTCNN. Using the WIDER FACE dataset, we conduct extensive evaluations across multiple image resolutions (160x160, 320x320, and 640x640) and assess each model's performance using metrics such as precision, recall, mAP50, mAP50-95, and inference time. Results indicate that YOLOv11 outperforms YOLOv12 and MTCNN in terms of detection accuracy, especially at higher resolutions, while YOLOv12 exhibits slightly better recall. MTCNN, although competitive in landmark localization, lags in real-time inference speed. Our findings provide actionable insights for selecting resolution-aware face detection models suitable for varying operational constraints.
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