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MSCD-YOLO:一个轻量级的密集行人检测模型,具有细粒度的特征信息交互
Qiang Liu1, Zhongmin Li1, Lei Zhang1
1School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
MSCD-YOLO是一款用于密集行人检测的新型轻型模型,使用更少的参数提高了准确性和稳定性. 这种方法可以增强实时监控和城市交通系统.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 密集的行人检测对于监控和交通管理等应用至关重要.
- 现有的方法在准确性,错误率,大模型尺寸和稳定性方面存在困难.
研究的目的:
- 提出MSCD-YOLO,一种轻量级密集的行人检测模型.
- 为了提高检测准确性,性能和稳定性,同时最大限度地减少参数.
主要方法:
- 利用MobileViT作为一个轻量级的骨干,以实现高效的本地和全球特征提取.
- 设计了SCNeck子网络,用于信息保存功能融合.
- 采用DEHead检测头用于多尺度特征融合.
主要成果:
- 与YOLOv8n.n.相比,MSCD-YOLO在Crowdhuman和Widerperson数据集上显示了改善的mAP@0.5和mAP@0.5:0.95数据集.
- 在降低模型复杂度的情况下,在检测精度和效率方面取得了显著的收益.
结论:
- 对于密集的行人检测挑战,MSCD-YOLO提供了卓越的解决方案.
- 该模型提供了高精度和高效率与降低参数数量.
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