改进了YOLOv3集成SENet,并优化了GIoU损失,用于封闭的行人检测
Qiangbo Zhang1, Yunxiang Liu1, Yu Zhang1
1School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
这项研究介绍了YOLOv3-Occlusion (YOLOv3-Occ),这是一个改进的物体检测模型,用于封闭的行人. 它增强了特征提取和界限框准确度,从而在拥挤的场景中提高了检测性能.
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:
- 隐蔽的行人检测对于智能系统至关重要,但面临着诸如假阳性和负性等挑战.
- 拥挤的场景与遮显著降低了现有的行人检测方法的准确性.
研究的目的:
- 为了提高封闭行人检测的准确性.
- 为了解决在拥挤环境中特征提取和定位的局限性.
主要方法:
- 开发了一种改进的你只看一次版本3 (YOLOv3) 模型,命名为YOLOv3-Occlusion (YOLOv3-Occ).
- 挤压和激发网络 (SENet) 被集成到YOLOv3中,以加强对被封闭的行人进行特征提取.
- 为了提高界限框预测准确度,引入了一个优化的通用交叉点对联 (GIoU) 损失,特别是GIoUIoG.
主要成果:
- YOLOv3-Occ模型在基准数据集上表现得更好.
- 在CityPersons数据集上实现了1.2%的MR-2增长.
- 在COCO2014数据集上获得了0.7%的mAP@50改进.
结论:
- 拟议的YOLOv3-Occ模型有效地增强了封闭的行人检测.
- 集成SENet和GIoUIoG损失有助于更准确的特征提取和本地化.
- 该方法在具有挑战性的,封闭的场景中检测行人方面取得了显著的改进.
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