基于改进路径聚合和池化YOLOv4的目标检测 |
An object detection method based on improved YOLOv4 path aggregation and pooling |
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DOI: |
中文关键词: 深度学习;目标检测;路径聚合;金字塔池化;YOLOv4 |
英文关键词:deep learning; object detection; path aggregation; pyramid pooling; YOLOv4 |
基金项目:国家自然科学基金(61501251)、南京邮电大学宽带无线通信与传感网技术教育部重点实验室开放研究基金(JZNY202113)和南京邮电大学科研项目(NY220207)资助项目 |
作者 | 单位 | 杨真真 | 南京邮电大学 宽带无线通信与传感网技术教育部重点实验室,江苏 南京 210023 | 郑艺欣 | 南京邮电大学 宽带无线通信与传感网技术教育部重点实验室,江苏 南京 210023 | 邵静 | 南京邮电大学 宽带无线通信与传感网技术教育部重点实验室,江苏 南京 210023 | 杨永鹏 | 南京邮电大学 宽带无线通信与传感网技术教育部重点实验室,江苏 南京 210023;南京信息职业技术学院 网络与通信学院,江苏 南京 210023 |
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中文摘要: |
针对YOLOv4目标检测器存在信息利用率不足的问题,提出了一种新的基于改进的路径聚合和池化YOLOv4的目标检测方法YOLOv4 P。为了充分利用路径聚合可以有效防止信息丢失这个特点,对YOLOv4的路径聚合网络进行改进,利用主干特征提取网络的第二个残差块,新增一个检测层,加强融合浅层特征层。另外,使用K means聚类对数据集重新进行处理,获得合适的先验框尺寸。此外,图像经过主干特征提取网络后的感受野比理论感受野小,为了增大感受野,在主干特征提取网络的后端加入金字塔池化模块,利用4种不同尺度的金字塔池化引入不同尺度下的特征信息。最后,在PASCAL VOC2007和VOC2012进行仿真实验,实验结果表明,提出的YOLOv4 P有效提高了检测精度。 |
英文摘要: |
Aiming at the problem of insufficient information utilization in the YOLOv4 object detector, an improved object detection method based on the improved path aggregation and pooling of the YOLOv4 model, namely YOLOv4 P, is proposed. First, we improve the path aggregation network of YOLOv4 since the path aggregation network can effectively prevent information loss. The second residual block of the backbone feature extraction network is used to add a detection layer to strengthen the fusion of shallow feature layers. Second, the datasets are reprocessed using the K means clustering to obtain a suitable prior box size. Third, since the size of the receptive field of the image after passing through the backbone feature extraction network is smaller than that of the theoretical one, a pyramid pooling module is added to the back end of the backbone feature extraction network and four different scales of pyramid pooling are used to introduce features at different scales to improve the receptive field. The simulation experiments on PASCAL VOC2007 and VOC2012 show that the proposed YOLOv4 P can effectively improve the detection accuracy. |
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