Improved YOLOv2 Object Detection Model

Rui Li, Jun Yang
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引用次数: 11

Abstract

Aiming at the problem of the large number of model parameters and poor performance on the small-size object of the YOLOv2 object detection model, an improved YOLOv2 object detection model is proposed. Firstly, it improves the YOLOv2 by introducing depth-wise separable convolution replace the standard convolution used in the YOLOv2. The number of parameters on the convolution layer is reduced by 78.83%. Secondly, the Feature Pyramid Network is introduced into the detection model to replace the YOLOv2’s image feature fusion method and perform object detection tasks on multi-scale image features. As a result, the ability of the improved YOLOv2 detection model to detect the small-size object is enhanced. Experimental results on PASCAL VOC 2007 datasets show that the improved YOLOv2 has a competitive accuracy to YOLOv2 and better performance on the small-size object.
改进的YOLOv2目标检测模型
针对YOLOv2目标检测模型模型参数多、对小尺寸目标检测性能差的问题,提出了一种改进的YOLOv2目标检测模型。首先,通过引入深度可分离卷积来取代YOLOv2中使用的标准卷积来改进YOLOv2。卷积层上的参数数量减少了78.83%。其次,将特征金字塔网络引入检测模型,取代YOLOv2的图像特征融合方法,在多尺度图像特征上执行目标检测任务。从而增强了改进的YOLOv2检测模型对小尺寸目标的检测能力。在PASCAL VOC 2007数据集上的实验结果表明,改进的YOLOv2具有与YOLOv2相当的精度,并且在小尺寸目标上具有更好的性能。
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