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An Effective Algorithm for Object Detection Based on Deep Learning

机译:一种基于深度学习的有效目标检测算法

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摘要

Intersection over Union (IoU) is an important function in object detection based on deep learning. But, there is a gap between the used distance losses and this metric value of maximizing. This paper improves the IoU function based on IoU. Redesigned the neural network structure, and used the PASCAL VOC2012 dataset. The structure model of the neural network is compare with others in the accuracy of object detection. Experimental results show that our method can achieve good results in object detection. The approach provides a new idea for object detection.
机译:在基于深度学习的目标检测中,联合交集(IoU)是一个重要的功能。但是,使用的距离损失与最大化的度量值之间存在差距。本文在IoU的基础上对IoU函数进行了改进。重新设计了神经网络结构,并使用PASCAL VOC2012数据集。在目标检测精度方面,将神经网络的结构模型与其他模型进行了比较。实验结果表明,该方法在目标检测中取得了良好的效果。该方法为目标检测提供了一种新的思路。

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