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首页> 外文会议>2018 international conference on image and video Processing, and artificial intelligence >A multi-scale feature fusion target detection algorithm
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A multi-scale feature fusion target detection algorithm

机译：一种多尺度特征融合目标检测算法

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摘要

For existing Faster R-CNN and single shot multibox detector (SSD) target detection algorithms, they all have the problem of low object detection accuracy under small target conditions. This paper proposes a general and effective target detection algorithm and the detection accuracy has greatly improved for smaller targets. The algorithm is divided into two parts. In the first part, in the feature extraction process, the feature map extracted by the basic feature extraction network is deconvoluted and merged with the previous layer feature map to generate Multi-scale feature maps with rich semantics and high resolution. Using proposed multi-scale feature maps to generate proposals. The second part uses the generated proposals to be sent to the Faster R-CNN network for classification and detection. Experiments show that using this algorithm for target detection can not only improve the recall of proposals, but also improve the accuracy of target detection, especially for small targets. The algorithm provides a new idea for small target detection.
机译：对于现有的Faster R-CNN和单发多盒检测器（SSD）目标检测算法，它们都具有在小目标条件下目标检测精度低的问题。提出了一种通用有效的目标检测算法，对较小目标的检测精度大大提高。该算法分为两部分。第一部分，在特征提取过程中，对基本特征提取网络提取的特征图进行反卷积，并与上一层特征图合并，生成语义丰富，分辨率高的多尺度特征图。使用建议的多尺度特征图生成建议。第二部分使用生成的建议将其发送到Faster R-CNN网络进行分类和检测。实验表明，使用该算法进行目标检测不仅可以提高建议的查全率，而且可以提高目标检测的准确性，特别是对于小目标。该算法为小目标检测提供了新思路。

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