基于可变形卷积改进SSD算法的目标检测方法
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南通大学机械工程学院 南通 226019

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TP391.4

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江苏省仿生功能材料重点实验室开放课题基金(BFM2101)


Target detection method based on deformable convolution improved SSD algorithm
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School of Mechanical Engineering, Nantong University, Nantong 226019, China

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    摘要:

    为了提高传统SSD算法对小目标检测的准确率,提出一种改进的SSD目标检测算法:采用基于可变形卷积的ResNet50作为SSD算法的特征提取网络,提高对目标的处理能力;设计特征金字塔(FPN)来融合不同层的特征图,丰富浅层特征图的语义信息;在特征融合时引入通道注意机制,提取相应的通道权重,增加重要信息的比例,提高检测效果。最后采用PASCAL-VOC2007开源数据集进行仿真实验,并与传统SSD目标检测算法进行对比,准确率得到显著提高,验证了该算法对小目标检测的有效性。

    Abstract:

    In order to improve the accuracy of the traditional SSD algorithm for small target detection, an improved SSD target detection algorithm is proposed: ResNet50 based on deformable convolution is used as the feature extraction network of the SSD algorithm to improve the processing ability of the target; the feature pyramid (FPN) to fuse feature maps of different layers and enrich the semantic information of shallow feature maps; introduce channel attention mechanism during feature fusion, extract corresponding channel weights, increase the proportion of important information, and improve the detection effect. Finally, the PASCAL-VOC2007 open source data set was used for simulation experiments, and compared with the traditional SSD target detection algorithm, the accuracy is significantly improved, which verifies the effectiveness of the algorithm for small target detection.

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蒋 晨,钱永明,姚兴田,李 壮.基于可变形卷积改进SSD算法的目标检测方法[J].电子测量技术,2022,45(16):116-122

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  • 在线发布日期: 2024-04-07
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