基于改进Faster R-CNN的瓶装饮料商品目标检测方法 |
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引用本文: | 陈欢欢,汪建晓,王高杰,陈 勇.基于改进Faster R-CNN的瓶装饮料商品目标检测方法[J].集成技术,2021,10(3):1-11. |
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作者姓名: | 陈欢欢 汪建晓 王高杰 陈 勇 |
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作者单位: | 佛山科学技术学院 佛山 528000;广东顺德创新设计研究院 佛山 528000;佛山科学技术学院 佛山 528000;广东顺德创新设计研究院 佛山 528000 |
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基金项目: | 广东省科技计划项目(2017A010102018) |
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摘 要: | 该文以无人售货机售卖瓶装饮料商品为研究场景,提出一种基于改进Faster R-CNN算法的瓶装饮料商品目标检测方法.首先,采用残差网络ResNet-50进行特征提取,加深网络对目标特征的提取和学习的深度;然后,根据瓶装饮料商品形态学特征,增加区域建议网络(Regional Proposal Network)的锚框数量和...
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关 键 词: | Faster R-CNN 目标检测 残差网络 区域提议网络 多维特征融合 |
Target Detection Method of Bottled Drinks Based on Improved Faster R-CNN |
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Authors: | CHEN Huanhuan WANG Jianxiao WANG Gaojie CHEN Yong |
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Abstract: | This paper presents an improved faster R-CNN algorithm based on the application of unmanned vending machine selling bottled drinks. Firstly, the residual network ResNet-50 is used as the feature extraction network to deepen the depth of target feature extraction and learning. Then, the number and style of anchor frame in regional proposal network (RPN) is improved according to the morphological characteristics of bottled beverage products. Finally, a multi-dimensional feature map fusion network is proposed to enhance the detection performance of small targets. The experimental results showed that, the loss value tends to converge after 10 000 iterations of model training. Average precision values of 10 categories of bottled beverage products are all larger than 90%. And the comprehensive detection recognition rate mean average precision value is 93.26%, which is improved 20% compared with the original model. |
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Keywords: | faster R-CNN target detection residual network regional proposal network multi-dimensional feature fusion |
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