基于深度学习的电器目标检测
陈从平1,2,李游1,徐道猛1,邓扬1,何枝蔚1
(1.三峡大学 机械与动力学院,湖北 宜昌 443002;2.常州大学 机械工程学院,江苏 常州 213164)
摘要:针对传统目标检测算法无法自适应提取目标相应特征并完成识别的现象,提出一种基于快速区域卷积神经网络(Faster R-CNN)模型的电器识别方法,其优势在于可以自适应获取不同场景下目标的特征,避免由于人为设计目标的特征而带来的主观因素影响,具有良好的鲁棒性与准确性。Faster R-CNN中首先通过建立区域建议网络RPN(Region Proposal Network)代替Fast R-CNN中的Selective Search方法,得到建议位置后再进行检测。为了解决训练过程当中正负样本失衡问题,在Faster R-CNN中引入了难负样本挖掘策略,增强了模型的判别能力,提高检测的精度。
关键词:目标检测;识别;深度学习;神经网络
中图分类号:TP183;V448.25+1 文献标志码:A doi:10.3969/j.issn.1006-0316.2020.01.001
文章编号:1006-0316 (2020) 01-0001-08
Electrical Target Detection Based on Deep learning
CHEN Congping1,2,LI You1,XU Daomeng1,DENG Yang1,HE Zhiwei1
( 1.College of Mechanical & Power Engineering, China Three Gorges University, Yichang 443002, China; 2.School of Mechanical Engineering, Changzhou University, Changzhou 213164, China )
Abstract:Aiming at solving the problem that the traditional target detection algorithm cannot adaptively extract the corresponding features of the target, this paper proposes an electrical detection method based on Faster Region Convolutional Neural Network (Faster R-CNN) model. The advantage of the target detection and recognition method based on deep learning is that it can adaptively acquire the features of the target in different scenarios, avoiding the influence of subjective factors due to the characteristics of the artificial design target. This method has good robustness and accuracy. The method used in this paper establishes the Regional Proposal Network (RPN) to replace the selective search method in the original Fast R-CNN, which can obtains the recommended location before detecting it. In order to solve the problem of positive and negative sample imbalance during training, this paper introduces a hard example mining strategy in Faster R-CNN, which enhances the discriminative ability of the model and improves the accuracy of detection.
Key words:target detection;identify;deep learning;the neural network
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收稿日期:2019-08-26
基金项目:国家重点研发计划课题(2018YFC1903101,废线路板器件智能拆解和分选技术研究与示范);国家自然科学基金项目(51475266,流体微挤出/堆积制备组织工程支架过程形态调控机理研究)
作者简介:陈从平(1976-),男,湖北荆州人,博士,教授,主要研究方向为机器视觉、深度学习、3D打印及机电系统控制。
 

 

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